Dental biofilm serves as an ecological reservoir of acid-producer pathogens in head and neck cancer patients with radiotherapy-related caries

doi:10.21203/rs.3.rs-4824173/v1

Download PDF

Article

Dental biofilm serves as an ecological reservoir of acid-producer pathogens in head and neck cancer patients with radiotherapy-related caries

https://doi.org/10.21203/rs.3.rs-4824173/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Radiotherapy-related caries (RRC) is an aggressive and debilitating oral toxicity that affects about half of the patients who undergo radiotherapy (RT) for head and neck cancer (HNC). However, the aetiology of RRC is not fully established, and there are no clinically validated methods for preventing it. To gain a better understanding of the risk factors and the microbiome’s role in causing RRC, we compared clinicopathological characteristics, oncological treatment regimens and toxicities, oral health condition, and oral microbiome at three different oral sites of RT-treated HNC patients with (RRC+) and without RRC (RRC-). We observed no significant differences between these groups in the clinicopathological characteristics and treatment regimens. However, RRC + patients were older and had poorer oral health conditions at the start of the RT treatment, with a lower number of teeth and a higher proportion of rehabilitated teeth compared to RCC- patients. In general, RRC + patients had lower microbiome diversity and the dental biofilm of RRC + patients displayed striking alterations in microbiome composition compared to RRC- patients, including enrichment of acidogenic species (such as Propionibacterium acidifaciens and Lactobacillus fermentum) and altered metabolic potential, with a higher abundance of genes from caries-related species (such as Streptococcus mutants and S. parasanguinis) linked to energy-related pathways associated with the synthesis of amino acids and sugars. We also compared RRC tissue with carious tissue from healthy subjects with conventional caries (CC). RRC tissue showed lower bacterial diversity, a higher prevalence of Lactobacillus dominance (relative abundance ≥ 40%), and different co-occurrence networks compared to CC. We provide oral microbiome insights to better understand RRC aetiology, which point to the potential of microbial-targeted therapies to prevent and treat RRC.

Biological sciences/Microbiology/Biofilms

Health sciences/Health care/Dentistry

supportive care

microbiome

radiotherapy

biofilm

dental caries

Head and neck cancer (HNC) has a high worldwide incidence and mortality rate¹. Around 75% of HNC patients undergo radiotherapy (RT) as primary or adjuvant treatment². While advancements like proton therapy show promise in preserving healthy tissues, intensity-modulated therapy remains the gold standard for HNC^1,2. Post-treatment RT toxicities are frequent and can significantly impact the short and long-term patient's quality of life. The most predominant acute oral toxicities are mucositis, opportunistic infections, and neurosensory disorders³. In addition, patients are chronically at risk of experiencing tissue fibrosis, osteoradionecrosis, salivary gland dysfunction, and radiation-related caries (RRC)^4,5.

RRC is a biofilm-dependent pathology that affects 29–57% of HNC patients who underwent RT⁶ and appears within the first 3 months following RT. It is considered harmful to the stomatognathic system due to the fast clinical progression and the limited effectiveness of preventive measures⁴. Unlike conventional caries (CC), RRC occurs in specific areas of the teeth (cervical portion and crown cusp) and often has a painless progression⁷. In later stages, RRC appears as enamel decalcification lines and brownish dentin. The most severe cases result in coronary amputations⁷. In addition to damaging dental tissue, RRC increases the risk of developing osteoradionecrosis in the jaw^8,9.

RRC aetiology still needs to be fully understood. No specific risk factors have been identified, but factors related to traditional tooth decay, such as diet and oral hygiene habits, may also contribute to RRC^9,10. A lower number of teeth before radiotherapy⁹, gingival recession post-radiotherapy¹¹, and poor oral hygiene⁹ have been reported to increase the risk of RRC. Additionally, factors associated with cancer treatment, such as the direct impact of radiation on dental tissues^12–15, and oral microbiome dysbiosis, may also play a role in the development of RRC. Recent studies specifically addressing the effect of RT on the oral microbiome generated conflicting results due to analysis of different oral sites, consideration of distinct oncological treatment regimens, and lack of clinical data to correlate with microbial data^16–21. However, most of these studies report a decrease in oral bacteria diversity after radiotherapy^16–18. Despite this, there are no studies on the oral microbiome of irradiated patients with RRC, and no specific microbial species or signature has been linked to RRC.

RRC is a chronic condition that significantly impacts a person's quality of life and daily routine^22,23. Regrettably, there are currently no clinically validated methods for preventing it and restorative management of RRC can be challenging. However, utilising precise molecular techniques to comprehensively understand the oral microbiome and identify risk factors and specific targets may help develop effective preventive strategies for managing biofilm-related oral pathologies such as RRC^24,25. Here, we compared clinicopathological characteristics, oncological treatment regimens and toxicities, oral health condition, and oral microbiome diversity and composition of the oral mucosa, dental biofilm, and gingival crevicular fluid of RT-treated HNC patients with (RRC+) and without RRC (RRC-). We also compared RRC tissue with carious tissue from healthy subjects with conventional caries (CC). Through an extensive metagenomic characterisation of irradiated dental biofilm and carious tissue, our data provide insights into RRC aetiology that might contribute to the development of microbial-targeted therapies to prevent RRC and restore oral health in HNC survivors.

