scholarly journals Cow Teat Skin, a Potential Source of Diverse Microbial Populations for Cheese Production

2011 ◽  
Vol 78 (2) ◽  
pp. 326-333 ◽  
Author(s):  
Isabelle Verdier-Metz ◽  
Geneviève Gagne ◽  
Stéphanie Bornes ◽  
Françoise Monsallier ◽  
Philippe Veisseire ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Raw Milk ◽  
Rrna Gene ◽  
Clavibacter Michiganensis ◽  
Content Type ◽  
Rrna Gene Sequencing ◽  
Nonstarter Lactic Acid Bacteria

ABSTRACTThe diversity of the microbial community on cow teat skin was evaluated using a culture-dependent method based on the use of different dairy-specific media, followed by the identification of isolates by 16S rRNA gene sequencing. This was combined with a direct molecular approach by cloning and 16S rRNA gene sequencing. This study highlighted the large diversity of the bacterial community that may be found on teat skin, where 79.8% of clones corresponded to various unidentified species as well as 66 identified species, mainly belonging to those commonly found in raw milk (Enterococcus,Pediococcus,Enterobacter,Pantoea,Aerococcus, andStaphylococcus). Several of them, such as nonstarter lactic acid bacteria (NSLAB),Staphylococcus, andActinobacteria, may contribute to the development of the sensory characteristics of cheese during ripening. Therefore, teat skin could be an interesting source or vector of biodiversity for milk. Variations of microbial counts and diversity between the farms studied have been observed. Moreover,Staphylococcus auricularis,Staphylococcus devriesei,Staphylococcus arlettae,Streptococcus bovis,Streptococcus equinus,Clavibacter michiganensis,Coprococcus catus, orArthrobacter gandavensiscommensal bacteria of teat skin and teat canal, as well as human skin, are not common in milk, suggesting that there is a breakdown of microbial flow from animal to milk. It would then be interesting to thoroughly study this microbial flow from teat to milk.

scholarly journals Relative Prevalence and Antimicrobial Susceptibility of Clinical Isolates of Elizabethkingia Species Based on 16S rRNA Gene Sequencing

2016 ◽  
Vol 55 (1) ◽  
pp. 274-280 ◽  
Author(s):  
Mi-Soon Han ◽  
Hyunsoo Kim ◽  
Yangsoon Lee ◽  
Myungsook Kim ◽  
Nam Su Ku ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Antimicrobial Susceptibility ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Content Type ◽  
Maldi Tof ◽  
Rrna Gene Sequencing ◽  
Relative Prevalence

ABSTRACT Some of the previously reported clinical isolates of Elizabethkingia meningoseptica may be later named species of Elizabethkingia . We determined the accuracy of species identification (with two matrix-assisted laser desorption ionization–time of flight mass spectrometry [MALDI-TOF MS] systems and the Vitek 2 GN card), relative prevalence of three Elizabethkingia spp. in clinical specimens, and antimicrobial susceptibility of the species identified by 16S rRNA gene sequencing. Specimens for culture were collected from patients in a university hospital in Seoul, South Korea, between 2009 and 2015. All 3 Elizabethkingia spp. were detected in patients; among the 86 isolates identified by 16S rRNA gene sequencing, 17 (19.8%) were E. meningoseptica , 18 (20.9%) were Elizabethkingia miricola , and 51 (59.3%) were Elizabethkingia anophelis . Only the MALDI-TOF Vitek MS system with an amended database correctly identified all of the isolates. The majority (76.7%) of the isolates were from the lower respiratory tract, and 8 (9.3%) were from blood. Over 90% of E. meningoseptica and E. anophelis isolates were susceptible to piperacillin-tazobactam and rifampin. In contrast, all E. miricola isolates were susceptible to fluoroquinolones except ciprofloxacin. Further studies are urgently needed to determine the optimal antimicrobial agents for the treatment of infections due to each individual Elizabethkingia species.

scholarly journals Comparison of Genotypic and Phylogenetic Relationships of Environmental Enterococcus Isolates by BOX-PCR Typing and 16S rRNA Gene Sequencing

2011 ◽  
Vol 77 (14) ◽  
pp. 5050-5055 ◽  
Author(s):  
Bina S. Nayak ◽  
Brian Badgley ◽  
Valerie J. Harwood
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Content Type ◽  
Rrna Gene Sequencing ◽  
Relationship Of ◽  
Predictive Relationship ◽  
Pcr Genotyping

ABSTRACTEnvironmentalEnterococcusspp. were compared by BOX-PCR genotyping and 16S rRNA gene sequencing to clarify the predictive relationship of BOX-PCR fingerprints to species designation. BOX-PCR and 16S rRNA gene relationships agreed for 77% of strains. BOX-PCR provided superior intraspecies discrimination but incorrectly identified some strains to the species level and divided some species into multiple groups.

scholarly journals Sepsis Due to a Novel Urease-Positive Helicobacter Species in a Young Man

