Search

ABSTRACT Aim Material and Methods Results Conclusions To discover new salivary biomarkers to diagnose periodontitis and evaluate the impact of age and smoking on predictive capacity.Saliva samples were collected from 44 healthy periodontal individuals and 41 with periodontitis. Samples were analysed by sequential window acquisition of all theoretical mass spectra (SWATH‐MS), and proteins were identified by employing the UniProt database. The diagnostic capacity of the molecules was determined with generalized additive models. The models obtained were single‐protein unadjusted and adjusted for age and smoking status, besides two‐protein combinations.Eight single salivary proteins had a bias‐corrected accuracy (bc‐ACC) of 78.8%–86.8% (bc‐sensitivity/bc‐specificity of 62.5%–86.9%/60.9%–98.1%) to diagnose periodontitis. Predictive capacity increased more by adjusting for age (bc‐ACC: 94.1%–98.2%; bc‐sensitivity/bc‐specificity: 90.2%–98.6%/93.6%–97.2%) than smoking (bc‐ACC: 83.9%–90.4%; bc‐sensitivity/bc‐specificity: 73.6%–89.9%/76.2%–96.4%). These proteins were keratin, type II cytoskeletal 1, protein S100‐A8, β‐2‐microglobulin, neutrophil defensin 1, lysozyme C, ubiquitin‐60S ribosomal protein L40, isoform 2 of tropomyosin α‐3 chain and resistin. Two dual combinations showed bc‐sensitivity/bc‐specificity of > 90%: β‐2‐microglobulin with profilin‐1, and lysozyme C with zymogen granule protein 16 homologue B.New salivary biomarkers show good or excellent ability to diagnose periodontitis. Age has a more significant influence on the accuracy of the single biomarkers than smoking, with results comparable to two‐protein combinations. [ABSTRACT FROM AUTHOR]

Aim: To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH‐MS). Materials and Methods: GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III–IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH‐MS, and proteins were identified by the UniProt human‐specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions. Results: In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias‐corrected (bc)‐sensitivity/bc‐specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP‐9, MMP‐8, zinc‐α‐2‐glycoprotein and neutrophil gelatinase‐associated lipocalin and ZG16B with cornulin provided increased bc‐sensitivity/bc‐specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions. Conclusions: New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings. [ABSTRACT FROM AUTHOR]

Background: Few investigations evaluated smoking's impact on the periodontal proteome. Therefore, this study aimed to analyse the influence of tobacco on the overall periodontal proteome and the differential expression of gingival crevicular fluid (GCF) proteins using sequential window acquisition of all theoretical mass spectra (SWATH-MS)., Methods: GCF samples were collected from 40 periodontitis subjects (stages III-IV). These were separated based on smoking status into smokers (17), ex-smokers (10), and non-smokers (13). Samples were analysed using SWATH-MS, and proteins were identified using the UniProt human-specific database. Data are available via ProteomeXchange with the identifier PXD043474. Principal component analysis (PCA) was employed to examine the spectral mass distribution of the proteome. Protein expression was different for a p-value <0.05 and a log2 fold change ≥0.3 (upregulated) or ≤-0.3 (downregulated)., Results: The distribution of overall proteome did not differ between non-smokers, smokers, and ex-smokers. Considering protein expression, 23 were differentially expressed in smokers vs. non-smokers (16 upregulated and 7 downregulated), 17 in ex-smokers vs. non-smokers (2 upregulated and 15 downregulated), and only 8 in smokers vs. ex-smokers (7 upregulated and 1 downregulated). Smoking increased the expression of proteins related to epithelial hyperkeratinization (keratins type II cytoskeletal 4, type I cytoskeletal 13 and type I cytoskeletal 19, cornulin, and fatty acid-binding protein 5). However, multiple immunoglobulins were underexpressed when comparing smokers and ex-smokers to non-smokers., Conclusion: Although smoking does not significantly modify the overall GCF proteome associated with periodontitis, it alters the expression of several proteins compared to never-smokers and ex-smokers., Plain Language Summary: Smoking is a critical risk factor for the development and progression of periodontitis. However, evidence of the effect of smoking on the subgingival proteome is scarce. Therefore, this study aimed to determine the impact of smoking on the overall proteome and differential expression of gingival crevicular fluid (GCF) proteins using the sequential window acquisition of all theoretical mass spectra (SWATH-MS) proteomic technique. For this purpose, GCF samples were collected from 40 subjects with periodontitis, of which 17 were smokers, 10 were ex-smokers, and 13 were non-smokers. These samples were analysed by SWATH-MS, and proteins were identified using the UniProt human-specific database. Analysis of the overall proteome showed that its distribution was not significantly different between smokers, ex-smokers, and non-smokers. However, several proteins were found to be differentially expressed according to the smoking status. Smoking can increase the expression of several keratins and proteins related to hyperkeratinization of the epithelium. However, in ex-smokers, these proteins return to similar levels to those of non-smokers. Moreover, smoking may induce a lower expression of proteins related to adaptive immunity, such as immunoglobulins. This immunosuppressive effect may persist in ex-smokers., (© 2024 The Author(s). Journal of Periodontology published by Wiley Periodicals LLC on behalf of American Academy of Periodontology.)

