Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline

AbstractOne of the major methods to identify microbial community composition, to unravel microbial population dynamics, and to explore microbial diversity in environmental samples is DNA- or RNA-based 16S rRNA (gene) amplicon sequencing. Subsequent bioinformatics analyses are required to extract valuable information from the high-throughput sequencing approach. However, manifold bioinformatics tools complicate their choice and might cause differences in data interpretation, making the selection of the pipeline a crucial step.Here, we compared the performance of most widely used 16S rRNA gene amplicon sequencing analysis tools (i.e. Mothur, QIIME1, QIIME2, and MEGAN) using mock datasets and environmental samples from contrasting terrestrial and freshwater sites. Our results showed that QIIME2 outcompeted all other investigated tools in sequence recovery (>10 times less false positives), taxonomic assignments (>22% better F-score) and diversity estimates (>5% better assessment), while there was still room for improvement e.g. imperfect sequence recovery (recall up to 87%) or detection of additional false sequences (precision up to 72%). Furthermore, we found that microbial diversity estimates and highest abundant taxa varied among analysis pipelines (i.e. only one in five genera was shared among all analysis tools) when analyzing environmental samples, which might skew biological conclusions.Our findings were subsequently implemented in a high-performance computing conformant workflow following the FAIR (Findable, Accessible, Interoperable, and Re-usable) principle, allowing reproducible 16S rRNA gene amplicon sequence analysis starting from raw sequence files. Our presented workflow can be utilized for future studies, thereby facilitating the analysis of high-throughput DNA- or RNA-based 16S rRNA (gene) sequencing data substantially.ImportanceMicroorganisms play an essential role in biogeochemical cycling events across the globe. Phylogenetic marker gene analysis is a widely used method to explore microbial community dynamics in space and time, to predict the ecological relevance of microbial populations, or to identify microbial key players in biogeochemical cycles. Several computational analysis methods were developed to aid 16S rRNA gene analysis but choosing the best method is not trivial. In this study, we compared popular analysis methods (i.e. Mothur, QIIME1 and 2, and MEGAN) using samples with known microbial composition (i.e. mock community samples) and environmental samples from contrasting habitats (i.e. groundwater, soil, sediment, and river water). Our findings provide guidance for choosing the currently optimal 16S rRNA gene sequencing analysis method and we implemented our recommended pipeline into a reproducible workflow, which follows highest bioinformatics standards and is open source and free to use.

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PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

10.1101/641464 ◽

2019 ◽

Cited By ~ 2

Author(s):

Peter Rubbens ◽

Ruben Props ◽

Frederiek-Maarten Kerckhof ◽

Nico Boon ◽

Willem Waegeman

Keyword(s):

Flow Cytometry ◽

Microbial Community ◽

16S Rrna ◽

16S Rrna Gene ◽

Microbial Diversity ◽

Amplicon Sequencing ◽

Gaussian Mixture ◽

Community Diversity ◽

Rrna Gene ◽

Flow Cytometry Data

AbstractMicrobial flow cytometry allows to rapidly characterize microbial communities. Recent research has demonstrated a moderate to strong connection between the cytometric diversity and taxonomic diversity based on 16S rRNA gene amplicon sequencing data. This creates the opportunity to integrate both types of data to study and predict the microbial community diversity in an automated and efficient way. However, microbial flow cytometry data results in a number of unique challenges that need to be addressed. The results of our work are threefold: i) We expand current microbial cytometry fingerprinting approaches by proposing and validating a model-based fingerprinting approach based upon Gaussian Mixture Models, which we called PhenoGMM. ii) We show that microbial diversity can be rapidly estimated by PhenoGMM. In combination with a supervised machine learning model, diversity estimations based on 16S rRNA gene amplicon sequencing data can be predicted. iii) We evaluate our method extensively by using multiple datasets from different ecosystems and compare its predictive power with a generic binning fingerprinting approach that is commonly used in microbial flow cytometry. These results demonstrate the strong connection between the genetic make-up of a microbial community and its phenotypic properties as measured by flow cytometry. Our workflow facilitates the study of microbial diversity and community dynamics using flow cytometry in a fast and quantitative way.ImportanceMicroorganisms are vital components in various ecoystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technique to characterize microbial community diversity and dynamics. It is an optical technique, able to rapidly characterize a number of phenotypic properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian Mixture Models. When samples have been analyzed by both flow cytometry and 16S rRNA gene amplicon sequencing, we show that supervised machine learning models can be used to find the relationship between the two types of data. We evaluate our workflow on datasets from different ecosystems, illustrating its general applicability for the analysisof microbial flow cytometry data. PhenoGMM facilitates the rapid characterization and predictive modelling of microbial diversity using flow cytometry.