Study population and clinical features.

We enrolled 33 HNC patients treated with radiotherapy at Hospital Sírio-Libanês, São Paulo, Brazil. These patients were divided into a group that developed (RRC+, n = 17) and another group that did not develop RRC (RRC-, n = 16). We also recruited patients with no previous history of cancer and RT and with conventional caries (CC, n = 16). The groups did not differ in sex or tobacco consumption, but patients in the RRC + group were significantly older, with mean age of 68.5 ± 10.5 years, compared to 54.9 ± 11.5 years for the RRC- group, and 56.8 ± 18.4 years for the CC group (p-value = 0.014, one-way ANOVA, Supplementary Table S1).

Most of the HNC patients had squamous cell carcinoma in the oral cavity and were treated with volumetric modulated arc therapy. There were no significant differences in the clinicopathological characteristics and treatment regimens between the two groups of HNC patients (Table 1) (Supplementary Table S2). Noteworthy, there was no difference in the radiation dose on the major salivary glands and in the incidence of xerostomia between patients with or without RRC (Table 1). However, RRC + patients had poorer oral health conditions at the start of the RT treatment, with a lower number of teeth and a higher proportion of rehabilitated teeth compared to RCC- patients, indicating that poor oral health is a major risk factor for RRC development (Table 1).

Table 1

Radiotherapy data and oral condition status of the study population
	Study Population		p-value, test
	RRC- (n = 16)	RRC+ (n = 17)	p-value, test
Radiotherapy - no. (%)
V-MAT	14 (87.5%)	12 (70.58%)	0.4823, c
3D-Conformal	1 (6.25%)	3 (17.64%)
Step-and-shoot	1 (6.25%)	2 (11.76%)
Organs at risk - in cGy (median, Q1 – Q3)
Ipsilateral Parotid Gland	2550, 2355–3531	2529, 1763–3934	0.6833, f
Contralateral Parotid Gland	1565, 930–2027	2326, 1103, 2529	0.2298, g
Ipsilateral Submandibular Gland	6321, 4596–6754	6088, 5537–6457	0.4559, f
Contralateral Submandibular Gland	3254, 1594–5101	4863, 2621–5833	0.3666, f
Oral cavity	4526, 3334–4891	3745, 1950–5003	0.4613, g
Dental arches	3074, 2318–4243	2759, 1911–3230	0.3245, g
Gross Tumour Volume	6600, 6000–6996	6798, 6450–6996	0.2864, f
Oral Condition - before treatment
Number of Teeth (average, min - max)	26.13, 15–32	17.24, 1–28	0.0009, g
Number of Implants (average, min - max)	0.81, 0–5	1.41, 0–7	0.7937, f
Rehabilitated teeth* (median in %, Q1 – Q3)	39, 22.6–59.7	64.7, 45,5–80.9	0.0272, g
Residual Root* (median in %, Q1 – Q3)	0,0–0	0,0-2.5	0.1026, f
cGy: centi-gray; *Total number of affected teeth/total number of teeth in the oral cavity; Q1: first quartile; Q3: third quartile; Tests: c: Fisher-Freeman-Halton Test; f: Mann-Whitney Test; g: Unpaired T-test.

Microbiome diversity and composition of radiation-related caries.

We performed 16S rRNA microbiome profiling of RRC and carious tissue from patients with no previous history of cancer or radiotherapy and with conventional caries (CC). RRC had lower bacterial diversity (Shannon's index) and lower richness of observed amplicon sequence variants (ASVs) compared to CC (Fig. 1a, Mann-Whitney U test – p-value = 0.009 and p-value = 0.033, respectively). The bacterial composition also differed between RRC and CC (Fig. 1b p-value = 0.001, F = 2.012, PERMANOVA), with RRC tissue displaying a more variable composition as observed by principal coordinate analysis (PCoA). The genus-level microbiome relative abundances of the carious tissue of each patient are shown in Fig. 1c. There were no significant differences in the relative abundance of genera related to dental colonisation between CC and RRC, such as Streptococcus, Actinomyces and Veillonella. However, Lactobacillus dominance (relative abundance ≥ 40%) was significantly more prevalent in RRC compared to CC tissue (44% vs 7%, Fisher’s exact test, p-value = 0.037).

