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196 results for “16S rRNA”
Cecal and colinic 16s rRNA sequencing OTUs
<p>The purpose of this study was to investigate the effects of Fermented Spent Mushroom Substrates (FSMS) on growth performance, serum biochemical, gut digestive enzyme activity, microbial community, genes expression of tight junction proteins and volatile fatty acids (VFA) in hindgut (colon and cecum) of weaned piglets. A total of 100 weaned Yihao native pigs (Native × Duroc, 50 males and 50 females) were allocated to two groups with five replicates and ten pigs per replicate. Pigs in the control group were fed a basal diet (BD group) and the others were fed basal diets supplemented with 3% FSMS (FSMS group). Relative to the BD Group, it had better results for Final weight, average daily gain (ADG) and feed conversion ratio (FCR) in FSMS Group but not significant (<i>p</i> > 0.05) which was accompanied by improved serum T3, IgG and IgA (<i>p</i> < 0.05) but lower serum TP, ALB, TC and TG during the overall period (<i>p</i> < 0.05). Similarly, FSMS significantly up-regulated (p < 0.05) the expression of Duodenal tight junction proteins such as pTJP1, pTJP2 and pOCLN. Meanwhile, Isobutyric acid, Valeric acid and Isovaleric acid levels were increased while Propanoic acid was decreased (<i>p</i> < 0.05) in the FSMS group than the BD group. In addition, the piglets in FSMS group changed the microbial diversity in the colon and cecum. 16S rRNA gene sequencing-based compositional analysis of the colonic and cecal microbiota showed differences in relative abundance of bacterial phyla (Firmicutes, Bacteroidetes etc.), genus (Lactobacillus, Streptococcus, Roseburia etc.) and species (Lactobacillus gasseri, Clostridium_disporicum etc.) between the BD and FSMS fed piglets. In conclusion, dietary supplementation with FSMS benefited to the intestinal mucosal barrier, immunity, and composition of microbiota.</p>
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.
Data from: COI is better than 16S rRNA for DNA barcoding Asiatic salamanders (Amphibia: Caudata: Hynobiidae)
The 5' region of the mitochondrial DNA (mtDNA) gene cytochrome c oxidase I (COI) is the standard marker for DNA barcoding. However, because COI tends to be highly variable in amphibians, sequencing is often challenging. Consequently, another mtDNA gene, 16S rRNA gene, is often advocated for amphibian barcoding. Herein, we directly compare the usefulness of COI and 16S in discriminating species of hynobiid salamanders using 130 individuals. Species identification and classification of these animals, which are endemic to Asia, is often based on morphology only. Analysis of Kimura 2-parameter genetic distances (K2P) documents the mean intraspecific variation for COI and 16S rRNA genes to be 1.4% and 0.3%, respectively. Whereas COI can always identify species, sometimes 16S cannot. Intra- and interspecific genetic divergences occasionally overlap in both markers thus reducing the value of a barcoding gap to identify genera. Regardless, COI is the better DNA barcoding marker for hynobiids. In addition to the comparison of two potential markers, high levels of intraspecific divergence in COI (>5%) suggest that both Onychodactylus fischeri and Salamandrella keyserlingii might be composites of cryptic species.
Feature table of nearly 330k 16S V4 rRNA microbiome samples
<p>Feature table of nearly 330k 16S V4 rRNA microbiome samples</p>
Principal coordinates for hundreds of thousands of 16S V4 rRNA samples
<p>Principal coordinates for hundreds of thousands of 16S V4 rRNA samples</p>
Supplementary material 1 from: Sogawa S, Tsuchiya K, Nagai S, Shimode S, Kuwahara VS (2022) Annual dynamics of eukaryotic and bacterial communities revealed by 18S and 16S rRNA metabarcoding in the coastal ecosystem of Sagami Bay, Japan. Metabarcoding and Metagenomics 6: e78181. https://doi.org/10.3897/mbmg.6.78181
Figures S1–S8
Supplementary material 4 from: Pham HTM, Karanovic I (2022) Four new Parasterope (Ostracoda, Myodocopina) from the Northwest Pacific and their phylogeny based on 16S rRNA. ZooKeys 1095: 13-42. https://doi.org/10.3897/zookeys.1095.77996
Table S3
Supplementary material 1 from: Pham HTM, Karanovic I (2022) Four new Parasterope (Ostracoda, Myodocopina) from the Northwest Pacific and their phylogeny based on 16S rRNA. ZooKeys 1095: 13-42. https://doi.org/10.3897/zookeys.1095.77996
A checklist species of Parasterope Kornicker, 1975
Supplementary material 3 from: Pham HTM, Karanovic I (2022) Four new Parasterope (Ostracoda, Myodocopina) from the Northwest Pacific and their phylogeny based on 16S rRNA. ZooKeys 1095: 13-42. https://doi.org/10.3897/zookeys.1095.77996