Introduction: Microbiome-based clinical applications that improve diagnosis related to oral health are of great interest to precision dentistry. Predictive studies on the salivary microbiome are scarce and of low methodological quality (low sample sizes, lack of biological heterogeneity, and absence of a validation process). None of them evaluates the impact of confounding factors as batch effects (BEs). This is the first 16S multi-batch study to analyze the salivary microbiome at the amplicon sequence variant (ASV) level in terms of differential abundance and machine learning models. This is done in periodontally healthy and periodontitis patients before and after removing BEs., Methods: Saliva was collected from 124 patients (50 healthy, 74 periodontitis) in our setting. Sequencing of the V3-V4 16S rRNA gene region was performed in Illumina MiSeq. In parallel, searches were conducted on four databases to identify previous Illumina V3-V4 sequencing studies on the salivary microbiome. Investigations that met predefined criteria were included in the analysis, and the own and external sequences were processed using the same bioinformatics protocol. The statistical analysis was performed in the R-Bioconductor environment., Results: The elimination of BEs reduced the number of ASVs with differential abundance between the groups by approximately one-third (Before=265; After=190). Before removing BEs, the model constructed using all study samples (796) comprised 16 ASVs (0.16%) and had an area under the curve (AUC) of 0.944, sensitivity of 90.73%, and specificity of 87.16%. The model built using two-thirds of the specimens (training=531) comprised 35 ASVs (0.36%) and had an AUC of 0.955, sensitivity of 86.54%, and specificity of 90.06% after being validated in the remaining one-third (test=265). After removing BEs, the models required more ASVs (all samples=200-2.03%; training=100-1.01%) to obtain slightly lower AUC (all=0.935; test=0.947), lower sensitivity (all=81.79%; test=78.85%), and similar specificity (all=91.51%; test=90.68%)., Conclusions: The removal of BEs controls false positive ASVs in the differential abundance analysis. However, their elimination implies a significantly larger number of predictor taxa to achieve optimal performance, creating less robust classifiers. As all the provided models can accurately discriminate health from periodontitis, implying good/excellent sensitivities/specificities, the salivary microbiome demonstrates potential clinical applicability as a precision diagnostic tool for periodontitis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Regueira-Iglesias, Suárez-Rodríguez, Blanco-Pintos, Relvas, Alonso-Sampedro, Balsa-Castro and Tomás.)

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them., (© 2023 The Authors. Molecular Oral Microbiology published by John Wiley & Sons Ltd.)