Next, we used a semi-parametric rank-based approach to study microbial association networks in RRC and CC. Co-occurrence network analysis revealed potential differences in bacterial interactions in RRC compared to CC (Supplementary Fig. S1b). In CC, the central nodes of the clusters included well-known dental caries genera, such as Prevotella 7, Veillonella, and Leptotrichia (Supplementary Fig. S1a). In RRC, we also observed the positive interaction Prevotella 7- Lachnospiraceae- Howardella. However, a central node with the periodontopathogen Fusobacterium emerged in RRC, indicating this high-binding capacity bacteria may play an essential role in RRC ^26,27.

Oral microbiome diversity and composition in HNC patients treated with radiotherapy.

We next examined the diversity and composition of the oral microbiome in HNC patients with and without RRC at three distinct sites: oral mucosa (OM), dental biofilm (DB), and gingival crevicular fluid (GCF). Our findings revealed that RRC + patients displayed lower bacterial diversity, as measured by the Shannon’s index and ASV richness, in the DB (Fig. 2b, Shanonn’s index p-value = 0.017 and ASV richness p-value = 0.006, Mann-Whitney U test), and GCF (Fig. 2c, Shanonn’s index p-value = 0.037 and ASV richness p-value = 0.069, Mann-Whitney U test), but not in the OM (Fig. 2a, Shanonn’s index p-value = 0.160 and ASV richness p-value = 0.130, Mann-Whitney U test).

We also found that bacterial composition in RRC + patients differed significantly from RRC- patients at all oral sites (Fig. 2d-f, OM p-value = 0.002, F = 2.149; DB p-value = 0.002, F = 1.767; GCF p-value = 0.001, F = 1.849, PERMANOVA). Interestingly, the presence of RRC in the oral cavity led to increased similarity in the microbiome among the three oral sites, as evidenced by the decreased average intra-subject distance between oral sites in the RRC + group (Fig. 2g, p-value = 0.049, Mann-Whitney U test).

Genera such as Lactobacillus, Bacteroides, Olsenella, Bifidobacterium, and Propionibacterium were enriched in the OM of RRC + patients (Fig. 2h). The DB showed the most notable differences in bacterial composition between the RRC + and RRC- groups, with enrichment of Lactobacillus, Bifidobacterium, and Propionibacterium in RCC + patients (Fig. 2i). In the GCF, no compositional difference was found related to periodontopathogens. However, similarly to the other sites, Lactobacillus was enriched in RRC + patients (Fig. 2j).

As the RRC incidence was previously associated with the irradiation dose in the parotid salivary gland⁷, we used microbiome multivariable association with linear models (MaAsLin2) to associate the median or maximum dose irradiated in the parotid gland with the amplicon-based sequencing data from all investigated oral sites, but no significant associations were found (Supplementary Data S1).

Enrichment of acid-producer species in the dental biofilm of HNC patients with RRC.

Overall, our findings suggest that the most prominent microbial shift occurred in DB, which has the biological potential to physically protect the expansion of pathogens, influencing oral health^28,29. Therefore, we next performed shotgun metagenomic sequencing on DB samples of RRC- and RRC + patients to further characterise the irradiated DB at the species-level and identify potential metabolic pathways associated with RRC's aggressive clinical behaviour.

Metagenomic data confirmed that the dental biofilm of RRC + patients have lower bacterial diversity compared to RRC- patients. This was indicated by a lower Shannon index (p-value = 0.012, Mann-Whitney U test) and a lower species-level genome bin richness (p-value = 0.01, Mann-Whitney U test) (see Supplementary Fig. S2a). Metagenomic data also confirmed the significant compositional differences between RRC + and RRC- groups, as measured by the Bray-Curtis dissimilarity index (Supplementary Fig. S2b, p-value = 0.016, F = 1.77, PERMANOVA).

When visually analysing the species-level relative abundance pattern between RRC + and RRC- groups (Fig. 3a-c), we observed that the RRC + group had a strikingly different profile, mainly due to the predominance of Lactobacillus species in the RRC + group (Fig. 3b). Indeed, L. harbinensis, L. helveicus, L. iners, L. mucosae, L. panis, L. timonensis, and L. ultunensis were only present in RRC + patients, contributing to the higher combined relative abundance of Lactobacillus species observed in patients with RRC (Fig. 3d, p-value = 0.006, Mann-Whitney U test). Additionally, the abundance of Candida species, including C. albicans, C. dubliens and C. tropicalis (Fig. 3c) was overall higher, although not statistically significant, in the RRC + group (Fig. 3e, p-value = 0.12, Mann-Whitney U test).

Metagenomic data also confirmed that acid producing species were more abundant in the DB of the RRC + group (Fig. 3g), including Propionibacterium acidifaciens, which was several-fold more abundant in patients with RRC. Furthermore, several acid-producing species of Lactobacillus were enriched in the RRC + group, including L. gasseri, L. rhamnosus, L. fermentum, L. vaginalis, and L. salivarius. Interestingly, we observed a significant positive correlation between Lactobacillus and Candida species in the DB of RRC + patients (Fig. 3f, p-value = 0.021, ρ = 0.47, Spearman’s rank correlation), suggesting that the production of acid metabolites by Lactobacillus species could promote oral colonisation by Candida species. An enrichment of health-related commensals was observed in the RRC- group, including Actinomyces johnsonii, Neisseria elongata, Cardiobacterium valvarum, Gemella morbillorum, Eikenella corrodens, and Corynebacterium durum (Fig. 3g).