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Distribution of Microbial Community Structure in Sediment from the Suyeong River and Bay of Busan, Republic of Korea, Determined by 16S rRNA Gene Amplicon Sequencing

Microbiology Resource Announcements ◽

10.1128/mra.00778-21 ◽

2021 ◽

Vol 10 (46) ◽

Author(s):

Ilwon Jeong ◽

Junho Lee ◽

Jong-Oh Kim ◽

Seokjin Yoon ◽

Kyunghoi Kim

Keyword(s):

Community Structure ◽

Microbial Community ◽

Sequence Analysis ◽

16S Rrna ◽

16S Rrna Gene ◽

Microbial Community Structure ◽

Amplicon Sequencing ◽

Republic Of Korea ◽

Rrna Gene ◽

Sediment Samples

Here, we report a 16S rRNA gene amplicon sequence analysis presenting the microbial community in sediments from the Suyeong River and Suyeong Bay, Republic of Korea. The dominant phyla in all sediment samples were Proteobacteria (39.69 to 53.62%) and Bacteroidetes (29.78 to 33.89%).

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Analysis of the Microbial Community Structure in Coastal Sediment of an Ascidian Farm in South Korea through 16S rRNA Gene Amplicon Sequencing

Microbiology Resource Announcements ◽

10.1128/mra.00584-21 ◽

2021 ◽

Vol 10 (30) ◽

Author(s):

Ilwon Jeong ◽

Jong-Oh Kim ◽

Seokjin Yoon ◽

Kyunghoi Kim

Keyword(s):

Community Structure ◽

Microbial Community ◽

16S Rrna ◽

16S Rrna Gene ◽

Microbial Community Structure ◽

Amplicon Sequencing ◽

Rrna Gene ◽

Content Type ◽

Styela Clava ◽

Structure Changes

Aquaculture places contamination pressure on the coastal environment. We investigated the microbial community structure changes in sediment in an ascidian Styela clava farm. Data profiling of the 16S rRNA gene amplicon sequence shows that the microbial diversity of sediment in the Styela clava farm is dominated by Proteobacteria phyla (relative abundance, 95.34 to 97.85%).

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Survey of Full Scale Side-Stream EBPR Facilities and Comparison with Conventional EBPR: Process Stability, Kinetics and Microbial Ecology

10.20944/preprints201808.0241.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Annalisa Onnis-Hayden ◽

Varun Srinivasan ◽

Nicholas B. Tooker ◽

Guangyu Li ◽

Dongqi Wang ◽

...

Keyword(s):

