Metatranscriptomic analysis shows functional alterations in subgingival biofilm in young smokers with periodontitis: a pilot study

OBJECTIVE
This study aimed to assess the influence of smoking on the subgingival metatranscriptomic profile of young patients affected by stage III/IV and generalized periodontal disease.


METHODOLOGY
In total, six young patients, both smokers and non-smokers (n=3/group), who were affected by periodontitis were chosen. The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for case-control reporting were followed. Periodontal clinical measurements and subgingival biofilm samples were collected. RNA was extracted from the biofilm and sequenced via Illumina HiSeq. Differential expression analysis used Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and differentially expressed genes were identified using the Sleuth package in R, with a statistical cutoff of ≤0.05.


RESULTS
This study found 3351 KEGGs in the subgingival biofilm of both groups. Smoking habits altered the functional behavior of subgingival biofilm, resulting in 304 differentially expressed KEGGs between groups. Moreover, seven pathways were modulated: glycan degradation, galactose metabolism, glycosaminoglycan degradation, oxidative phosphorylation, peptidoglycan biosynthesis, butanoate metabolism, and glycosphingolipid biosynthesis. Smoking also altered antibiotic resistance gene levels in subgingival biofilm by significantly overexpressing genes related to beta-lactamase, permeability, antibiotic efflux pumps, and antibiotic-resistant synthetases.


CONCLUSION
Due to the limitations of a small sample size, our data suggest that smoking may influence the functional behavior of subgingival biofilm, modifying pathways that negatively impact the behavior of subgingival biofilm, which may lead to a more virulent community.


Introduction
Smoking is a widely recognized major risk factor for the progression and severity of periodontal diseases as it is associated with increased probing depth, clinical attachment loss, gingival recession, and a higher likelihood of future tooth loss than in non-smokers.
Additionally, periodontal patients experience worse clinical outcomes after periodontal therapy. 1 Tobacco consumption is widespread among young adults, often beginning in adolescence. 2Consequently, individuals who develop periodontal diseases at a young age may experience a poorer association much earlier in life.
Clinical evidence shows that the negative impact of smoking is even more pronounced at early ages.Studies associating smoking with aggressive periodontitis indicate that these patients show a significant number of non-responsive sites to nonsurgical therapy, with a higher risk of long-term disease recurrence. 3Moreover, even after periodontal treatment, these patients seem to show a faster subgingival recolonization by periodontopathogens than non-smokers, 4 suggesting that smoking may locally modify the subgingival community in parallel with dysfunctional immune responses and dysbiosis.
The microbial community of smoking-associated periodontitis is taxonomically less diverse and distinct than that in non-smokers, and periodontally healthy smokers show a subgingival microbiome composition closely related to that in diseased subjects. 5The expression of virulence factors or of other genes favoring host stimulation could be more impacting than taxonomy since commensal bacteria -rather than only well-known pathogens -can modify community behavior and impact functional microbial content.An in vitro study showed that smoke exposure was associated with transcriptional shifts in biofilm, increasing virulence gene expression and creating an anaerobic, proinflammatory, and pathogen-rich environment. 6us, metatranscriptomics has successfully characterized the functional signatures of the subgingival biofilm from healthy and diseased patients. 7However, the comparison between distinct diseased environments and the effect of heavy smoking habits on those environments is yet to be performed.
Therefore, this study aims to investigate whether the additional influence of smoking habits alters the gene expression profile of the subgingival biofilm in individuals suffering from generalized periodontal disease.

Study design
The influence of smoking on the subgingival transcriptome of young subjects with generalized grade C periodontitis was evaluated in this crosssectional study (formerly known as "generalized aggressive periodontitis").This research was approved by the local ethics committee (088325/2017).Written informed consent was obtained from all participants.