Altered potential of energy-related pathways on commensal bacteria

Finally, we evaluated if the differences in the DB bacterial composition observed between RRC + and RRC- patients affected the metabolic potential of the microbial community. We found an enrichment of genes linked to energy-related pathways associated with the synthesis of amino acids and sugars in the RRC + compared to the RRC- group (Fig. 4a). Although pathways from the abundant P. acidifaciens contributed to this enrichment, it was driven mainly by genes from commensals species present in both groups, such as S. parasanguinis, S. vestibularis, S. mutans, Veillonela parvula, and V. atypica. (Fig. 4a). We also observed lower richness (p-value = 0.015, Mann-Whitney U test) and diversity (p-value = 0.028, Mann-Whitney U test) of metabolic pathways in the RRC + group, which aligns with the lower microbial diversity previously observed in this group (Fig. 4b). Interestingly, the composition of metabolic pathways in the RCC + group were not only different (Fig. 4c, PERMANOVA, p-value = 0.042, F = 1.799), but also more variable compared to the RRC- group (p-value = 0.047, F = 4.011, PERMDISP), which might be metabolic pathway-level evidence of an Anna Karenina effect in the perturbed RRC + ecosystem ³⁰.

RRC significantly impairs the stomatognathic system. Despite its high incidence, the pathophysiology of RRC remains unclear, and preventive guidelines are primarily derived from knowledge of non-irradiated dental caries. This knowledge gap combined with non-targetable risk factors and imprecise guidelines jeopardises the oral health of HNC survivors. This study investigated the microbial differences between patients who develop RRC post-radiotherapy and those who do not by collecting samples from three oral sites of HNC patients exposed to radiotherapy. We also compared demineralised dental tissue from RRC, and CC. Additionally, we performed, for the first time metagenomic sequencing on DB samples from irradiated HNC patients, unveiling species-level insights to better understand RRC aetiology, Finally, we collected extensive clinical data on oral health status, radiotherapy planning, and long-term supportive care follow-up to complement the microbial data.

In line with previous work^8,31, our study found that poor pretreatment oral status was associated with RRC incidence. In our study population, fewer teeth and a higher proportion of rehabilitated teeth before RT were risk factors for RRC development. Other clinical parameters, such as gingival recession and periodontal pocket depth, have been associated with RRC development ^8,31, but were not evaluated in the present work. Our study did not identify a direct relationship between radiation dose and the onset of RRC. Nevertheless, it is widely recognised that radiation can have an adverse effect on tooth structure, and thermal contractions induced by radiation therapy may promote bacterial infiltration. Subsequent studies should explore alterations in the oral microbiome in conjunction with the evaluation of radiation-induced changes in the morphological, mechanical, and chemical properties of permanent teeth.

Similarly, our analysis did not uncover a direct association between the radiation dose to major salivary glands, the incidence of xerostomia, and the development of RRC. This finding aligns with prior research that also found no significant association between salivary flow and RRC incidence^20,31. Similarly, we found no associations between the radiation dose administered to the parotid gland and changes in the oral microbiome among irradiated patients. Salivary gland damage directly impacts the patient's quality of life and deserves proper clinical attention to restore oral health^32–34. Although salivary gland hypofunction has long been hypothesised as the sole cause of RRC, no longitudinal studies have so far quantitatively correlated salivary gland irradiation dose with bacterial diversity decline or shifts in microbial composition in patients with RRC. Some studies have reported stable microbial diversity during radiotherapy^16,17,35, although ulcerated oral mucositis can disrupt this stability^36,37. It is worth noting that xerostomia data in our study was collected retrospectively, and forthcoming studies should consider the assessment of xerostomia at the time of oral sample collection, while also including an analysis of changes in the physicochemical properties of the saliva.

The microbiome profile of RRC tissue revealed lower diversity, with dominance and enrichment of the Lactobacillus genus and Fusobacterium playing a central role in the microbial co-occurrence network. This finding may explain the aggressive nature of RRC compared to conventional caries. Lactobacillus, lacking specific adhesins, thrives in retentive niches like dental cavities, where its high acid production creates an inhospitable environment for competing microbes^38,39. Conversely, Fusobacterium binds to various substrates and microbes, thereby supporting the adhesion of oral colonisers in inhospitable environments. Moreover, the metabolic activity of Fusobacterium is known to be enhanced in polymicrobial communities⁴⁰. Further investigations into the specific roles of these microorganisms in RRC pathogenesis are warranted.

