scholarly journals Updating urinary microbiome analyses to enhance biologic interpretation

2021 ◽  
Author(s):  
Nazema Y Siddiqui ◽  
Li Ma ◽  
Linda Brubaker ◽  
Jialiang Mao ◽  
Carter Hoffman ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Bacterial Genome ◽  
Rrna Gene ◽  
Sequence Variant ◽  
Sequencing Data ◽  
Variable Regions ◽  
Urinary Microbiome ◽  
The Impact

Objective: An approach for assessing the urinary microbiome is 16S rRNA gene sequencing, where a segment of the bacterial genome is amplified and sequenced. Methods used to analyze these data are rapidly evolving, although the research implications are not known. This re-analysis of an existing dataset aimed to determine the impact of updated bioinformatic and statistical techniques. Methods: A prior Pelvic Floor Disorders Network (PFDN) study compared the urinary microbiome in 123 women with mixed urinary incontinence (MUI) and 84 controls. We used the PFDN unprocessed sequencing data of V1-V3 and V4-V6 16S variable regions, processed operational taxonomic unit (OTU) tables, and de-identified clinical data. We processed sequencing data with an updated bioinformatic pipeline, which used DADA2 to generate amplicon sequence variant (ASV) tables. Taxa from ASV tables were compared to OTU tables generated from the original processing; taxa from different variable regions (e.g., V1-V3 versus V4-V6) after updated processing were also compared. After updated processing, data were analyzed with multiple filtering thresholds. Several techniques were tested to cluster samples into microbial communities. Multivariable regression was used to test for associations between microbial communities and MUI, while controlling for potentially confounding variables. Results: Of taxa identified through updated bioinformatic processing, only 40% were identified originally, though taxa identified through both methods represented >99% of sequencing data in terms of relative abundance. When different 16S rRNA gene regions were sequenced from the same samples, there were differences noted in recovered taxa. When the original clustering methods were applied to reprocessed sequencing data, we confirmed differences in microbial communities associated with MUI. However, when samples were clustered with a different methodology, microbial communities were no longer associated with MUI. Conclusions: Updated bioinformatic processing techniques recover many different taxa compared to prior techniques, though most of these differences exist in low abundance taxa that occupy a small proportion of the overall microbiome. Detection of high abundance taxa are not significantly impacted by bioinformatic strategy. However, there are different biases for less abundant taxa; these differences as well as downstream clustering methodology and filtering thresholds may affect interpretation of overall results.

scholarly journals Dynamics of Soil Microbial Communities During Diazepam and Oxazepam Biodegradation in Soil Flooded by Water From a WWTP

2021 ◽  
Vol 12 ◽  
Author(s):  
Marc Crampon ◽  
Coralie Soulier ◽  
Pauline Sidoli ◽  
Jennifer Hellal ◽  
Catherine Joulian ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Soil Microbial Communities ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Treated Wastewater ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Rrna Gene Sequencing

The demand for energy and chemicals is constantly growing, leading to an increase of the amounts of contaminants discharged to the environment. Among these, pharmaceutical molecules are frequently found in treated wastewater that is discharged into superficial waters. Indeed, wastewater treatment plants (WWTPs) are designed to remove organic pollution from urban effluents but are not specific, especially toward contaminants of emerging concern (CECs), which finally reach the natural environment. In this context, it is important to study the fate of micropollutants, especially in a soil aquifer treatment (SAT) context for water from WWTPs, and for the most persistent molecules such as benzodiazepines. In the present study, soils sampled in a reed bed frequently flooded by water from a WWTP were spiked with diazepam and oxazepam in microcosms, and their concentrations were monitored for 97 days. It appeared that the two molecules were completely degraded after 15 days of incubation. Samples were collected during the experiment in order to follow the dynamics of the microbial communities, based on 16S rRNA gene sequencing for Archaea and Bacteria, and ITS2 gene for Fungi. The evolution of diversity and of specific operating taxonomic units (OTUs) highlighted an impact of the addition of benzodiazepines, a rapid resilience of the fungal community and an evolution of the bacterial community. It appeared that OTUs from the Brevibacillus genus were more abundant at the beginning of the biodegradation process, for diazepam and oxazepam conditions. Additionally, Tax4Fun tool was applied to 16S rRNA gene sequencing data to infer on the evolution of specific metabolic functions during biodegradation. It finally appeared that the microbial community in soils frequently exposed to water from WWTP, potentially containing CECs such as diazepam and oxazepam, may be adapted to the degradation of persistent contaminants.