Background: Sequencing has been widely used to study the composition of the oral microbiome present in various health conditions. The extent of the coverage of the 16S rRNA gene primers employed for this purpose has not, however, been evaluated in silico using oral-specific databases. This paper analyses these primers using two databases containing 16S rRNA sequences from bacteria and archaea found in the human mouth and describes some of the best primers for each domain., Results: A total of 369 distinct individual primers were identified from sequencing studies of the oral microbiome and other ecosystems. These were evaluated against a database reported in the literature of 16S rRNA sequences obtained from oral bacteria, which was modified by our group, and a self-created oral archaea database. Both databases contained the genomic variants detected for each included species. Primers were evaluated at the variant and species levels, and those with a species coverage (SC) ≥75.00% were selected for the pair analyses. All possible combinations of the forward and reverse primers were identified, with the resulting 4638 primer pairs also evaluated using the two databases. The best bacteria-specific pairs targeted the 3-4, 4-7, and 3-7 16S rRNA gene regions, with SC levels of 98.83-97.14%; meanwhile, the optimum archaea-specific primer pairs amplified regions 5-6, 3-6, and 3-6, with SC estimates of 95.88%. Finally, the best pairs for detecting both domains targeted regions 4-5, 3-5, and 5-9, and produced SC values of 95.71-94.54% and 99.48-96.91% for bacteria and archaea, respectively., Conclusions: Given the three amplicon length categories (100-300, 301-600, and >600 base pairs), the primer pairs with the best coverage values for detecting oral bacteria were as follows: KP&#95;F048-OP&#95;R043 (region 3-4; primer pair position for Escherichia coli J01859.1: 342-529), KP&#95;F051-OP&#95;R030 (4-7; 514-1079), and KP&#95;F048-OP&#95;R030 (3-7; 342-1079). For detecting oral archaea, these were as follows: OP&#95;F066-KP&#95;R013 (5-6; 784-undefined), KP&#95;F020-KP&#95;R013 (3-6; 518-undefined), and OP&#95;F114-KP&#95;R013 (3-6; 340-undefined). Lastly, for detecting both domains jointly they were KP&#95;F020-KP&#95;R032 (4-5; 518-801), OP&#95;F114-KP&#95;R031 (3-5; 340-801), and OP&#95;F066-OP&#95;R121 (5-9; 784-1405). The primer pairs with the best coverage identified herein are not among those described most widely in the oral microbiome literature. Video Abstract., (© 2023. The Author(s).)

This study aimed to evaluate the number of 16S rRNA genes in the complete genomes of the bacterial and archaeal species inhabiting the human mouth and to assess how the use of different primer pairs would affect the detection and classification of redundant amplicons and matching amplicons (MAs) from different taxa. A total of 518 oral-bacterial and 191 oral-archaeal complete genomes were downloaded from the NCBI database, and their complete 16S rRNA genes were extracted. The numbers of genes and variants per genome were calculated. Next, 39 primer pairs were used to search for matches in the genomes and obtain amplicons. For each primer, we calculated the number of gene amplicons, variants, genomes, and species detected and the percentage of coverage at the species level with no MAs (SC-NMA). The results showed that 94.09% of oral bacteria and 52.59% of oral archaea had more than one intragenomic 16S rRNA gene. From 1.29% to 46.70% of bacterial species and from 4.65% to 38.89% of archaea detected by the primers had MAs. The best primers were the following (SC-NMA; region; position for Escherichia coli [GenBank version no. J01859.1]): KP&#95;F048-OP&#95;R030 for bacteria (93.55%; V3 to V7; 342 to 1079), KP&#95;F018-KP&#95;R063 for archaea (89.63%; V3 to V9; undefined to 1506), and OP&#95;F114-OP&#95;R121 for both domains (92.52%; V3 to V9; 340 to 1405). In addition to 16S rRNA gene redundancy, the presence of MAs must be controlled to ensure an accurate interpretation of microbial diversity data. The SC-NMA is a more useful parameter than the conventional coverage percentage for selecting the best primer pairs. The pairs used the most in the oral microbiome literature were not among the best performers. IMPORTANCE Hundreds of publications have studied the oral microbiome through 16S rRNA gene sequencing. However, none have assessed the number of 16S rRNA genes in the genomes of oral microbes, or how the use of primer pairs targeting different regions affects the detection of MAs from different taxa. Here, we found that almost all oral bacteria and more than half of oral archaea have more than one intragenomic 16S rRNA gene. The performance of the primer pairs in not detecting MAs increases as the length of the amplicon augments. As none of those most employed in the oral literature were among the best performers, we selected a series of primers to detect bacteria and/or archaea based on their percentage of species detected without MAs. The intragenomic 16S rRNA gene redundancy and the presence of MAs between distinct taxa need to be considered to ensure an accurate interpretation of microbial diversity data.