Study Population
Non-smokers (PerioCNsmk) and smokers the number of samples in previous preliminary studies employing RNAseq analysis. 9,10In total, six patients were included in this study, 3 in each group.The

STROBE (Strengthening the Reporting of Observational
Studies in Epidemiology) guidelines for case-control reporting were adopted in this study. 11e following were considered as exclusion criteria: (1) periapical or pulp alterations; (2) systemic alteration or use of medications that may influence response to periodontal treatment (such as antibiotics and anti-inflammatories) six months before this study; (3) pregnant and lactating women; (4) periodontal treatment (including subgingival instrumentation) in the six months preceding this study; (5)
A calibrated standard probe (UNC-15, Hu-Friedy, Chicago, IL, USA) and the same calibrated clinician (RVCS) performed the measurements.Intra-class correlation showed 91% reproducibility for CAL and 94% for PD.Calibration was conducted on three Grade C periodontitis subjects who had no involvement with this study.Each subject underwent two examinations in two sessions with a 24-hour interval between them.

Biofilm collection and RNA extraction
Supragingival plaque was first removed, and the areas were thoroughly dried and isolated with cotton rolls.In total, six interproximal sites with the deepest periodontal probing depth (PD ≥ 5 mm) were chosen.
Bifurcation areas and third molars were excluded.Subgingival biofilm samples were collected using periodontal curettes (Hu-Friedy, Chicago, IL, USA).ncbi.nlm.nih.gov/bioproject/PRJNA757462.Quality control was analyzed by the FastQC software. 15immomatic v0.39 16 was used to clear reads with a quality lower than Phred 20 (Q20).We extracted microorganism sequences based on two steps.In the first step, the reads aligned to the human genome (hg38) were eliminated by mapping with bowtie2. 17 the second step, based on these filtered data, SortMeRNA 18 was run to filter the ribosomal RNA from the metatranscriptomic data.The reference microbiome was generated based on the Human Oral Microbiome Database (HOMD) 19 and the de novo assembly of the unmapped reads in the HOMD.The cleaned reads were aligned in HOMD using bowtie2, and the de novo sequences from unaligned reads were assembled using Trinity v2.8.3. 20Data quantification at the gene and transcript level was performed on Kallisto v0.49 21.Differentially expressed genes were identified using the Sleuth v0.30.0 package, 22 considering a q-value cutoff <= 0.05.The functional orthologs, called the KO (KEGG Orthology) group, rather than a single gene or protein, were identified by the KEGG database 23 to find experimental evidence in a specific organism that can be extended to other organisms in the microbiome.KO analysis used the complete list of identifiers in the enrichment pathway analysis to reconstruct the predicted pathways using the FMAP pipeline. 24Additionally, the sequences obtained after alignment with HOMD and de novo assembly step were screened and matched for transcripts related to antibiotic resistance, using a comprehensive antibiotic resistance database as reference. 25 showing a similar degree of periodontal destruction between subjects.Moreover, this study found no differences in probing depth between collection sites (p>0.05).