Microbial Community ◽

16S Rrna ◽

16S Rrna Gene ◽

16S Rrna Gene Sequencing ◽

Amplicon Sequencing ◽

Systematic Evaluation ◽

Rrna Gene ◽

Process Stability ◽

Side Stream ◽

Rrna Gene Sequencing

Side-stream EBPR process (S2EBPR) is a new alternative to address the common challenges in EBPR related to weak wastewater influent and to improve EBPR process stability. A systematic evaluation and comparison of the process performance and microbial community structure between four S2EBPR with conventional EBPR configurations in US was conducted. The statistical analysis suggested higher performance stability in S2EBPR than the conventional EBPRs, although possible bias is recognized due to variations in the target permit levels and plant-specific factors among the plants. Total and known PAOs and GAOs abundance and identities were investigated with FISH, DAPI, 16S rRNA gene sequencing and Raman microspectroscopy. The results suggested comparable relative PAO and Candidatus Accumulibacter abundances in S2EBPR and conventional EBPR systems. Tetrasphaera, a putative PAO, was also found at similar abundance in S2EBPR as in conventional facilities, whereas the relative abundance of known GAOs was lower in S2EBPR than those typically seen at conventional EBPRs. Microbial community analyses via 16S rRNA gene amplicon sequencing revealed differences in the community phylogenetic fingerprints between S2EBPR and conventional plants. Shannon and Inverse Simpson indices, which are combined measures of richness and evenness evaluation of the microbial communities, suggested that the microbial diversity in S2EBPR plants were higher than those in conventional EBPRs.

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An inter-laboratory study to investigate the impact of the bioinformatics component on microbiome analysis using mock communities

Scientific Reports ◽

10.1038/s41598-021-89881-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Denise M. O’Sullivan ◽

Ronan M. Doyle ◽

Sasithon Temisak ◽

Nicholas Redshaw ◽

Alexandra S. Whale ◽

...

Keyword(s):

Microbial Community ◽

16S Rrna ◽

16S Rrna Gene ◽

Relative Abundance ◽

Amplicon Sequencing ◽

16S Rrna Genes ◽

Rrna Genes ◽

Rrna Gene ◽

Microbiome Analysis ◽

The Impact

AbstractDespite the advent of whole genome metagenomics, targeted approaches (such as 16S rRNA gene amplicon sequencing) continue to be valuable for determining the microbial composition of samples. Amplicon microbiome sequencing can be performed on clinical samples from a normally sterile site to determine the aetiology of an infection (usually single pathogen identification) or samples from more complex niches such as human mucosa or environmental samples where multiple microorganisms need to be identified. The methodologies are frequently applied to determine both presence of micro-organisms and their quantity or relative abundance. There are a number of technical steps required to perform microbial community profiling, many of which may have appreciable precision and bias that impacts final results. In order for these methods to be applied with the greatest accuracy, comparative studies across different laboratories are warranted. In this study we explored the impact of the bioinformatic approaches taken in different laboratories on microbiome assessment using 16S rRNA gene amplicon sequencing results. Data were generated from two mock microbial community samples which were amplified using primer sets spanning five different variable regions of 16S rRNA genes. The PCR-sequencing analysis included three technical repeats of the process to determine the repeatability of their methods. Thirteen laboratories participated in the study, and each analysed the same FASTQ files using their choice of pipeline. This study captured the methods used and the resulting sequence annotation and relative abundance output from bioinformatic analyses. Results were compared to digital PCR assessment of the absolute abundance of each target representing each organism in the mock microbial community samples and also to analyses of shotgun metagenome sequence data. This ring trial demonstrates that the choice of bioinformatic analysis pipeline alone can result in different estimations of the composition of the microbiome when using 16S rRNA gene amplicon sequencing data. The study observed differences in terms of both presence and abundance of organisms and provides a resource for ensuring reproducible pipeline development and application. The observed differences were especially prevalent when using custom databases and applying high stringency operational taxonomic unit (OTU) cut-off limits. In order to apply sequencing approaches with greater accuracy, the impact of different analytical steps needs to be clearly delineated and solutions devised to harmonise microbiome analysis results.

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Much ado about nothing? Off-target amplification can lead to false-positive bacterial brain microbiome detection in healthy and Parkinson’s disease individuals

Microbiome ◽

10.1186/s40168-021-01012-1 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Janis R. Bedarf ◽

Naiara Beraza ◽

Hassan Khazneh ◽

Ezgi Özkurt ◽

David Baker ◽

...

