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66 results for “16S rRNA gene”
16S rRNA gene sequencing data from: Breastmilk IgG engages the neonatal immune system to instruct immune responses to gut antigens
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Lower St. Lawrence Estuary bacterial 16S rRNA gene diversity
<p>The Estuary and Gulf of St. Lawrence (EGSL) in eastern Canada is among the largest and most productive coastal ecosystems in the world.<b> </b>Very little information on bacterial diversity exists, hampering our understanding of the relationships between bacterial community structure and biogeochemical function in the EGSL. During the productive spring period, we investigated free-living and particle-associated bacterial communities across the stratified waters of the Lower St. Lawrence Estuary, including the particle-rich surface and bottom boundary layers. Modeling of community structure based on 16S rRNA gene and transcript diversity identified bacterial assemblages specifically associated with four habitat types defined by water mass (upper water or lower water column) and size fraction (free-living or particle-associated). Assemblages from the upper waters represent sets of co-occurring bacterial populations that are widely distributed across Lower St. Lawrence Estuary surface waters., and likely key contributors to organic matter degradation during the spring. In addition, we provide strong evidence that particles in deep hypoxic waters and the bottom boundary layer support a metabolically-active bacterial community that is compositionally distinct compared to surface particles and the free-living communities. Among the distinctive features of the bacterial assemblage associated with lower water particles was the presence of uncultivated lineages of Deltaproteobacteria, including marine Myxobacteria. Overall, these results provide an important ecological framework for further investigations of the biogeochemical contributions of bacterial populations in this important coastal marine ecosystem.</p>
Extended Data Fig. 2-27 Geographical information of bioinformatic predicted samples based on the analysis of 16S rRNA gene in four PE degrading bacteria
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16S rRNA gene data for aerobic BTEX-degrading enrichments exposed to sulfonamide polyfluorinated substances in fire-fighting foams and transformation products
<p>Per- and polyfluoroalkyl substances (PFASs) from aqueous film forming foams (AFFFs) can hinder bioremediation of co-contaminants, such as trichloroethene (TCE) and benzene, toluene, ethylbenzene, and xylene (BTEX). Anaerobic dechlorination can require bioaugmentation of <em>Dehalococcoides</em> and for BTEX, oxygen is often sparged to stimulate in-situ aerobic biodegradation. We tested PFAS inhibition to TCE and BTEX bioremediation by exposing an anaerobic TCE-dechlorinating co-culture, an aerobic BTEX-degrading enrichment culture, and an anaerobic toluene-degrading enrichment culture to n-dimethyl perfluorohexane sulfonamido amine (AmPr-FHxSA), perfluorohexane sulfonamide (FHxSA), perfluorohexane sulfonic acid (PFHxS), or non-fluorinated surfactant sodium dodecyl sulfate (SDS). The anaerobic TCE-dechlorinating co-culture was resistant to individual PFASs exposures but was inhibited by >1,000x diluted AFFF. FHxSA and AmPr-FHxSA inhibited the aerobic BTEX-degrading enrichment. The anaerobic toluene-degrading enrichment was not inhibited by AFFF or individual PFASs. Increases in amino acids in the anaerobic TCE-dechlorinating co-culture compared to the control indicated stress response, while the BTEX culture exhibited lower concentrations of all amino acids upon exposure to most surfactants (both fluorinated and non-fluorinated) compared to the control. These data suggest the main mechanisms of microbial toxicity are related to interactions with cell membrane synthesis as well as protein stress signaling.</p>
Fig. 1. Neighbour-joining phylogenetic tree derived using 16S rRNA gene sequences, showing the relationships between strain HNM0687T in Gordonia mangrovi sp. nov., a novel actinobacterium isolated from mangrove soil in Hainan
Fig. 1. Neighbour-joining phylogenetic tree derived using 16S rRNA gene sequences, showing the relationships between strain HNM0687T and other type strains of the genus Gordonia. Only values above 50% are shown. Asterisks represent clades that were also recovered by the maximum-likelihood and maximum-parsimony methods. Bar, one nucleotide substitution per 100 nucleotides.
Supplementary material 1 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
COI sequences dataset of Dicronocephalus species in this study.: Explanation note: This COI data includes 50 individual sequences of the examined Dicronocephalus species and subspecies in this study
Supplementary material 3 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
The combined dataset of COI and 16S rRNA of Dicronocephalus species in this study.: Explanation note: There is the concatenated sequences of COI and 16S rRNA genes correspondence with each sample.
Supplementary material 2 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
16S rRNA sequences data set of Dicronocephalus species in this study.: Explanation note: This 16S rRNA data includes 46 individual sequences of the examined Dicronocephalus species in this study.
FIGURE 1 in Phylogenetic relationships among the genera of the Penaeidae (Crustacea: Decapoda) revealed by mitochondrial 16S rRNA gene sequences
FIGURE 1. Morphological phylogeny of the penaeid genera proposed by (a) Kubo 1949, reconstructed from text (genera in brackets were not fully analyzed and '?' refers to uncertain relationship) and (b) Burkenroad 1983, reconstructed from key (mentioned by the author as "...a natural key down to the level of genus"), with Penaeini as Peneini, Parapenaeini as Parapeneini, Trachypenaeini as Trachypeneini, and Metapenaeus as Mangalura. *Considered to be the most primitive genus in the family.