Table S2
Supplementary material 5 from: Pham HTM, Karanovic I (2022) Four new Parasterope (Ostracoda, Myodocopina) from the Northwest Pacific and their phylogeny based on 16S rRNA. ZooKeys 1095: 13-42. https://doi.org/10.3897/zookeys.1095.77996
Table S4
Supplementary material 2 from: Pham HTM, Karanovic I (2022) Four new Parasterope (Ostracoda, Myodocopina) from the Northwest Pacific and their phylogeny based on 16S rRNA. ZooKeys 1095: 13-42. https://doi.org/10.3897/zookeys.1095.77996
Table S1
Wstępna analiza genów 16s rRNA oraz nodA nierizobiowych endosymbiontów koniczyny białej (Trifolium repens) i czerwonej (Trifolium pratense)
<p><span>Materiał do badań stanowiło DNA genomowe wyizolowane z hodowli płynnej izolatów bakteryjnych zasiedlających brodawki korzeniowe. DNA genomowe izolowano komercyjnym zestawem Genomic Mini firmy A&A biotechnology. Następnie przeprowadzno reakcję PCR ze starterami <span>Y1 (5′-TGGCTCAGAACGAACGCTGGCGGC-3′), Y2 (5′-CCCACTGCTGCCTCCCGTAGGAGT-3′) amplifikującymi fragment genu </span></span><span>16S rRNA oraz </span><span>gyrA-F 5’-CAGTCAGGAAATGCGTACGTCCTT-3’, gyrA-R 5’- CAAGGTAATGCTCCAGGCATTGCT-3’; atpDF 5’-ATCGGCGAGCCGGTCGACGA-3’, atpDR 5’-GCCGACACTTCCGAACCNGCCTG-3’; recA6F 5’-CGKCTSGTAGAGGAYAAATCGGTGGA-3’, recA555R 5’-CGRATCTGGTTGATGAAGTCACCAT-3’; nodA-1 5’-TGCRGTGGAARNTRNNCTGG-3’, nodA-2 5’-GGNCCGTCRTCRAAWGTCAR-3’. Produkty reakcji PCR oczyszczono komercyjnym zestawem Clean-up, jakość i ilość produktów sprawdzono spektrofotometrycznie. Usługę sekwencjonowania zlecono firmie GENOMED.</span></p>
Fig. 1 in MITOCHONDRIAL 16S AND 12S rRNA SEQUENCE ANALYSIS IN FOUR SALMONID SPECIES FROM ROMANIA
Fig. 1. Variable sites in 16S rRNA (1) and 12S rRNA (2). The numbers represent the position occupied in the 16S rRNA, and 12S rRNA respectively. Identical sites are indicated by the symbol "·" and
Data from: Assessment of a 16S rRNA amplicon Illumina sequencing procedure for studying the microbiome of a symbiont-rich aphid genus
The bacterial communities inhabiting arthropods are generally dominated by a few endosymbionts that play an important role in the ecology of their hosts. Rather than comparing bacterial species richness across samples, ecological studies on arthropod endosymbionts often seek to identify the main bacterial strains associated with each specimen studied. The filtering out of contaminants from the results and the accurate taxonomic assignment of sequences are therefore crucial in arthropod microbiome studies. We aimed here to validate an Illumina 16S rRNA gene sequencing protocol and analytical pipeline for investigating endosymbiotic bacteria associated with aphids. Using replicate DNA samples from 12 species (Aphididae: Lachninae, Cinara) and several controls, we removed individual sequences not meeting a minimum threshold number of reads in each sample and carried out taxonomic assignment for the remaining sequences. With this approach, we show that: i) contaminants accounted for a negligible proportion of the bacteria identified in our samples; ii) the taxonomic composition of our samples and the relative abundance of reads assigned to a taxon were very similar across PCR and DNA replicates for each aphid sample; in particular, bacterial DNA concentration had no impact on the results. Furthermore, by analysing the distribution of unique sequences across samples rather than aggregating them into operational taxonomic units (OTUs), we gained insight into the specificity of endosymbionts for their hosts. Our results confirm that Serratia symbiotica is often present in Cinara species, in addition to the primary symbiont, Buchnera aphidicola. Furthermore, our findings reveal new symbiotic associations with Erwinia and Sodalis-related bacteria. We conclude with suggestions for generating and analysing 16S rRNA gene sequences for arthropod endosymbiont studies.
FIGURE 4 in Phylogenetic relationships within the genus Staurois (Anura, Ranidae) based on 16S rRNA sequences
FIGURE 4. Relationships among the genus Staurois as inferred from Bayesian analysis.
FIGURE 2 in Phylogenetic relationships within the genus Staurois (Anura, Ranidae) based on 16S rRNA sequences
FIGURE 2. Localities of specimens examined for this study.
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.
Figure 4 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 4 - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
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International Brain Laboratory public data
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OpenNeuro
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