Keyword(s):

Parkinson’S Disease ◽

16S Rrna ◽

16S Rrna Gene ◽

False Positive ◽

Gene Sequencing ◽

Bacterial Biomass ◽

16S Rrna Gene Sequencing ◽

Amplicon Sequencing ◽

Rrna Gene ◽

Rrna Gene Sequencing

Abstract Background Recent studies suggested the existence of (poly-)microbial infections in human brains. These have been described either as putative pathogens linked to the neuro-inflammatory changes seen in Parkinson’s disease (PD) and Alzheimer’s disease (AD) or as a “brain microbiome” in the context of healthy patients’ brain samples. Methods Using 16S rRNA gene sequencing, we tested the hypothesis that there is a bacterial brain microbiome. We evaluated brain samples from healthy human subjects and individuals suffering from PD (olfactory bulb and pre-frontal cortex), as well as murine brains. In line with state-of-the-art recommendations, we included several negative and positive controls in our analysis and estimated total bacterial biomass by 16S rRNA gene qPCR. Results Amplicon sequencing did detect bacterial signals in both human and murine samples, but estimated bacterial biomass was extremely low in all samples. Stringent reanalyses implied bacterial signals being explained by a combination of exogenous DNA contamination (54.8%) and false positive amplification of host DNA (34.2%, off-target amplicons). Several seemingly brain-enriched microbes in our dataset turned out to be false-positive signals upon closer examination. We identified off-target amplification as a major confounding factor in low-bacterial/high-host-DNA scenarios. These amplified human or mouse DNA sequences were clustered and falsely assigned to bacterial taxa in the majority of tested amplicon sequencing pipelines. Off-target amplicons seemed to be related to the tissue’s sterility and could also be found in independent brain 16S rRNA gene sequences. Conclusions Taxonomic signals obtained from (extremely) low biomass samples by 16S rRNA gene sequencing must be scrutinized closely to exclude the possibility of off-target amplifications, amplicons that can only appear enriched in biological samples, but are sometimes assigned to bacterial taxa. Sequences must be explicitly matched against any possible background genomes present in large quantities (i.e., the host genome). Using close scrutiny in our approach, we find no evidence supporting the hypothetical presence of either a brain microbiome or a bacterial infection in PD brains.

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The diversity of active microbial groups in an activated sludge process treating painting process wastewater

E3S Web of Conferences ◽

10.1051/e3sconf/202014801002 ◽

2020 ◽

Vol 148 ◽

pp. 01002

Author(s):

Herto Dwi Ariesyady ◽

Mentari Rizki Mayanda ◽

Tsukasa Ito

Keyword(s):

Wastewater Treatment ◽

Microbial Community ◽

16S Rrna ◽

16S Rrna Gene ◽

Activated Sludge ◽

Biological Treatment ◽

Rrna Gene ◽

Activated Sludge Process ◽

Painting Process ◽

Process Wastewater

Activated sludge process is one of the wastewater treatment method that is applied for many wastewater types including painting process wastewater of automotive industry. This wastewater is well-known to have high heavy metals concentration which could deteriorate water environment if appropriate performance of the wastewater treatment could not be achieved. In this study, we monitored microbial community diversity in a Painting Biological Treatment (PBT) system. We applied a combination of cultivation and genotypic biological methods based on 16S rRNA gene sequence analysis to identify the diversity of active microbial community. The results showed that active microbes that could grow in this activated sludge system were dominated by Gram-negative bacteria. Based on 16S rRNA gene sequencing analysis, it was revealed that their microbial diversity has close association with Bacterium strain E286, Isosphaera pallida, Lycinibacillus fusiformis, Microbacterium sp., Orchobactrum sp., Pseudomonas guariconensis, Pseudomonas sp. strain MR84, Pseudomonas sp. MC 54, Serpens sp., Stenotrophomonas acidaminiphila, and Xylella fastidiosa with similarity of 86 – 99%. This findings reflects that microbial community in a Painting Biological Treatment (PBT) system using activated sludge process could adapt with xenobiotics in the wastewater and has a wide range of diversity indicating a complex metabolism mechanism in the treatment process.

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