Although clustering by operational taxonomic units (OTUs) is widely used in the oral microbial literature, no research has specifically evaluated the extent of the limitations of this sequence clustering-based method in the oral microbiome. Consequently, our objectives were to: 1) evaluate in-silico the coverage of a set of previously selected primer pairs to detect oral species having 16S rRNA sequence segments with ≥97% similarity; 2) describe oral species with highly similar sequence segments and determine whether they belong to distinct genera or other higher taxonomic ranks. Thirty-nine primer pairs were employed to obtain the in-silico amplicons from the complete genomes of 186 bacterial and 135 archaeal species. Each fasta file for the same primer pair was inserted as subject and query in BLASTN for obtaining the similarity percentage between amplicons belonging to different oral species. Amplicons with 100% alignment coverage of the query sequences and with an amplicon similarity value ≥97% (ASI97) were selected. For each primer, the species coverage with no ASI97 (SC-NASI97) was calculated. Based on the SC-NASI97 parameter, the best primer pairs were OP&#95;F053-KP&#95;R020 for bacteria (region V1-V3; primer pair position for Escherichia coli J01859.1: 9-356); KP&#95;F018-KP&#95;R002 for archaea (V4; undefined-532); and OP&#95;F114-KP&#95;R031 for both (V3-V5; 340-801). Around 80% of the oral-bacteria and oral-archaea species analyzed had an ASI97 with at least one other species. These very similar species play different roles in the oral microbiota and belong to bacterial genera such as Campylobacter , Rothia , Streptococcus and Tannerella , and archaeal genera such as Halovivax , Methanosarcina and Methanosalsum . Moreover, ~20% and ~30% of these two-by-two similarity relationships were established between species from different bacterial and archaeal genera, respectively. Even taxa from distinct families, orders, and classes could be grouped in the same possible OTU. Consequently, regardless of the primer pair used, sequence clustering with a 97% similarity provides an inaccurate description of oral-bacterial and oral-archaeal species, which can greatly affect microbial diversity parameters. As a result, OTU clustering conditions the credibility of associations between some oral species and certain health and disease conditions. This significantly limits the comparability of the microbial diversity findings reported in oral microbiome literature., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Regueira-Iglesias, Vázquez-González, Balsa-Castro, Blanco-Pintos, Martín-Biedma, Arce, Carreira and Tomás.)

Periodontitis is one of the world's most common chronic human diseases and has a significant impact on oral health. Recent evidence has revealed a link between periodontitis and certain severe systemic conditions. Moreover, periodontal patients remain so for life, even following successful therapy, requiring ongoing supportive care to prevent the disease's recurrence. The first challenge in treating the condition is ensuring a timely and accurate diagnosis since the loss of periodontal bone and soft tissue is progressive and largely irreversible. Although current clinical and radiographic parameters are the best available for identifying and monitoring the disease, the scientific community has a particular interest in finding quantifiable biomarkers in oral fluids that can improve early detection rates of periodontitis and evaluations of its severity. It is widely accepted that periodontitis is associated with polymicrobial dysbiosis and a chronic inflammatory immune response in the host. This response causes the generation of mediators like cytokines. Higher concentrations of cytokines are involved in inflammation and disease progression, acting as a network of biological redundancy. Most of the cytokines investigated concerning the periodontitis pathogenesis are proinflammatory. Of all of them, interleukin (IL) 1beta has been studied the most, followed by tumor necrosis factor (TNF) alpha and IL6. In contrast, only a few papers have evaluated antiinflammatory cytokines, with the most researched being IL4 and IL10. Several systemic reviews have concluded that the specific cytokines present in patients with periodontitis have a distinctive profile, which may indicate their possible discriminatory potential. In this chapter, the focus is on analyzing studies that investigate the accuracy of diagnoses of periodontitis based on the cytokines present in gingival crevicular fluid and saliva. The findings of our research group are also described., (© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Aim: To obtain salivary interleukin (IL) 1β-based models to predict the probability of the occurrence of periodontitis, differentiating by smoking habit., Materials/methods: A total of 141 participants were recruited, 62 periodontally healthy controls and 79 subjects affected by periodontitis. Fifty of the diseased patients were given non-surgical periodontal treatment and showed significant clinical improvement in 2 months. IL1β was measured in the salivary samples using the Luminex instrument. Binary logistic regression models were obtained to differentiate untreated periodontitis from periodontal health (first modelling) and untreated periodontitis from treated periodontitis (second modelling), distinguishing between non-smokers and smokers. The area under the curve (AUC) and classification measures were calculated., Results: In the first modelling, IL1β presented AUC values of 0.830 for non-smokers and 0.689 for smokers (accuracy = 77.6% and 70.7%, respectively). In the second, the predictive models revealed AUC values of 0.671 for non-smokers and 0.708 for smokers (accuracy = 70.0% and 75.0%, respectively)., Conclusion: Salivary IL1β has an excellent diagnostic capability when it comes to distinguishing systemically healthy patients with untreated periodontitis from those who are periodontally healthy, although this discriminatory potential is reduced in smokers. The diagnostic capacity of salivary IL1β remains acceptable for differentiating between untreated and treated periodontitis., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)