Beyond the distinct microbial profile of RRC tissue, we examined the impact of RRC on other oral microbial communities. Our findings revealed that RRC + patients displayed lower bacterial diversity in the DB and GCF. We also found that bacterial composition in RRC + patients differed significantly from RRC- patients at all oral sites and that the presence of RRC in the oral cavity led to oral dysbiosis and increased similarity in the microbiome among the three oral sites. Despite the decrease in microbial diversity in the GCF of RRC + patients, we did not observe an overgrowth of periodontal pathogens, and further studies should confirm the lack of increased risk of periodontal disease onset post-RT. The DB showed the most significant differences in bacterial composition between the RRC + and RRC- groups, with enrichment of Lactobacillus, and Propionibacterium in RCC + patients documented by both 16S rRNA sequencing and shotgun metagenomic sequencing. These bacteria produce high concentrations of lactic acid, propanoic acid, and acetic acid^41–43. The increased abundance of Lactobacillus in the oral cavity of HNC patients treated with radiotherapy was previously reported in other studies^16,18,20. Interestingly, low salivary pH has been linked to an increased incidence of RRC^20,31, and the observed enrichment in acid-producing bacteria may contribute to low salivary pH, independently of salivary flow, and promote the development of RRC.

Finally, our findings show that the dental biofilm of patients with RCC contains not only differentially abundant species, but also an altered overall metabolic potential of the microbial community. The differences we observed at both the taxonomic and functional levels indicate that the presence of RRC in the oral cavity significantly impacts the dental biofilm, even in teeth that have not yet been affected by carious lesions. By combining taxonomic and microbiome functional potential evidence, we propose that dynamic microbial cooperations are established in the irradiated DB of RRC + patients. Specifically, early colonisers, such as Streptococcus and Veillonella, could exhibit higher expression of energy-related metabolic pathways, promoting the production of pyruvate acid and coenzyme A - necessary for heterolactic and homolactic fermentation⁴⁴. The availability of pyruvate for degradation into propionate, lactate, acetate, and butyrate - produced by Propionibacterium, Lactobacillus, and Bifidobacterium, respectively – might create an acid-inhospitable microenvironment, which limits the growth of non-aciduric species but could support the proliferation of acidophilic bacterial and fungal species in the DB. In this context, the unique presence of Candida species in the RRC + DB warrants further exploration to determine if this is due to opportunistic growth, symbiosis with cariogenic pathogens⁴⁵ without participation in the disease, or active role in RRC pathophysiology and dental colonisation⁴⁶. Our data highlights the complexity of the DB ^{27–29,47,48} and how the DB microbial composition can be influenced by cancer treatment and toxicities⁴⁹.

The cariogenic potential of the oral microbiome in healthy subjects has been well-documented in the scientific literature. However, no studies have been conducted on the oral microbiome of irradiated patients with RRC. This study represents a pioneering effort in providing in-depth, species-level insights aimed at enhancing our understanding of RRC aetiology and facilitating the development of targeted microbial therapies for the prevention and treatment of RRC. In summary, we showed that RRC tissue had lower bacterial diversity, Lactobacillus dominance, and different co-occurrence networks compared to CC. RRC + patients had lower microbiome diversity and the dental biofilm of RRC + patients displayed striking alterations in microbiome composition compared to RRC- patients, including enrichment of acidogenic species and altered metabolic potential.

Developing effective guidelines to prevent RRC requires a comprehensive understanding of DB growth, formation, and microbial metabolites. Our results highlight the critical importance of oral health education and care for individuals with HNC undergoing RT. We advocate for the prescription of fluoride products to promote the formation of pH-resistant fluorapatite. We also underscore the significance of using antimicrobial strategies, such as chlorhexidine or antimicrobial photobiomodulation, on the enamel and dentin before the application of dental materials during the restorative management of RRC. Future longitudinal studies are warranted to assess whether the observed microbial imbalance in RRC patients is a lasting condition or if dental rehabilitation can facilitate the restoration of ecological balance.

Patient recruitment and sample collection

This cross-sectional study was performed at Hospital Sírio-Libanês - São Paulo, Brazil, and approved by the Research Ethics Committee of Hospital Sírio-Libanês (#HSL2018-71). All the patients signed the informed written consent prior to the study and under the Declaration of Helsinki.

The OM, DB, GCF and demineralised dental tissue (RRC and CC) samples were collected simultaneously on the day of the RRC/C diagnosis. Irradiated patients (RRC- and RRC = group) were time-matched in months post-RT with a follow-up median time of 40.5 months (Supplementary Fig. S3).

The samples were collected per site: OM: rubbing the buccal mucosa, alveolar sulcus and tongue dorsum with a sterile swab (Inlab, São Paulo, Brazil); DB: rubbing the vestibular supragingival tooth of the upper and lower arches with the sterile swab (Inlab, São Paulo, Brazil); GFC: absorption with 12 paper points units (F3 Cellpack Protaper) (Dentsply Maillefer, Ballaigues, Switzerland) in the gingival sulcus for 60 seconds (only patients with no presence of active periodontal disease or bleeding on probing); Carious tissue (CC and RRC): the demineralised carious tissue was collected with a sterile Lucas curette instrument.