scholarly journals Hidden heterogeneity and co-occurrence networks of soil prokaryotic communities revealed at the scale of individual soil aggregates

2020 ◽  
Author(s):  
Márton Szoboszlay ◽  
Christoph C. Tebbe
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Spatial Information ◽  
Soil Aggregates ◽  
Soil Microbial Communities ◽  
Rrna Gene ◽  
Ammonium Oxidation ◽  
Soil Microbial ◽  
The Impact

AbstractSequencing PCR-amplified gene fragments from metagenomic DNA is a widely applied method for studying the diversity and dynamics of soil microbial communities. Typically DNA is extracted from 0.25 to 1 g of soil. These amounts, however, neglect the heterogeneity of soil present at the scale of soil aggregates; and thus, ignore a crucial scale for understanding the structure and functionality of soil microbial communities. Here we show with a nitrogen-depleted agricultural soil the impact of reducing the amount of soil used for DNA extraction from 250 mg to approx. 1 mg in order to access spatial information on the prokaryotic community structure as indicated by 16S rRNA-gene amplicon analyses. Furthermore, we demonstrate that individual aggregates from the same soil differ in their prokaryotic communities. The analysis of 16S rRNA gene amplicon sequences from individual soil aggregates allowed us, in contrast to 250 mg soil samples, to construct a co-occurrence network that provides insight into the structure of microbial associations in the studied soil. Two dense clusters were apparent in the network, one dominated by Thaumarchaeota, known to be capable of ammonium oxidation at low N concentrations, and the other by Acidobacteria subgroup 6 probably representing an oligotrophic lifestyle to obtain energy from SOC. Overall this study demonstrates that DNA obtained from individual soil aggregates provides new insights into how microbial communities are assembled.

scholarly journals Evaluating the Impact of Wastewater Effluent on Microbial Communities in the Panke, an Urban River

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 888 ◽  
Author(s):  
Marcella Nega ◽  
Burga Braun ◽  
Sven Künzel ◽  
Ulrich Szewzyk
Keyword(s):  
Microbial Community ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Sampling Site ◽  
River Sediments ◽  
Microbial Community Composition ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Alpha And Beta Diversity ◽  
The Impact

Pharmaceuticals are consumed in high amounts and can enter as emerging organic compounds in surface waters as they are only partially retained in wastewater treatment plants (WWTPs). Receiving pharmaceuticals may burden the aquatic environment, as they are designed to be bioactive even at low concentrations. Sediment biofilm populations were analyzed in river sediments due to the exposure of an inflow of WWTP effluents. Illumina MiSeq 16S rRNA gene amplicon sequencing was performed of 108 sediment samples, which were taken from multiple cores within three sampling locations in the Panke River, with one sampling site located downstream of the inflow. Sequencing data were processed to infer microbial community structure in samples concerning the environmental variables, such as micropollutants and physicochemical parameters measured for each core. More than 25 different micropollutants were measured in pore water samples, in which bezafibrate, clofibric acid, carbamazepine, and diclofenac were detected at high concentrations. Bacterial 16S rRNA gene amplicons revealed Nitrospirae, Proteobacteria, Chloroflexi, Actinobacteria, Acidobacteria, Bacteroidetes, and Ignavibacteriae as the most abundant groups in the samples. Differences in microbial community composition were observed with respect to micropollutants. However, our findings revealed that the composition of the microbial community was not only governed by the effluent. The significant changes in the alpha- and beta-diversity were explained by phenobarbital and SO42−, which did not originate from the WWTP indicating that more unobserved factors are also likely to play a role in affecting the biofilm community’s composition.

scholarly journals Generation of Multimillion-Sequence 16S rRNA Gene Libraries from Complex Microbial Communities by Assembling Paired-End Illumina Reads

2011 ◽  
Vol 77 (11) ◽  
pp. 3846-3852 ◽  
Author(s):  
Andrea K. Bartram ◽  
Michael D. J. Lynch ◽  
Jennifer C. Stearns ◽  
Gabriel Moreno-Hagelsieb ◽  
Josh D. Neufeld
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
16S Rrna Gene Sequencing ◽  
Taxonomic Diversity ◽  
Illumina Genome Analyzer ◽  
Rrna Gene ◽  
Variable Regions ◽  
Gene Libraries

