Dataset Information

Subgingival microbiota of dogs with healthy gingiva or early periodontal disease from different geographical locations.

ABSTRACT:

Background

Periodontal disease is the most common oral disease of dogs worldwide and results from a complex interplay between plaque bacteria, the host and environmental factors. Recent studies have enhanced our understanding of the associations between the plaque microbiota and canine periodontal disease. These studies, however, were limited in their geographical reach. Thus associations between the canine oral microbiota and geographical location were investigated by determining the composition of subgingival plaque samples from 587 dogs residing in the United Kingdom (UK), United States of America (USA), China and Thailand using 454-pyrosequencing.

Results

After quality filtering 6,944,757 sequence reads were obtained and clustering of these at ?98% sequence resulted in 280 operational taxonomic units (OTUs) following exclusion of rare OTUs (present at ConclusionsThe bacterial associations with health, gingivitis and periodontitis were conserved across dogs from the UK, USA, China and Thailand. These bacterial signatures of periodontal health and disease have potential as biomarkers for disease detection.

SUBMITTER: Wallis C

PROVIDER: S-EPMC7789547 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Subgingival microbiota of dogs with healthy gingiva or early periodontal disease from different geographical locations.

Wallis C C Milella L L Colyer A A O'Flynn C C Harris S S Holcombe L J LJ

BMC veterinary research 20210106 1

<h4>Background</h4>Periodontal disease is the most common oral disease of dogs worldwide and results from a complex interplay between plaque bacteria, the host and environmental factors. Recent studies have enhanced our understanding of the associations between the plaque microbiota and canine periodontal disease. These studies, however, were limited in their geographical reach. Thus associations between the canine oral microbiota and geographical location were investigated by determining the co ...[more]

PMID: 33407419

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Project description:Background: Although human gingival fibroblasts (hGF) and human periodontal ligament fibroblasts (hPDLF) exhibit numerous phenotypic similarities, it has been suggested that the secretory and behavioral differences, which exist between these cell types, are a result of the membrane protein composition of these cells. Methods: Four matched pairs of hGF and hPDLF were cultured. Prior to confluence, membrane bound and associated proteins from cells of the 4th passage were extracted. The processed protein samples were identified by digestion with trypsin and sequenced using capillary-liquid chromatography tandem mass spectrometry on an Thermo Scientific LTQ-Orbitrap XL mass spectrometer. Scaffold by Proteome Software was used to quantitate and validate protein identifications derived from MS/MS sequencing results. Results: Four hundred fifty proteins were common to both hGF and hPDLF. Of the proteins identified, 214 were known membrane bound or associated proteins and 165 proteins were known nuclear associated proteins. Twenty-seven proteins, identified from the 450 proteins, common to both hGF and hPDLF, were detected in statistically significant greater quantities in either hGF or hPDLF. More specifically, 13 proteins were detected in significantly greater quantities in hGF, while 14 proteins were detected in significantly greater quantities in hPDLF. Conclusions: Distinct differences in the cellular protein catalog may reflect the dynamic role and high energy requirements of hGF in extracellular matrix remodeling and response to inflammatory challenge as well as the role of hPDGF in monitoring mechanical stress and maintaining tissue homeostasis during regeneration and remineralization. Method Details: Sequence information from the MS/MS data was processed by converting the .raw files into a merged file (.mgf) using an in-house program, RAW2MZXML_n_MGF_batch (merge.pl, a Perl script). Isotope distributions for the precursor ions of the MS/MS spectra were deconvoluted to obtain the charge states and monoisotopic m/z values of the precursor ions during the data conversion. The resulting mgf files were searched using Mascot Daemon by Matrix Science version 2.3.2 (Boston, MA) and the database searched against the full SwissProt database version 2012_06 (536,489 sequences; 190,389,898 residues) or NCBI database version 20120515 (18,099,548 sequences; 6,208,559,787 residues The mass accuracy of the precursor ions were set to 20ppm, accidental pick of 13C peaks was also included into the search. The fragment mass tolerance was set to 0.5 Da. Considered variable modifications were oxidation (Met), deamidation (N and Q) and carbamidomethylation (Cys). Four missed cleavages for the enzyme were permitted. A decoy database was also searched to determine the false discovery rate (FDR) and peptides were filtered according to the FDR. The significance threshold was set at p less than 0.05 and bold red peptides is required for valid peptide identification. Proteins with a Mascot score of 50 or higher with a minimum of two unique peptides from one protein having a -b or -y ion sequence tag of five residues or better were accepted. Any modifications or low score peptide/protein identifications were manually checked for validation. Spectral Counting: Label Free Quantitation was performed using the spectral counting approach. Scaffold (version Scaffold_3.4.9, Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm (Keller, A et al Anal. Chem. 2002;74(20):5383-92). Protein identifications were accepted if they could be established at greater than 95.0% probability and contained at least 1 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii, AI Anal Chem. 2003 Sep 1;75(17):4646-58). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony.

Dataset Information

Subgingival microbiota of dogs with healthy gingiva or early periodontal disease from different geographical locations.

Background

Results

Publications

Subgingival microbiota of dogs with healthy gingiva or early periodontal disease from different geographical locations.

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