Differentially expressed genes for smokers
Figure 1 shows the distribution of the 3351 KEGGs in the subgingival biofilm of both groups (Supplementary Table 1).Differential metatranscriptomic analysis found 304 differentially expressed KEGGs between both groups (PerioCSmk versus PerioCNsmk), 112 overexpressed and 192 underexpressed in the smoker's group (Supplementary Table        puzzle.This study assessed the influence of smoking on the behavior of a dysbiotic subgingival biofilm by a metatranscriptomic approach (whole mRNA sequencing).To our knowledge, this is the first study to investigate the effect of smoking on the functional signature of the microbial community at diseased sites, using the same disease scenario as a control in patients with generalized periodontitis.We found that patients in the smoking group showed significant differences in relation to non-smokers regarding subgingival community gene expression.The main disparities were related to the expression of orthologs associated with metabolism, bacterial-host interactions, and virulence in the subgingival biofilm.These results suggest how a risk factor may distinctly and notably influence disease pathogenesis by altering gene expression in biofilm.
The usefulness of metatranscriptomic data of smoking influence is valuable even with three samples in each group as we used paired-end reads to increase sequencing depth, and a fairly small number of replicates can reach a robust power in data analysis. 7,26e results of each sample between groups showed that smoking habits alter the regulation of some pathways, directly influencing subgingival environment dynamics.Smokers' biofilm was associated with a downregulation of galactose metabolism.The negative regulation of aerobic carbohydrate metabolism genes has been associated with smoke exposure and the establishment of a pathogenic community with increased expression of virulence genes. 6Additionally, the alteration in regulatory genes of oxidative phosphorylation in the subgingival environment may favor oxidative stress and the establishment of the anaerobic and reactive oxygen species in the microbial community. 6,24The influence of smoking in oral microbiota by oxygen tension alteration and a higher proportion of anaerobic species has offered a striking point associated with smoking in periodontally healthy subjects, 5,28 pointing out similar mechanisms in dysbiotic environments.Conversely, a study using a predictive tool to determine the metagenomic content found the enrichment of genes related to galactose metabolism and depletion of oxidative phosphorylation pathways in smoker adults affected by periodontitis. 29Despite predictive limitations, the authors found an influence of smoking on oral oxygen metabolism, explaining some changes in taxonomic and transcriptome results.
Research has also observed the reduced expression of genes related to fumarate reductase (an enzyme that belongs to anaerobic respiration), which can influence the redox interconversion of fumarate and succinate, altering bacterial development in microaerophilic environments 30 and modifying the butanoate metabolism.Studies have described butyrate as the main product of gut microbial fermentation, which may exert immunomodulatory effects on intestinal macrophages and maintain intestinal epithelial cells. 31 subjects with periodontitis, smoking altering the butanoate chain could explain the more virulent aggressive periodontitis group. 41Thus, smoking also impairs genes related to these functions and may thus negatively impact periodontal treatment outcomes.
In summary, this study examined the additional impact of smoking on young patients with generalized periodontitis and found the overexpression of microbial genes related to host immunomodulation and modifications in metabolic pathways that could influence disease progression.Despite its limited sample size, this study significantly evaluated the number of genes and KEGG pathways.This information is valuable to elaborate hypotheses, validate targeted genes in larger populations, and explore the interplay between these pathways, smoking, and community virulence.This study also emphasizes the importance of studies comparing two disease scenarios to evaluate how biofilm behavior changes, particularly considering the challenges of clinically assessing the direct effects of smoking habits.

(
PerioCSmk) were chosen according to the following inclusion criteria: (1) diagnosis of generalized periodontitis Grade C Stage 3-4 (PerioC); 8 (2) age below 35 years at the moment of diagnosis and systemic health evaluation; (3) presence of at least 15 teeth; (4) presence of at least six teeth with deep sites (≥7 mm) in areas other than bifurcations.Patients were considered smokers if they had consumed more than 10 cigarettes a day for at least five years, whereas non-smokers were those without a history of smoking.Sample size was determined considering After a single collection in each site, samples from the same patient were pooled together and immediately stored in Eppendorf tubes containing 100μl of RNA storage reagent (RNAlater™ Stabilization Solution, Thermo Fisher Scientific, MA, USA).Total RNA was extracted by a specific kit (RNAeasy ® Mini Kit extraction, Valencia, CA, USA) after the following extraction buffers were added: Lysozyme 20 mg/ml (Thermo Fisher Scientific, MA, USA) + Mutanolysin 5,000U / ml (Thermo Fisher Scientific, MA, USA) + Tris-1M HCl, pH 8.0.The extracted RNA was stored in a freezer at −80 °C until RNA sequencing.Metatranscriptome sequencing RNA was sequenced via the Illumina HiSeq 2,500 platform.RNA-seq libraries were prepared using a specific kit (TruSeq™ RNA Sample Prep Kit v2, Illumina, Inc.; San Diego CA, USA) according to the manufacturer's instructions.The adjusted libraries were sequenced in the same lane with 2×100-bp paired-end reads on the Illumina HiSeq sequencer.The RNA-seq data obtained in this study are available in the Sequence Read Archive repository, https://www.
Means and standard deviations were calculated for the clinical parameters.For clinical measurements, data were initially tested for normal distribution by the Shapiro-Wilk test.Numeric demographic parameters with normal distribution were analyzed by the nonpaired t-test, and categorical frequency data were analyzed by the chi-squared test.All tests were performed with a 5% significance level on SIGMAplot (Systat Software Inc., United States).