DNA extraction

For OM, DB, and GCF samples, 600µL of Tris-EDTA buffer (10 mM Tris, 1 mM EDTA, pH 8.0) was added to the Eppendorf to transfer the biological material to the buffer. PureLinkTM RNase A (20 mg/mL, Thermo Fisher Scientific, Waltham, USA) was added (6µL for OM and DB samples, and 8µL for GCF samples), followed by 30µL of Protease 7.5AU (QIAGEN, Hilden, Germany). The DNA was extracted using the QIAamp DNA Blood&Tissue Kit (QIAGEN, Hilden, Germany), according to the manufacturer's protocol.

For carious tissue (C and RRC samples): The pre-processing step consisted of adding 900µL of TES buffer (Tris HCl pH8.0 1 mM + EDTA pH8.0 + SDS 10%) and 50µL of Proteinase K 600 mAU/mL (QIAGEN, Hilden, Germany) for overnight incubation. The processing step consisted of adding 2µL of PureLink™ RNaseA (20 mg/mL, Thermo Fisher Scientific, Waltham, USA) and 1mL of UltraPure™ Buffer-Saturated Phenol (Thermo Fisher Scientific, Waltham, USA) for phase separation. The supernatant was collected after up-and-down movement with (i) phenol: chloroform: isoamyl alcohol (25:24:1), (ii) chloroform: isoamyl alcohol (24:1), and (iii) isopropanol and 3M sodium acetate, respectively. The pellet was cleaned twice with ice-cold 70% ethanol and resuspended in Tris-EDTA buffer (10 mM Tris, 1 mM EDTA, pH8.0).

Clinical data collection and analysis

The computed tomography scans were uploaded in the Eclipse External Beam Planning software (version 15.6, Varian Medical Systems, Inc, Palo Alto, USA) for delineating the primary tumour, organs at risk (parotid salivary glands, submandibular glands, mandibular jaw), dental arches and oral cavity. The planning target volume was created in accordance with the Radiation Therapy Oncology Group (RTOG) reports. Dose calculation was performed with an anisotropic analytical algorithm (AAA_10028), grid resolution of 0.2cm and heterogeneity correction. Oral mucositis was measured during RT, based on the World Health Organization (WHO) grading scale⁵⁰. Xerostomia was measured during sample collection, based on the Common Terminology Criteria for Adverse Events (CTCAE) scale (version 3.0). Oral condition data was collected based on oral examination, panoramic radiograph, or computed tomography scan. The number of teeth was measured by counting natural and rehabilitated functional elements. The number of implants was counted only in rehabilitated implants. Rehabilitated teeth were counted according to previous history of dental procedure (e.g., composite resin). Residual root was accounted by tooth roots remaining above or below the bone crest.

Differences in demographic, dental, and oncological variables were analysed according to the characteristics of numerical and binary variables. Binary variables were analysed through contingency tables using the Chi-square, Fisher's exact, or Fisher-Freeman-Halton tests. Numerical variables were tested for normality with the Shapiro-Wilk test and submitted to the appropriate test. The non-parametric data was analysed by Mann-Whitney U test or Kruskal-Wallis, and the parametric data was analysed by the one-way ANOVA test. The use of each test is indicated in the corresponding table. The p-value was considered significant when < 0.05.

16S rRNA gene amplification and sequencing and data analysis

16S rRNA gene sequencing was performed on 129 samples. Amplification was performed with the KAPA HiFi HotStart PCR Kit (Kapa Biosystem, Cape Town, South Africa) and the use of specific primers for the V3-V4 regions of the 16S rRNA gene (F (5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGG-3') and R (5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTAC-3')). The purification of the PCR products was performed with the AMPure XP PCR Purification Kit (Beckman Coulter, Brea, USA), and the samples were quantified by fluorimetry with the Qubit dsDNA HS Assay (Thermo Fisher Scientific, Waltham, USA). The pool was quantified with the kit NEBNext DNA Library Prep Master Mix Set for Illumina (New England Biolabs, Ipswich, USA), and the protocol of the kit MiSeq Reagent Kit V3 (Illumina, San Diego, USA) was performed to sequence the library on the Illumina MiSeq (Illumina, San Diego, USA).