FIGURE 2 in Phylogenetic relationships among the genera of the Penaeidae (Crustacea: Decapoda) revealed by mitochondrial 16S rRNA gene sequences
FIGURE 2. BIO-neighbor-joining (BIO-NJ) tree of Penaeidae based on partial mitochondrial 16S rRNA gene sequences. Numbers on branches indicate bootstrap values from BIO-NJ (normal text), maximum parsimony (in italics), maximum likelihood (in bold) analyses and posterior probability values from Bayesian (in italics bold) analyses. Bootstrap values below 50% are not shown. A, B, C refer to the three main clades in the tree. Parapenaeini, Trachypenaeini and Penaeini are the three groups as defined by Burkenroad (1983).
The 16S rRNA genes of five strains of the genus Vibrio
<p>The 16S rRNA genes of five strains of the genus Vibrio. These strains isolated from marine sediments.</p>
Alpha Defensin and 16S rRNA Gene in Diagnosis of PJI
ClinicalTrials.gov study NCT03714165. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Using 16S rRNA Gene Sequencing Analysis Intestinal Microbiota in Constipation Patients
ClinicalTrials.gov study NCT02984969. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
16S rRNA gene data for aerobic BTEX-degrading enrichments exposed to sulfonamide polyfluorinated substances in fire-fighting foams and transformation products
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Lower St. Lawrence Estuary bacterial 16S rRNA gene diversity
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Macrosystems 16S rRNA Genes for Bacteria and Archaea at HJA, HFR, BCI, CWT, LUQ, and NWT - UPARSE Resample 20K
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This data set captures temperature-dependent latitudinal microbial diversity sampled for in forest soils based on taxonomic and phylogenetic diversity observed on 16S rRNA genes for bacteria and archaea by the University of Oklahoma Institute for Environmental Genomics as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Exploring protocol bias in airway microbiome studies: One versus two PCR steps and 16S rRNA gene region V3 V4 versus V4
<p>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 <i>Streptococcus</i>, <i>Prevotella</i>, <i>Veillonella</i> and <i>Rothia</i> 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.</p>
Data from: Phylogenetic relatedness determined between antibiotic resistance and 16S rRNA genes in actinobacteria
Background: Distribution and evolutionary history of resistance genes in environmental actinobacteria provide information on intensity of antibiosis and evolution of specific secondary metabolic pathways at a given site. To this day, actinobacteria producing biologically active compounds were isolated mostly from soil but only a limited range of soil environments were commonly sampled. Consequently, soil remains an unexplored environment in search for novel producers and related evolutionary questions. Results: Ninety actinobacteria strains isolated at contrasting soil sites were characterized phylogenetically by 16S rRNA gene, for presence of erm and ABC transporter resistance genes and antibiotic production. An analogous analysis was performed in silico with 246 and 31 strains from Integrated Microbial Genomes (JGI_IMG) database selected by the presence of ABC transporter genes and erm genes, respectively. In the isolates, distances of erm gene sequences were significantly correlated to phylogenetic distances based on 16S rRNA genes, while ABC transporter gene distances were not. The phylogenetic distance of isolates was significantly correlated to soil pH and organic matter content of isolation sites. In the analysis of JGI_IMG datasets the correlation between phylogeny of resistance genes and the strain phylogeny based on 16S rRNA genes or five housekeeping genes was observed for both the erm genes and ABC transporter genes in both actinobacteria and streptomycetes. However, in the analysis of sequences from genomes where both resistance genes occurred together the correlation was observed for both ABC transporter and erm genes in actinobacteria but in streptomycetes only in the erm gene. Conclusions: The type of erm resistance gene sequences was influenced by linkage to 16S rRNA gene sequences and site characteristics. The phylogeny of ABC transporter gene was correlated to 16S rRNA genes mainly above the genus level. The results support the concept of new specific secondary metabolite scaffolds occurring more likely in taxonomically distant producers but suggest that the antibiotic selection of gene pools is also influenced by site conditions.
Data from: Bacterial characterization of Beijing drinking water by flow cytometry and MiSeq sequencing of the 16S rRNA gene
Flow cytometry (FCM) and 16S rRNA gene sequencing data are commonly used to monitor and characterize microbial differences in drinking water distribution systems. In this study, to assess microbial differences in drinking water distribution systems, 12 water samples from different sources water (groundwater, GW; surface water, SW) were analyzed by FCM, heterotrophic plate count (HPC), and 16S rRNA gene sequencing. FCM intact cell concentrations varied from 2.2 × 103 cells/mL to 1.6 × 104 cells/mL in the network. Characteristics of each water sample were also observed by FCM fluorescence fingerprint analysis. 16S rRNA gene sequencing showed that Proteobacteria (76.9–42.3%) or Cyanobacteria (42.0–3.1%) was most abundant among samples. Proteobacteria were abundant in samples containing chlorine, indicating resistance to disinfection. Interestingly, Mycobacterium, Corynebacterium, and Pseudomonas, were detected in drinking water distribution systems. There was no evidence that these microorganisms represented a health concern through water consumption by the general population. However, they provided a health risk for special crowd, such as the elderly or infants, patients with burns and immune-compromised people exposed by drinking. The combined use of FCM to detect total bacteria concentrations and sequencing to determine the relative abundance of pathogenic bacteria resulted in the quantitative evaluation of drinking water distribution systems. Knowledge regarding the concentration of opportunistic pathogenic bacteria will be particularly useful for epidemiological studies.
Figure 5 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
Figure 5 - Anterior edge of clypeus of Dicronocephalus. A Dicronocephalus adamsi adamsi B Dicronocephalus adamsi drumonti C Dicranocephalus yui yui D Dicronocephalus dabryi E Dicronocephalus uenoi katoi F Dicronocephalus wallichii bowringi G Dicronocephalus wallichii wallichii H Dicronocephalus wallichii bourgoini.
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International Brain Laboratory public data
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OpenNeuro
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