2015 ◽  
Vol 53 (7) ◽  
pp. 2378-2380 ◽  
Author(s):  
Nobuaki Mori ◽  
Narito Kagawa ◽  
Akiko Higuchi ◽  
Yasuko Aoki ◽  
Kiyofumi Ohkusu
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Biochemical Properties ◽  
Rrna Gene ◽  
Short Term ◽  
Content Type ◽  
First Case ◽  
Rrna Gene Sequencing

We report the first case of sepsis with enterocolitis that was caused by a novel urease-positiveHelicobacterspecies in a young man. The isolate was characterized via 16S rRNA gene sequencing and their biochemical properties, and the patient was successfully treated with short-term antimicrobial therapy; no recurrence was observed.

scholarly journals Metagenomic Approach for Identification of the Pathogens Associated with Diarrhea in Stool Specimens

2015 ◽  
Vol 54 (2) ◽  
pp. 368-375 ◽  
Author(s):  
Yanjiao Zhou ◽  
Kristine M. Wylie ◽  
Rana E. El Feghaly ◽  
Kathie A. Mihindukulasuriya ◽  
Alexis Elward ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Resistance Genes ◽  
Control Sample ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Content Type ◽  
Rrna Gene Sequencing

The potential to rapidly capture the entire microbial community structure and/or gene content makes metagenomic sequencing an attractive tool for pathogen identification and the detection of resistance/virulence genes in clinical settings. Here, we assessed the consistency between PCR from a diagnostic laboratory, quantitative PCR (qPCR) from a research laboratory, 16S rRNA gene sequencing, and metagenomic shotgun sequencing (MSS) forClostridium difficileidentification in diarrhea stool samples. Twenty-twoC. difficile-positive diarrhea samples identified by PCR and qPCR and fiveC. difficile-negative diarrhea controls were studied.C. difficilewas detected in 90.9% ofC. difficile-positive samples using 16S rRNA gene sequencing, andC. difficilewas detected in 86.3% ofC. difficile-positive samples using MSS. CFU inferred from qPCR analysis were positively correlated with the relative abundance ofC. difficilefrom 16S rRNA gene sequencing (r2= −0.60) and MSS (r2= −0.55).C. difficilewas codetected withClostridium perfringens, norovirus, sapovirus, parechovirus, and anellovirus in 3.7% to 27.3% of the samples. A high load ofCandidaspp. was found in a symptomatic control sample in which no causative agents for diarrhea were identified in routine clinical testing. Beta-lactamase and tetracycline resistance genes were the most prevalent (25.9%) antibiotic resistance genes in these samples. In summary, the proof-of-concept study demonstrated that next-generation sequencing (NGS) in pathogen detection is moderately correlated with laboratory testing and is advantageous in detecting pathogens withouta prioriknowledge.

scholarly journals Corynebacterium godavarianum Jani et al. 2018 and Corynebacterium hadale Wei et al. 2018 are both later heterotypic synonyms of Corynebacterium gottingense Atasayar et al. 2017, proposal of an emended description of Corynebacterium gottingense Atasayar et al. 2017

2020 ◽  
Vol 70 (5) ◽  
pp. 3534-3540 ◽  
Author(s):  
K. A. Bernard ◽  
A. L. Pacheco ◽  
T. Burdz ◽  
D. Wiebe ◽  
Anne-Marie Bernier
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Type Species ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Content Type ◽  
Link Type ◽  
Emended Description ◽  
Rrna Gene Sequencing

Seven strains of an unidentifiable Corynebacterium species recovered from blood cultures, urine or cerebrospinal fluid over 26 years, closest to but differentiated from Corynebacterium imitans by 16S rRNA gene and partial rpoB gene sequencing, were studied. In November 2017, Atasayar et al. described a blood culture isolate as Corynebacterium gottingense sp. nov., which had >99 % similarity by 16S rRNA gene sequencing to the Canadian strains. In January 2018, Jani et al. described Corynebacterium godavarianum sp. nov., recovered from the Godavari River, India, which also had >99 % similarity by 16S/rpoB sequencing to the Canadian strains and C. gottingense. In May 2018, Wei et al. described Corynebacterium hadale recovered from hadopelagic water; this too had >99 % similarity by 16S rRNA gene sequencing to C. gottingense , C. godavarianum and the Canadian strains. C. gottingense DSM 103494T and C. godavarianum LMG 29598T were acquired and whole genome sequencing was performed (not previously done). Results were compared with genomes from C. hadale (GenBank accession NQMQ01) and the Canadian isolates. We found that these ten genomes formed a single taxon when compared using digital DNA–DNAhybridization, average nucleotide identity using blastn and average amino acid identity criteria but exhibited some subtle biochemical and chemotaxonomic differences. Heuristically, we propose that C. godavarianum and C. hadale are later heterotypic synonyms of, and the Canadian isolates are identifiable as, C. gottingense . We provide an emended description of Corynebacterium gottingense Atasayar et al. 2017; genomes ranged from 2.48 to 2.69 Mb ( C. gottingense DSM 103494T, 2.62 Mb) with G+C content of 65.1–65.6 mol% (WGS), recovered from clinical and environmental sites.

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 µmol 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.

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