All samples obtained an average of 150,332 raw reads and 103,642 useful reads (64.8%) were obtained after applying the quality filters. The amplicon-sequencing data was pre-processed in the QIIME2 framework, using the DADA2 pipeline⁵¹ to generate amplicon sequence variants (ASVs). After DADA2, an average 103,642 high-quality reads were obtained. ASVs were taxonomically assigned using the SILVA database and the VSEARCH tool. Finally, the data was normalised by Scaling with Ranked Subsampling^52,53 at 13,385 reads/sample. Alpha diversity⁵⁴ was calculated using the Shannon index⁵⁵ and the observed number of ASVs (as a proxy for richness). The beta diversity was measured by the Bray-Curtis dissimilarity⁵⁶ index and visualised by PCoA. Overall compositional difference between the groups was assessed by the PERMANOVA test. Microbial co-occurrence networks were inferred using the SPRING algorithm⁵⁷ and the R package NetCoMi⁵⁸. Differential abundance between groups was assessed through the ANCOM-BC method^59,60. To evaluate the association between irradiated dose in parotid glands and microbial composition, the R package MaAsLin2⁶¹ was used.

Dental biofilm shotgun metagenomic sequencing and data analysis

Shotgun metagenomic sequencing was performed on the DB samples of the RRC- and RRC + groups (n = 24). For each DB sample, the DNA was re-quantified by fluorimetry with the Qubit dsDNA HS Assay (Thermo Fisher Scientific, Waltham, USA) and diluted to 1 ng. The DNA tagmentation, PCR, and libraries were prepared using the Nextera-XT DNA Library Prep Kit (Illumina, San Diego, USA) following the steps of the manufacturer's protocol. The library clean-up was performed with the AMPure XP PCR Purification (Beckman Coulter, Brea, USA). The libraries were sequenced on the Illumina NovaSeq6000 (Illumina, San Diego, USA).

The shotgun metagenomic sequencing data was pre-processed using kneaddata with default parameters (https://github.com/biobakery/kneaddata). After kneaddata, an average 16.23M high-quality reads were obtained. MetaPhlAn4⁶² was used to profile taxonomic compositions and HUMAnN3 was used to analyse the functional profile by quantification of the metabolic pathways per species with the MetaCyc database⁶³.

COMPETING INTERESTS

The authors declare no competing interests.

Author Contribution

ERF and AAC designed the study. JSB, WMS and ERF recruited patients. JSB and ERF collected oral samples. JSB and ERF analysed patient’s imaging exams. JSB, FR and TMMTA collected oncological and radiotherapy data. JSB, VH, FHK, PA, EMC and LTI processed the samples. VH performed microbiome analyses. JSB and VH performed statistical analysis. AAC acquired funding for the study. All the authors read and approved the final manuscript. All authors are accountable for all aspects of the work.

ACKNOWLEDGEMENTS

JSB was supported by Coordination of Superior Level Staff Improvement (CAPES Foundation). FHK was supported by the State of São Paulo Research Foundation (FAPESP).

Data Availability

The 16S rRNA amplicon sequencing data and the shotgun metagenomic sequencing data are available in NCBI-SRA under the accession code PRJNA1093152.

