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.

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 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.

Screening and Molecular Characterization of Cellulase Producing Actinobacteria from Litchi Orchard

2019 ◽  
Vol 13 (1) ◽  
pp. 90-101
Author(s):  
Sanju Kumari ◽  
Utkarshini Sharma ◽  
Rohit Krishna ◽  
Kanak Sinha ◽  
Santosh Kumar
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Molecular Characterization ◽  
Biochemical Characterization ◽  
Evolutionary Relationship ◽  
Gene Sequencing ◽  
Cellulase Activity ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Rrna Gene Sequencing

Background: Cellulolysis is of considerable economic importance in laundry detergents, textile and pulp and paper industries and in fermentation of biomass into biofuels. Objective: The aim was to screen cellulase producing actinobacteria from the fruit orchard because of its requirement in several chemical reactions. Methods: Strains of actinobacteria were isolated on Sabouraud’s agar medium. Similarities in cultural and biochemical characterization by growing the strains on ISP medium and dissimilarities among them perpetuated to recognise nine groups of actinobacteria. Cellulase activity was measured by the diameter of clear zone around colonies on CMC agar and the amount of reducing sugar liberated from carboxymethyl cellulose in the supernatant of the CMC broth. Further, 16S rRNA gene sequencing and molecular characterization were placed before NCBI for obtaining recognition with accession numbers. Results: Prominent clear zones on spraying Congo Red were found around the cultures of strains of three groups SK703, SK706, SK708 on CMC agar plates. The enzyme assay for carboxymethylcellulase displayed extra cellulase activity in broth: 0.14, 0.82 and 0.66 &#181;mol mL-1 min-1, respectively at optimum conditions of 35°C, pH 7.3 and 96 h of incubation. However, the specific cellulase activities per 1 mg of protein did not differ that way. It was 1.55, 1.71 and 1.83 μmol mL-1 min-1. The growing mycelia possessed short compact chains of 10-20 conidia on aerial branches. These morphological and biochemical characteristics, followed by their verification by Bergey’s Manual, categorically allowed the strains to be placed under actinobacteria. Further, 16S rRNA gene sequencing, molecular characterization and their evolutionary relationship through phylogenetics also confirmed the putative cellulase producing isolates of SK706 and SK708 subgroups to be the strains of Streptomyces. These strains on getting NCBI recognition were christened as Streptomyces glaucescens strain SK91L (KF527284) and Streptomyces rochei strain SK78L (KF515951), respectively. Conclusion: Conclusive evidence on the basis of different parameters established the presence of cellulase producing actinobacteria in the litchi orchard which can convert cellulose into fermentable sugar.

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

Microbiome ◽  
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.

scholarly journals Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Francesco Durazzi ◽  
Claudia Sala ◽  
Gastone Castellani ◽  
Gerardo Manfreda ◽  
Daniel Remondini ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Shotgun Sequencing ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Experimental Conditions ◽  
Rrna Gene Sequencing

AbstractIn this paper we compared taxonomic results obtained by metataxonomics (16S rRNA gene sequencing) and metagenomics (whole shotgun metagenomic sequencing) to investigate their reliability for bacteria profiling, studying the chicken gut as a model system. The experimental conditions included two compartments of gastrointestinal tracts and two sampling times. We compared the relative abundance distributions obtained with the two sequencing strategies and then tested their capability to distinguish the experimental conditions. The results showed that 16S rRNA gene sequencing detects only part of the gut microbiota community revealed by shotgun sequencing. Specifically, when a sufficient number of reads is available, Shotgun sequencing has more power to identify less abundant taxa than 16S sequencing. Finally, we showed that the less abundant genera detected only by shotgun sequencing are biologically meaningful, being able to discriminate between the experimental conditions as much as the more abundant genera detected by both sequencing strategies.

scholarly journals Non-Specific Immunity Associated Gut Microbiome in Aristichthys nobilis under Different Rearing Strategies

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 916
Author(s):  
Jianming Yuan ◽  
Zhijian Wang ◽  
Bo Wang ◽  
Huiqing Mei ◽  
Xuliang Zhai ◽  
...  
Keyword(s):  
Enzyme Activity ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Bighead Carp ◽  
Live Food ◽  
Rrna Gene ◽  
Food Fish ◽  
Rrna Gene Sequencing

To understand the intestinal microbial diversity and community structure of bighead carp (Aristichthys nobilis) under different feeding strategies, 39 fish from three groups (A: 9 fish, natural live food only; B: 15 fish, natural live food + fish formulated feeds; C: 15 fish, natural live food + fish formulated feed + lactic acid bacteria) were obtained for the high throughput 16S rRNA gene sequencing. We first examined five non-specific immunity indications of the carp—lysozyme (LZM), catalase (CAT), glutathione reductase (GR), glutathione peroxidase (GSH-PX), and superoxide dismutase (SOD). Interestingly, the composition of gut microbiota and related non-specific immune indices were affected by the feeding treatment of the bighead carp. Notably, all enzyme activity indexes were significantly different (p < 0.01) in the spleen and three enzyme activity indexes (LZM, GSH-PX, and SOD) had significant differences in the hepatopancreas (p < 0.001) of the carp from the three groups. The 16S rRNA gene sequencing showed higher diversity in groups B and C. Compared to group A, the relative abundance of Actinobacteria increased significantly and the relative abundance of Proteobacteria and Firmicutes decreased significantly in groups B and C at the phylum level. Functional analysis revealed the association between non-specific immune indicators and import genera in the hepatopancreas and spleen of bighead carp. This study provides new insights into the gut microbiomes and non-specific immune of bighead carp.

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