scholarly journals Evaluation of the Colorimetric VITEK 2 Card for Identification of Gram-Negative Nonfermentative Rods: Comparison to 16S rRNA Gene Sequencing

2007 ◽  
Vol 45 (7) ◽  
pp. 2270-2273 ◽  
Author(s):  
A. Zbinden ◽  
E. C. Bottger ◽  
P. P. Bosshard ◽  
R. Zbinden
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Gram Negative ◽  
Vitek 2 ◽  
Rrna Gene Sequencing

Comparison of VITEK 2, MALDI-TOF MS, 16S rRNA gene sequencing, and whole-genome sequencing for identification of Roseomonas mucosa

2019 ◽  
Vol 134 ◽  
pp. 103576 ◽  
Author(s):  
Wolfram W. Rudolph ◽  
Florian Gunzer ◽  
Melanie Trauth ◽  
Boyke Bunk ◽  
Richard Bigge ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Whole Genome ◽  
Vitek 2 ◽  
Rrna Gene Sequencing ◽  
Roseomonas Mucosa ◽  
Tof Ms

scholarly journals Fastidious Gram-Negatives: Identification by the Vitek 2 Neisseria-Haemophilus Card and by Partial 16S rRNA Gene Sequencing Analysis

2010 ◽  
Vol 4 (1) ◽  
pp. 123-131 ◽  
Author(s):  
Jens JØrgen Christensen ◽  
Brita Bruun ◽  
Ute Wolff Sönksen ◽  
Lisbeth Nielsen ◽  
Annemarie Hesselbjerg ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Sequencing Analysis ◽  
Vitek 2 ◽  
Rrna Gene Sequencing

scholarly journals Comparison of the Vitek 2, API 20A, and 16s rRNA Gene Sequencing for the Identification of Anaerobic Bacteria

2015 ◽  
Vol 18 (1) ◽  
pp. 20 ◽  
Author(s):  
Gyun Cheol Park ◽  
Sook Jin Jang ◽  
Min Jung Lee ◽  
Joong-Ki Kook ◽  
Min Jung Kim ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Anaerobic Bacteria ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Vitek 2 ◽  
Rrna Gene Sequencing

scholarly journals In silico analysis of 16S rRNA gene sequencing based methods for identification of medically important aerobic Gram-negative bacteria

2011 ◽  
Vol 60 (9) ◽  
pp. 1281-1286 ◽  
Author(s):  
Jade L. L. Teng ◽  
Ming-Yiu Yeung ◽  
Geoffrey Yue ◽  
Rex K. H. Au-Yeung ◽  
Eugene Y. H. Yeung ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
In Silico Analysis ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Rrna Gene Sequencing ◽  
Silico Analysis

scholarly journals Optimizing microbiome sequencing for small intestinal aspirates: validation of novel techniques through the REIMAGINE study

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Gabriela Guimaraes Sousa Leite ◽  
Walter Morales ◽  
Stacy Weitsman ◽  
Shreya Celly ◽  
Gonzalo Parodi ◽  
...  
Keyword(s):  
Small Intestine ◽  
Small Bowel ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Macconkey Agar ◽  
Gram Negative ◽  
Rrna Gene Sequencing

Abstract Background The human small intestine plays a central role in the processes of digestion and nutrient absorption. However, characterizations of the human gut microbiome have largely relied on stool samples, and the associated methodologies are ill-suited for the viscosity and low microbial biomass of small intestine samples. As part of the REIMAGINE study to examine the specific roles of the small bowel microbiome in human health and disease, this study aimed to develop and validate methodologies to optimize microbial analysis of the small intestine. Results Subjects undergoing esophagogastroduodenoscopy without colon preparation for standard of care were prospectively recruited, and ~ 2 ml samples of luminal fluid were obtained from the duodenum using a custom sterile aspiration catheter. Samples of duodenal aspirates were either untreated (DA-U, N = 127) or pretreated with dithiothreitol (DA-DTT, N = 101), then cultured on MacConkey agar for quantitation of aerobic gram-negative bacteria, typically from the class Gammaproteobacteria, and on blood agar for quantitation of anaerobic microorganisms. DA-DTT exhibited 2.86-fold greater anaerobic bacterial counts compared to DA-U (P = 0.0101), but were not statistically different on MacConkey agar. DNA isolation from DA-U (N = 112) and DA-DTT (N = 43) samples and library preparation for 16S rRNA gene sequencing were also performed using modified protocols. DA-DTT samples exhibited 3.81-fold higher DNA concentrations (P = 0.0014) and 4.18-fold higher 16S library concentrations (P < 0.0001) then DA-U samples. 16S rRNA gene sequencing revealed increases in the detected relative abundances of obligate and facultative anaerobes in DA-DTT samples, including increases in the genera Clostridium (false discovery rate (FDR) P = 4.38E-6), Enterococcus (FDR P = 2.57E-8), Fusobacterium (FDR P = 0.02) and Bacteroides (FDR P = 5.43E-9). Detected levels of Gram-negative enteropathogens from the phylum Proteobacteria, such as Klebsiella (FDR P = 2.73E-6) and Providencia (FDR P < 0.0001) (family Enterobacteriaceae) and Pseudomonas (family Pseudomonadaceae) (FDR P = 0.04), were also increased in DA-DTT samples. Conclusions This study validates novel DTT-based methodology which optimizes microbial culture and 16S rRNA gene sequencing for the study of the small bowel microbiome. The microbial analyses indicate increased isolation of facultative and obligate anaerobes from the mucus layer using these novel techniques.

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