ABSTRACTMicrobial communities host unparalleled taxonomic diversity. Adequate characterization of environmental and host-associated samples remains a challenge for microbiologists, despite the advent of 16S rRNA gene sequencing. In order to increase the depth of sampling for diverse bacterial communities, we developed a method for sequencing and assembling millions of paired-end reads from the 16S rRNA gene (spanning the V3 region; ∼200 nucleotides) by using an Illumina genome analyzer. To confirm reproducibility and to identify a suitable computational pipeline for data analysis, sequence libraries were prepared in duplicate for both a defined mixture of DNAs from known cultured bacterial isolates (>1 million postassembly sequences) and an Arctic tundra soil sample (>6 million postassembly sequences). The Illumina 16S rRNA gene libraries represent a substantial increase in number of sequences over all extant next-generation sequencing approaches (e.g., 454 pyrosequencing), while the assembly of paired-end 125-base reads offers a methodological advantage by incorporating an initial quality control step for each 16S rRNA gene sequence. This method incorporates indexed primers to enable the characterization of multiple microbial communities in a single flow cell lane, may be modified readily to target other variable regions or genes, and demonstrates unprecedented and economical access to DNAs from organisms that exist at low relative abundances.

scholarly journals Minimizing spurious features in 16S rRNA gene amplicon sequencing

2018 ◽  
Author(s):  
Jing Wang ◽  
Qianpeng Zhang ◽  
Guojun Wu ◽  
Chenhong Zhang ◽  
Menghui Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Simulated Data ◽  
Amplicon Sequencing ◽  
Rrna Gene ◽  
Sequencing Data ◽  
The Real ◽  
Matthew’S Correlation Coefficient

The 16S rRNA gene amplicon sequencing is a widely used high-throughput method for the taxonomic inference in microbial communities. Many data analysis pipelines have been developed to enhance the accuracy in reflecting the real taxonomy, in order to better guide the downstream identification, isolation and mechanistic studies. Though rigorous quality filtration steps were adopted in these pipelines, with well-designed mock and simulated data sets, we found that there were still a widely divergent number of spurious features due to the “pseudo sequences” artificially generated during the PCR and sequencing process. These pseudo sequences were in low abundances, and were unreliable determined through a weighted re-sampling test. To minimize their influences on the characterization of taxonomy, we proposed an approach that contains two steps, an abundance filtering (AF) step and the subsequent AF-based OTU picking and remapping (AOR) step, which can efficiently decrease the spurious OTUs, sequences or oligotyping features, and improve Matthew's Correlation Coefficient (MCC) values in OTU clustering. The approach can be easily integrated with the popularly-used 16S rRNA sequencing data analysis pipelines, to make the number of OTUs, alpha and beta diversities from divergent pipelines more consistent with the real structure of microbial communities.

scholarly journals Minimizing spurious features in 16S rRNA gene amplicon sequencing

2018 ◽  
Author(s):  
Jing Wang ◽  
Qianpeng Zhang ◽  
Guojun Wu ◽  
Chenhong Zhang ◽  
Menghui Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Simulated Data ◽  
Amplicon Sequencing ◽  
Rrna Gene ◽  
Sequencing Data ◽  
The Real ◽  
Matthew’S Correlation Coefficient

The 16S rRNA gene amplicon sequencing is a widely used high-throughput method for the taxonomic inference in microbial communities. Many data analysis pipelines have been developed to enhance the accuracy in reflecting the real taxonomy, in order to better guide the downstream identification, isolation and mechanistic studies. Though rigorous quality filtration steps were adopted in these pipelines, with well-designed mock and simulated data sets, we found that there were still a widely divergent number of spurious features due to the “pseudo sequences” artificially generated during the PCR and sequencing process. These pseudo sequences were in low abundances, and were unreliable determined through a weighted re-sampling test. To minimize their influences on the characterization of taxonomy, we proposed an approach that contains two steps, an abundance filtering (AF) step and the subsequent AF-based OTU picking and remapping (AOR) step, which can efficiently decrease the spurious OTUs, sequences or oligotyping features, and improve Matthew's Correlation Coefficient (MCC) values in OTU clustering. The approach can be easily integrated with the popularly-used 16S rRNA sequencing data analysis pipelines, to make the number of OTUs, alpha and beta diversities from divergent pipelines more consistent with the real structure of microbial communities.

scholarly journals Exploring protocol bias in airway microbiome studies: one versus two PCR steps and 16S rRNA gene region V3 V4 versus V4