2
). KEGGs with positive fold change values indicate overexpression in smokers, whereas negative values, overexpression in non-smokers or underexpression in smokers.Tables 2 and 3 describe the 10 overexpressed KEGGs with the highest fold change values in each group.Smoking modulated KEGGs related to bacterial metabolism and upregulated genes mainly related to peptidases, inhibitors, and cellular processes.Evaluating the bacterial taxonomy associated with those genes showed that both groups possess genes regulated by members of the red complex of gramnegative pathogens.However, an increase in some KEGGs exclusive of Gram-positive species (e.g., Actinomyces genera and Streptococcus gordonii species) occurred in the 10 overexpressed KEGGs in PerioCSmk, such as C5a peptidase (K08652), polycystin 1 (K04985), ribonuclease D (K03684), and ABC transport system ATP-binding/permease protein (K21397).Pathways regulated for smokers Supplementary

Figure 1 -Supplementary
Figure 1-Heatmap analysis of bacterial KEGG orthologues, identified in the metatranscriptomic analysis of subgingival biofilm of the PerioCSmk and PerioCNSmk groups.According to their sequence similarities, the KEGGs were grouped into three different clusters dehydrogenases and hydratases, which can act as oxidoreductases.Oxidative phosphorylation is the most prominent pathway chain regulated by smoking.KEGGs related to NADH dehydrogenases and NADHquinone oxidoreductases from diverse subunits were upregulated, except for three KEGGs associated with the subunit D, which were downregulated.Smokers showed some alterations in the hydrophilic domain of the mitochondrial matrix membrane and hydron translocation in smokers.Antibiotic resistance genes (ARGs) Smoking altered the levels of antibiotic resistance genes (ARGs) in subgingival biofilm.Using a comprehensive antibiotic resistance database, six genes showed overexpression in PerioCSmk, such as general bacterial porin (which reduces permeability to beta-lactams), the resistance-nodulation-cell division antibiotic efflux pump, and the antibiotic-resistant isoleucyl-tRNA synthetase, most of which are related to Prevotella sp. and Tannerella sp (Supplementary complex relation between hosts' response and biofilm, together with the functional profile of the biofilm, play a role in disease development.Moreover, several risk factors could contribute to altering each piece of this

Figure 2 -
Figure 2-Heatmap analysis of bacterial pathways found by the values (TPM) of each KEGG in patients' subgingival biofilm.A) Representation of the glycosaminoglycan degradation, glycosphingolipid biosynthesis, other glycan degradation, and peptidoglycan biosynthesis pathways.B) Representation of the galactose metabolism pathway.C) Representation of the butanoate metabolism and oxidative phosphorylation pathways.

J Appl Oral Sci. 2024;32:e20240031 3/10
Metatranscriptomic analysis shows functional alterations in subgingival biofilm in young smokers with periodontitis: a pilot study

Table 1
summarizes the demographic and clinical characteristics of PerioC smokers and non-smokers.Participants' age and gender showed no significant

Table 3
shows the total contribution of KEGGs on several pathways in both groups, whereas No differences in parameters occurred between PerioCSmk and PerioCNSmk.(Student's t-test, considering p<0.05).PI: Plaque index; BoP: bleeding on probing index; PD: probing depth; CAL: clinical attachment level.

Table 1 -
Demographic and clinical data for the PerioCSmk and PerioCNSmk groups.
Metatranscriptomic analysis shows functional alterations in subgingival biofilm in young smokers with periodontitis: a pilot study J Appl Oral Sci.2024;32:e20240031 7/10