Discriminating activated sludge flocs from biofilm microbial communities in a novel pilot-scale reciprocation MBR using high-throughput 16S rRNA gene sequencing

2018 ◽  
Vol 217 ◽  
pp. 268-277 ◽  
Author(s):  
Ryan De Sotto ◽  
Jaeho Ho ◽  
Woonyoung Lee ◽  
Sungwoo Bae
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
High Throughput ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Pilot Scale ◽  
Rrna Gene ◽  
Sludge Flocs ◽  
Rrna Gene Sequencing

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 MicroPheno: Predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples

2018 ◽  
Author(s):  
Ehsaneddin Asgari ◽  
Kiavash Garakani ◽  
Alice Carolyn McHardy ◽  
Mohammad R.K. Mofrad
Keyword(s):  
Deep Learning ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Gene Sequencing ◽  
Operational Taxonomic Unit ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Sequence Alignments ◽  
Rrna Gene Sequencing

Motivation: Microbial communities play important roles in the function and maintenance of various biosystems, ranging from the human body to the environment. A major challenge in microbiome research is the classification of microbial communities of different environments or host phenotypes. The most common and cost-effective approach for such studies to date is 16S rRNA gene sequencing. Recent falls in sequencing costs have increased the demand for simple, efficient, and accurate methods for rapid detection or diagnosis with proved applications in medicine, agriculture, and forensic science. We describe a reference- and alignment-free approach for predicting environments and host phenotypes from 16S rRNA gene sequencing based on k-mer representations that benefits from a bootstrapping framework for investigating the sufficiency of shallow sub-samples. Deep learning methods as well as classical approaches were explored for predicting environments and host phenotypes. Results: k-mer distribution of shallow sub-samples outperformed the computationally costly Operational Taxonomic Unit (OTU) features in the tasks of body-site identification and Crohn's disease prediction. Aside from being more accurate, using k-mer features in shallow sub-samples allows (i) skipping computationally costly sequence alignments required in OTU-picking, and (ii) provided a proof of concept for the sufficiency of shallow and short-length 16S rRNA sequencing for phenotype prediction. In addition, k-mer features predicted representative 16S rRNA gene sequences of 18 ecological environments, and 5 organismal environments with high macro-F1 scores of 0.88 and 0.87. For large datasets, deep learning outperformed classical methods such as Random Forest and SVM. Availability: The software and datasets are available at https://llp.berkeley.edu/micropheno.

scholarly journals High-throughput 16S rRNA gene sequencing reveals that 6-hydroxydopamine affects gut microbial environment

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0217194 ◽  
Author(s):  
Jin Gyu Choi ◽  
Eugene Huh ◽  
Namkwon Kim ◽  
Dong-Hyun Kim ◽  
Myung Sook Oh
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
High Throughput ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Rrna Gene Sequencing ◽  
Microbial Environment ◽  
6 Hydroxydopamine

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 High-Throughput Sequencing for Examining Salmonella Prevalence and Pathogen—Microbiota Relationships in Barn Swallows

2021 ◽  
Vol 9 ◽  
Author(s):  
Olivia N. Choi ◽  
Ammon Corl ◽  
Andrew Wolfenden ◽  
Avishai Lublin ◽  
Suzanne L. Ishaq ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Barn Swallows ◽  
Rrna Gene Sequencing

Studies in both humans and model organisms suggest that the microbiome may play a significant role in host health, including digestion and immune function. Microbiota can offer protection from exogenous pathogens through colonization resistance, but microbial dysbiosis in the gastrointestinal tract can decrease resistance and is associated with pathogenesis. Little is known about the effects of potential pathogens, such as Salmonella, on the microbiome in wildlife, which are known to play an important role in disease transmission to humans. Culturing techniques have traditionally been used to detect pathogens, but recent studies have utilized high throughput sequencing of the 16S rRNA gene to characterize host-associated microbial communities (i.e., the microbiome) and to detect specific bacteria. Building upon this work, we evaluated the utility of high throughput 16S rRNA gene sequencing for potential bacterial pathogen detection in barn swallows (Hirundo rustica) and used these data to explore relationships between potential pathogens and microbiota. To accomplish this, we first compared the detection of Salmonella spp. in swallows using 16S rRNA data with standard culture techniques. Second, we examined the prevalence of Salmonella using 16S rRNA data and examined the relationship between Salmonella-presence or -absence and individual host factors. Lastly, we evaluated host-associated bacterial diversity and community composition in Salmonella-present vs. -absent birds. Out of 108 samples, we detected Salmonella in six (5.6%) samples based on culture, 25 (23.1%) samples with unrarefied 16S rRNA gene sequencing data, and three (2.8%) samples with both techniques. We found that sex, migratory status, and weight were correlated with Salmonella presence in swallows. In addition, bacterial community composition and diversity differed between birds based on Salmonella status. This study highlights the value of 16S rRNA gene sequencing data for monitoring pathogens in wild birds and investigating the ecology of host microbe-pathogen relationships, data which are important for prediction and mitigation of disease spillover into domestic animals and humans.

scholarly journals Exogenous and endogenous microbiomes of wild-caught Phormia regina (Diptera: Calliphoridae) flies from a suburban farm by 16S rRNA gene sequencing

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jean M. Deguenon ◽  
Nicholas Travanty ◽  
Jiwei Zhu ◽  
Ann Carr ◽  
Steven Denning ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Phormia Regina ◽  
Rrna Gene ◽  
Blow Fly ◽  
Rrna Gene Sequencing ◽  
External Body

AbstractThe black blow fly, Phormia regina (Meigen) (Diptera: Calliphoridae) is one of the most abundant carrion flies in North America. Calliphorids are important in agriculture and animal production, veterinary sciences, forensics and medical entomology. While the role of flies in the epidemiology of human and animal diseases is an active area of research, little is known about the microorganisms associated with these insects. We examined the diversity of wild-caught black blow fly endogenous (internal body) and exogenous (external body) microbial communities using 16S rRNA gene sequencing. Overall, 27 phyla, 171 families and 533 genera were detected, and diversity was significantly higher (P < 0.05) on external body surfaces. At the genus level, Dysgonomonas, Ignatzschineria, Acinetobacter, Vagococcus, Myroides, and Wohlfahrtiimonas were predominant. Cloning and sequencing of nearly full-length fragments of the 16S rRNA gene showed that some of the species identified are known to be pathogenic to humans, animals, and plants. Myroides odoratimimus and Acinetobacter radioresistens are well-known, multi-drug resistant bacteria. These results provide a snapshot of the microbial communities harbored by adult black blow flies and call for more comprehensive studies to better characterize the role these flies may play in the transmission of pathogenic microorganisms.

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