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Christine Drengenes ◽  
Tomas M. L. Eagan ◽  
Ingvild Haaland ◽  
Harald G. Wiker ◽  
Rune Nielsen
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Bacterial Load ◽  
Marker Gene ◽  
Gene Region ◽  
Lower Airway ◽  
Rrna Gene ◽  
Wide Range ◽  
Airway Microbiome ◽  
The Impact

Abstract Background Studies on the airway microbiome have been performed using a wide range of laboratory protocols for high-throughput sequencing of the bacterial 16S ribosomal RNA (16S rRNA) gene. We sought to determine the impact of number of polymerase chain reaction (PCR) steps (1- or 2- steps) and choice of target marker gene region (V3 V4 and V4) on the presentation of the upper and lower airway microbiome. Our analyses included lllumina MiSeq sequencing following three setups: Setup 1 (2-step PCR; V3 V4 region), Setup 2 (2-step PCR; V4 region), Setup 3 (1-step PCR; V4 region). Samples included oral wash, protected specimen brushes and protected bronchoalveolar lavage (healthy and obstructive lung disease), and negative controls. Results The number of sequences and amplicon sequence variants (ASV) decreased in order setup1 > setup2 > setup3. This trend appeared to be associated with an increased taxonomic resolution when sequencing the V3 V4 region (setup 1) and an increased number of small ASVs in setups 1 and 2. The latter was considered a result of contamination in the two-step PCR protocols as well as sequencing across multiple runs (setup 1). Although genera Streptococcus, Prevotella, Veillonella and Rothia dominated, differences in relative abundance were observed across all setups. Analyses of beta-diversity revealed that while oral wash samples (high biomass) clustered together regardless of number of PCR steps, samples from the lungs (low biomass) separated. The removal of contaminants identified using the Decontam package in R, did not resolve differences in results between sequencing setups. Conclusions Differences in number of PCR steps will have an impact of final bacterial community descriptions, and more so for samples of low bacterial load. Our findings could not be explained by differences in contamination levels alone, and more research is needed to understand how variations in PCR-setups and reagents may be contributing to the observed protocol bias.

Characterization of the depth-related changes in the microbial communities in Lake Hovsgol sediment by 16S rRNA gene-based approaches

2008 ◽  
Vol 46 (2) ◽  
pp. 125-136 ◽  
Author(s):  
Young-Do Nam ◽  
Youlboong Sung ◽  
Ho-Won Chang ◽  
Seong Woon Roh ◽  
Kyoung-Ho Kim ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Rrna Gene ◽  
Lake Hovsgol

scholarly journals Metagenome assembled genomes of novel taxa from an acid mine drainage environment

2021 ◽  
Author(s):  
Christen L. Grettenberger ◽  
Trinity L. Hamilton
Keyword(s):  
Acid Mine Drainage ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Mine Drainage ◽  
Biogeochemical Cycling ◽  
Rrna Gene ◽  
Metabolic Potential ◽  
Carbon And Nitrogen ◽  
Acid Mine

Acid mine drainage (AMD) is a global problem in which iron sulfide minerals oxidize and generate acidic, metal-rich water. Bioremediation relies on understanding how microbial communities inhabiting an AMD site contribute to biogeochemical cycling. A number of studies have reported community composition in AMD sites from 16S rRNA gene amplicons but it remains difficult to link taxa to function, especially in the absence of closely related cultured species or those with published genomes. Unfortunately, there is a paucity of genomes and cultured taxa from AMD environments. Here, we report 29 novel metagenome assembled genomes from Cabin Branch, an AMD site in the Daniel Boone National Forest, KY, USA. The genomes span 11 bacterial phyla and one Archaea and include taxa that contribute to carbon, nitrogen, sulfur, and iron cycling. These data reveal overlooked taxa that contribute to carbon fixation in AMD sites as well as uncharacterized Fe(II)-oxidizing bacteria. These data provide additional context for 16S rRNA gene studies, add to our understanding of the taxa involved in biogeochemical cycling in AMD environments, and can inform bioremediation strategies. IMPORTANCE Bioremediating acid mine drainage requires understanding how microbial communities influence geochemical cycling of iron and sulfur and biologically important elements like carbon and nitrogen. Research in this area has provided an abundance of 16S rRNA gene amplicon data. However, linking these data to metabolisms is difficult because many AMD taxa are uncultured or lack published genomes. Here, we present metagenome assembled genomes from 29 novel AMD taxa and detail their metabolic potential. These data provide information on AMD taxa that could be important for bioremediation strategies including taxa that are involved in cycling iron, sulfur, carbon, and nitrogen.

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