scholarly journals Controlling for contaminants in low biomass 16S rRNA gene sequencing experiments

2018 ◽  
Author(s):  
Lisa Karstens ◽  
Mark Asquith ◽  
Sean Davin ◽  
Damien Fair ◽  
W. Thomas Gregory ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Biomass ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Bacterial Dna ◽  
Computational Approaches ◽  
Rrna Gene Sequencing

AbstractBackgroundMicrobial communities are commonly studied using culture-independent methods such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination. This is particularly problematic for sites of low microbial biomass such as the urinary tract, placenta, and lower airway. Computational approaches have been proposed as a post-processing step to identify and remove potential contaminants, but their performance has not been independently evaluated.To identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we used a serial dilution of a mock microbial community. We evaluated two computational approaches to identify and remove contaminants: 1) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam and 2) predicting the proportion of experimental sample arising from defined contaminant sources implemented in SourceTracker.ResultsAs expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 79.12% of the most dilute sample arising from contaminant sequences. Inclusion of contaminant sequences in analyses leads to overinflated diversity estimates (up to 12 times greater than the expected values) and distorts microbiome composition. SourceTracker successfully removed over 98% of contaminants when the experimental environments are well defined. However, SourceTracker performed poorly when the experimental environment is unknown, failing to remove the majority of contaminants. Decontam successfully removed 74-91% of contaminants regardless of prior knowledge of the experimental environment.ConclusionsOur study indicates that computational methods can reduce the amount of contaminants in 16S rRNA gene sequencing experiments. The appropriate computational approach for removing contaminant sequences from an experiment depends on the prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

scholarly journals Controlling for Contaminants in Low-Biomass 16S rRNA Gene Sequencing Experiments

mSystems ◽  
2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Lisa Karstens ◽  
Mark Asquith ◽  
Sean Davin ◽  
Damien Fair ◽  
W. Thomas Gregory ◽  
...  
Keyword(s):  
Microbial Biomass ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Bacterial Dna ◽  
Dilution Series ◽  
Rrna Gene Sequencing

ABSTRACTMicrobial communities are commonly studied using culture-independent methods, such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination, particularly in low-microbial-biomass niches. Computational approaches to identify contaminant sequences have been proposed, but their performance has not been independently evaluated. To identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we created a mock microbial community dilution series. We evaluated four computational approaches to identify and remove contaminants, as follows: (i) filtering sequences present in a negative control, (ii) filtering sequences based on relative abundance, (iii) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam, and (iv) predicting the sequence proportion arising from defined contaminant sources implemented in SourceTracker. As expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 80.1% of the most diluted sample arising from contaminant sequences. Inclusion of contaminant sequences led to overinflated diversity estimates and distorted microbiome composition. All methods for contaminant identification successfully identified some contaminant sequences, which varied depending on the method parameters used and contaminant prevalence. Notably, removing sequences present in a negative control erroneously removed >20% of expected sequences. SourceTracker successfully removed over 98% of contaminants when the experimental environments were well defined. However, SourceTracker misclassified expected sequences and performed poorly when the experimental environment was unknown, failing to remove >97% of contaminants. In contrast, the Decontam frequency method did not remove expected sequences and successfully removed 70 to 90% of the contaminants.IMPORTANCEThe relative scarcity of microbes in low-microbial-biomass environments makes accurate determination of community composition challenging. Identifying and controlling for contaminant bacterial DNA are critical steps in understanding microbial communities from these low-biomass environments. Our study introduces the use of a mock community dilution series as a positive control and evaluates four computational strategies that can identify contaminants in 16S rRNA gene sequencing experiments in order to remove them from downstream analyses. The appropriate computational approach for removing contaminant sequences from an experiment depends on prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

scholarly journals Arsenic mobilization in a high arsenic groundwater revealed by metagenomic and Geochip analyses

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Zhou Jiang ◽  
Ping Li ◽  
Yanhong Wang ◽  
Han Liu ◽  
Dazhun Wei ◽  
...  
Keyword(s):  
Microbial Community ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Arsenic Mobilization ◽  
Rrna Gene Sequencing ◽  
High Arsenic ◽  
High Arsenic Groundwater

Abstract Microbial metabolisms of arsenic, iron, sulfur, nitrogen and organic matter play important roles in arsenic mobilization in aquifer. In this study, microbial community composition and functional potentials in a high arsenic groundwater were investigated using integrated techniques of RNA- and DNA-based 16S rRNA gene sequencing, metagenomic sequencing and functional gene arrays. 16S rRNA gene sequencing showed the sample was dominated by members of Proteobacteria (62.3–75.2%), such as genera of Simplicispira (5.7–6.7%), Pseudomonas (3.3–5.7%), Ferribacterium (1.6–4.4%), Solimonas (1.8–3.2%), Geobacter (0.8–2.2%) and Sediminibacterium (0.6–2.4%). Functional potential analyses indicated that organics degradation, assimilatory sulfate reduction, As-resistant pathway, iron reduction, ammonification, nitrogen fixation, denitrification and dissimilatory nitrate reduction to ammonia were prevalent. The composition and function of microbial community and reconstructed genome bins suggest that high level of arsenite in the groundwater may be attributed to arsenate release from iron oxides reductive dissolution by the iron-reducing bacteria, and subsequent arsenate reduction by ammonia-producing bacteria featuring ars operon. This study highlights the relationship between biogeochemical cycling of arsenic and nitrogen in groundwater, which potentially occur in other aquifers with high levels of ammonia and arsenic.

