The mycobiome of the oral cavity in healthy dogs and dogs with periodontal disease

Author(s):  
Brook A. Niemiec ◽  
Jerzy Gawor ◽  
Shuiquan Tang ◽  
Aishani Prem ◽  
Janina A. Krumbeck

Abstract OBJECTIVE To investigate the mycobiome of the oral cavity in healthy dogs and dogs with various stages of periodontal disease. ANIMALS 51 dogs without periodontal disease (n = 12) or with mild (10), moderate (19), or severe (10) periodontal disease. PROCEDURES The whole maxillary arcade of each dog was sampled with a sterile swab, and swabs were submitted for next-generation DNA sequencing targeting the internal transcribed spacer 2 region with a commercial sequencing platform. RESULTS Fungi were detected in all samples, with a total of 320 fungal species from 135 families detected in the data set. No single fungal species was found in all samples. The 3 most frequently found fungal species were Cladosporium sp (46/51 samples), Malassezia restricta (44/51 samples), and Malassezia arunalokei (36/51 samples). Certain fungi, specifically those of the family Didymellaceae, the family Irpicaceae, and the order Pleosporales, were significantly associated with different stages of periodontitis. Mycobial analysis indicated that Cladosporium sp could be considered part of the core oral cavity mycobiome. CONCLUSIONS AND CLINICAL RELEVANCE Results highlighted that fungi are present in the oral cavity of dogs and are characterized by substantial species diversity, with different fungal communities associated with various stages of periodontal disease. The next-generation DNA sequencing used in the present study revealed substantially more species of fungi than previous culture-based studies.

ChemBioChem ◽  
2016 ◽  
Vol 17 (17) ◽  
pp. 1628-1635 ◽  
Author(s):  
Nina Svensen ◽  
Olve B. Peersen ◽  
Samie R. Jaffrey

2008 ◽  
Vol 26 (10) ◽  
pp. 1135-1145 ◽  
Author(s):  
Jay Shendure ◽  
Hanlee Ji

Author(s):  
Brook A. Niemiec ◽  
Jerzy Gawor ◽  
Shuiquan Tang ◽  
Aishani Prem ◽  
Janina A. Krumbeck

Abstract OBJECTIVE To compare the bacteriome of the oral cavity in healthy dogs and dogs with various stages of periodontal disease. ANIMALS Dogs without periodontal disease (n = 12) or with mild (10), moderate (19), or severe (10) periodontal disease. PROCEDURES The maxillary arcade of each dog was sampled with a sterile swab, and swabs were submitted for next-generation DNA sequencing targeting the V1–V3 region of the 16S rRNA gene. RESULTS 714 bacterial species from 177 families were identified. The 3 most frequently found bacterial species were Actinomyces sp (48/51 samples), Porphyromonas cangingivalis (47/51 samples), and a Campylobacter sp (48/51 samples). The most abundant species were P cangingivalis, Porphyromonas gulae, and an undefined Porphyromonas sp. Porphyromonas cangingivalis and Campylobacter sp were part of the core microbiome shared among the 4 groups, and P gulae, which was significantly enriched in dogs with severe periodontal disease, was part of the core microbiome shared between all groups except dogs without periodontal disease. Christensenellaceae sp, Bacteroidales sp, Family XIII sp, Methanobrevibacter oralis, Peptostreptococcus canis, and Tannerella sp formed a unique core microbiome in dogs with severe periodontal disease. CONCLUSIONS AND CLINICAL RELEVANCE Results highlighted that in dogs, potential pathogens can be common members of the oral cavity bacteriome in the absence of disease, and changes in the relative abundance of certain members of the bacteriome can be associated with severity of periodontal disease. Future studies may aim to determine whether these changes are the cause or result of periodontal disease or the host immune response.


2018 ◽  
Vol 32 (6) ◽  
pp. 429-444 ◽  
Author(s):  
Claire L. Hoban ◽  
Ian F. Musgrave ◽  
Megan L. Coghlan ◽  
Matthew W. P. Power ◽  
Roger W. Byard ◽  
...  

2015 ◽  
Vol 76 ◽  
pp. 63
Author(s):  
Stephanie Conklin ◽  
Bing Yang ◽  
Nate Baird ◽  
Brad Baas ◽  
Ali Crawford ◽  
...  

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1419 ◽  
Author(s):  
Jose E. Kroll ◽  
Jihoon Kim ◽  
Lucila Ohno-Machado ◽  
Sandro J. de Souza

Motivation.Alternative splicing events (ASEs) are prevalent in the transcriptome of eukaryotic species and are known to influence many biological phenomena. The identification and quantification of these events are crucial for a better understanding of biological processes. Next-generation DNA sequencing technologies have allowed deep characterization of transcriptomes and made it possible to address these issues. ASEs analysis, however, represents a challenging task especially when many different samples need to be compared. Some popular tools for the analysis of ASEs are known to report thousands of events without annotations and/or graphical representations. A new tool for the identification and visualization of ASEs is here described, which can be used by biologists without a solid bioinformatics background.Results.A software suite namedSplicing Expresswas created to perform ASEs analysis from transcriptome sequencing data derived from next-generation DNA sequencing platforms. Its major goal is to serve the needs of biomedical researchers who do not have bioinformatics skills.Splicing Expressperforms automatic annotation of transcriptome data (GTF files) using gene coordinates available from the UCSC genome browser and allows the analysis of data from all available species. The identification of ASEs is done by a known algorithm previously implemented in another tool namedSplooce. As a final result,Splicing Expresscreates a set of HTML files composed of graphics and tables designed to describe the expression profile of ASEs among all analyzed samples. By using RNA-Seq data from the Illumina Human Body Map and the Rat Body Map, we show thatSplicing Expressis able to perform all tasks in a straightforward way, identifying well-known specific events.Availability and Implementation.Splicing Expressis written in Perl and is suitable to run only in UNIX-like systems. More details can be found at:http://www.bioinformatics-brazil.org/splicingexpress.


Sign in / Sign up

Export Citation Format

Share Document