scholarly journals The Viable Microbiome of Human Milk Differs from the Metataxonomic Profile

Nutrients ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 4445
Author(s):  
Lisa F. Stinson ◽  
Michelle L. Trevenen ◽  
Donna T. Geddes
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Human Milk ◽  
Dna Content ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Bacterial Dna ◽  
Cutibacterium Acnes ◽  
Rrna Gene Sequencing

Bacteria in human milk contribute to the establishment of the infant gut microbiome. As such, numerous studies have characterized the human milk microbiome using DNA sequencing technologies, particularly 16S rRNA gene sequencing. However, such methods are not able to differentiate between DNA from viable and non-viable bacteria. The extent to which bacterial DNA detected in human milk represents living, biologically active cells is therefore unclear. Here, we characterized both the viable bacterial content and the total bacterial DNA content (derived from viable and non-viable cells) of fresh human milk (n = 10). In order to differentiate the living from the dead, a combination of propidium monoazide (PMA) and full-length 16S rRNA gene sequencing was used. Our results demonstrate that the majority of OTUs recovered from fresh human milk samples (67.3%) reflected DNA from non-viable organisms. PMA-treated samples differed significantly in their bacterial composition compared to untreated samples (PERMANOVA p < 0.0001). Additionally, an OTU mapping to Cutibacterium acnes had a significantly higher relative abundance in PMA-treated (viable) samples. These results demonstrate that the total bacterial DNA content of human milk is not representative of the viable human milk microbiome. Our findings raise questions about the validity of conclusions drawn from previous studies in which viability testing was not used, and have broad implications for the design of future work in this field.

scholarly journals Coastal Bacterial Community Response to Glacier Melting in the Western Antarctic Peninsula

2021 ◽  
Vol 9 (1) ◽  
pp. 88
Author(s):  
María Estrella Alcamán-Arias ◽  
Sebastián Fuentes-Alburquenque ◽  
Pablo Vergara-Barros ◽  
Jerónimo Cifuentes-Anticevic ◽  
Josefa Verdugo ◽  
...  
Keyword(s):  
Microbial Community ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Antarctic Peninsula ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Western Antarctic Peninsula ◽  
Dilution Experiments ◽  
Rrna Gene Sequencing

Current warming in the Western Antarctic Peninsula (WAP) has multiple effects on the marine ecosystem, modifying the trophic web and the nutrient regime. In this study, the effect of decreased surface salinity on the marine microbial community as a consequence of freshening from nearby glaciers was investigated in Chile Bay, Greenwich Island, WAP. In the summer of 2016, samples were collected from glacier ice and transects along the bay for 16S rRNA gene sequencing, while in situ dilution experiments were conducted and analyzed using 16S rRNA gene sequencing and metatranscriptomic analysis. The results reveal that certain common seawater genera, such as Polaribacter, Pseudoalteromonas and HTCC2207, responded positively to decreased salinity in both the bay transect and experiments. The relative abundance of these bacteria slightly decreased, but their functional activity was maintained and increased the over time in the dilution experiments. However, while ice bacteria, such as Flavobacterium and Polaromonas, tolerated the increased salinity after mixing with seawater, their gene expression decreased considerably. We suggest that these bacterial taxa could be defined as sentinels of freshening events in the Antarctic coastal system. Furthermore, these results suggest that a significant portion of the microbial community is resilient and can adapt to disturbances, such as freshening due to the warming effect of climate change in Antarctica.

scholarly journals Seasonal Sampling of a Microbial Community in the Sediment of Geoje-Hansan Bay, Republic of Korea

2021 ◽  
Vol 10 (31) ◽  
Author(s):  
Seongsik Park ◽  
Hee-Eun Woo ◽  
Jong-Oh Kim ◽  
In-Cheol Lee ◽  
Seokjin Yoon ◽  
...  
Keyword(s):  
Microbial Community ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Marine Pollution ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Republic Of Korea ◽  
Rrna Gene ◽  
Oyster Farms ◽  
Rrna Gene Sequencing

Several oyster farms are concentrated in Geoje-Hansan Bay, Republic of Korea, and there is concern about marine pollution. Hence, we monitored the sediment at this site for a year using 16S rRNA gene sequencing. The predominant phyla were Proteobacteria (69.9 to 79.1%) and Bacteroidetes (8.2 to 10.6%) in all seasons.

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