Chow, L. Q. M. Head and Neck Cancer. New England Journal of Medicine 382, 60–72 (2020).
Grégoire, V., Langendijk, J. A. & Nuyts, S. Advances in Radiotherapy for Head and Neck Cancer. J Clin Oncol 33, 3277–3284 (2015).
Wardill, H. R., Bossi, P. & Sonis, S. T. Understanding Protective Mechanisms of an Oral Probiotic in Reducing Radiation-Induced Oral Mucositis. Journal of Clinical Oncology 42, 1436–1438 (2024).
Kielbassa, A. M., Hinkelbein, W., Hellwig, E. & Meyer-Lückel, H. Radiation-related damage to dentition. Lancet Oncol 7, 326–335 (2006).
Pedroso, C. M. et al. Over 300 Radiation Caries Papers: Reflections From the Rearview Mirror. Frontiers in Oral Health 3, (2022).
Moore, C. et al. Dental caries following radiotherapy for head and neck cancer: A systematic review. Oral Oncology vol. 100 Preprint at https://doi.org/10.1016/j.oraloncology.2019.104484 (2020).
Watson, E., Eason, B., Kreher, M. & Glogauer, M. The DMFS160: A new index for measuring post-radiation caries. Oral Oncol 108, 104823 (2020).
Lalla, R. V et al. Tooth-level predictors of tooth loss and exposed bone after radiation therapy for head and neck cancer. The Journal of the American Dental Association 154, 519–528.e4 (2023).
Brennan, M. T. et al. Tooth Failure Post-Radiotherapy in Head and Neck Cancer: Primary Report of the Clinical Registry of Dental Outcomes in Head and Neck Cancer Patients (OraRad) Study. International Journal of Radiation Oncology*Biology*Physics 113, 320–330 (2022).
Wong, A. C. & Levy, M. New Approaches to Microbiome-Based Therapies. mSystems 4, (2019).
Lalla, R. V et al. Radiation therapy for head and neck cancer leads to gingival recession associated with dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol 133, 539–546 (2022).
Lopes, C. de C. A. et al. Effect of fluoride application during radiotherapy on enamel demineralization. Journal of Applied Oral Science 27, (2019).
Douchy, L. et al. The effect of therapeutic radiation on dental enamel and dentin: A systematic review. Dent Mater 38, e181–e201 (2022).
Parahyba, C. J. et al. Radiation dose distribution in the teeth, maxilla, and mandible of patients with oropharyngeal and nasopharyngeal tumors who were treated with intensity-modulated radiotherapy. Head Neck 38, 1621–1627 (2016).
Cunha, S. R. et al. In vivo and in vitro radiotherapy increased dentin enzymatic activity. J Dent 100, (2020).
Mäkinen, A. I. et al. Salivary microbiome profiles of oral cancer patients analyzed before and after treatment. Microbiome 11, 171 (2023).
Kumpitsch, C., Moissl-Eichinger, C., Pock, J., Thurnher, D. & Wolf, A. Preliminary insights into the impact of primary radiochemotherapy on the salivary microbiome in head and neck squamous cell carcinoma. Sci Rep 10, 16582 (2020).
Mojdami, Z. D. et al. The Effect of Intensity-Modulated Radiotherapy to the Head and Neck Region on the Oral Innate Immune Response and Oral Microbiome: A Prospective Cohort Study of Head and Neck Tumour Patients. Int J Mol Sci 23, (2022).
Reyes-Gibby, C. C. et al. Oral microbiome and onset of oral mucositis in patients with squamous cell carcinoma of the head and neck. Cancer 126, 5124–5136 (2020).
Mougeot, J. L. C. et al. Caries-associated oral microbiome in head and neck cancer radiation patients: a longitudinal study. J Oral Microbiol 11, (2019).
Zhang, J. et al. Investigation of Salivary Function and Oral Microbiota of Radiation Caries-Free People with Nasopharyngeal Carcinoma. PLoS One 10, e0123137- (2015).
Saunders, D. P. et al. Systematic review of antimicrobials, mucosal coating agents, anesthetics, and analgesics for the management of oral mucositis in cancer patients and clinical practice guidelines. Support Care Cancer 28, 2473–2484 (2020).
Elad, S. et al. MASCC/ISOO clinical practice guidelines for the management of mucositis secondary to cancer therapy. Cancer 126, 4423–4431 (2020).
Belibasakis, G. N., Bostanci, N., Marsh, P. D. & Zaura, E. Applications of the oral microbiome in personalized dentistry. Arch Oral Biol 104, 7–12 (2019).
Rosier, B. T., Buetas, E., Moya-Gonzalvez, E. M., Artacho, A. & Mira, A. Nitrate as a potential prebiotic for the oral microbiome. Sci Rep 10, 12895 (2020).
Kolenbrander, P. E. Multispecies communities: interspecies interactions influence growth on saliva as sole nutritional source. Int J Oral Sci 3, 49–54 (2011).
Kolenbrander, P. E., Palmer, R. J., Periasamy, S. & Jakubovics, N. S. Oral multispecies biofilm development and the key role of cell–cell distance. Nat Rev Microbiol 8, 471–480 (2010).
Aurea, S.-S. et al. Polymicrobial Aggregates in Human Saliva Build the Oral Biofilm. mBio 13, e00131-22 (2022).
Baker, J. L., Mark Welch, J. L., Kauffman, K. M., McLean, J. S. & He, X. The oral microbiome: diversity, biogeography and human health. Nat Rev Microbiol 22, 89–104 (2024).
Zaneveld, J. R., McMinds, R. & Vega Thurber, R. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat Microbiol 2, 17121 (2017).
Brennan, M. T. et al. Dental Caries Postradiotherapy in Head and Neck Cancer. JDR Clin Trans Res 8, 234–243 (2022).
Nguyen, C. T., Lin, S., Clark, H., Hovan, A. & Wu, J. Salivary flow rate after conventional and intensity-modulated radiation therapy. The Journal of the American Dental Association 149, 432–441 (2018).
Mercadante, V. et al. Salivary Gland Hypofunction and/or Xerostomia Induced by Nonsurgical Cancer Therapies: ISOO/MASCC/ASCO Guideline. J Clin Oncol 39, 2825–2843 (2021).
Mercadante, V., Al Hamad, A., Lodi, G., Porter, S. & Fedele, S. Interventions for the management of radiotherapy-induced xerostomia and hyposalivation: A systematic review and meta-analysis. Oral Oncology vol. 66 64–74 Preprint at https://doi.org/10.1016/j.oraloncology.2016.12.031 (2017).
Zhu, X.-X. et al. The Potential Effect of Oral Microbiota in the Prediction of Mucositis During Radiotherapy for Nasopharyngeal Carcinoma. EBioMedicine 18, 23–31 (2017).
Hou, J. et al. Distinct shifts in the oral microbiota are associated with the progression and aggravation of mucositis during radiotherapy. Radiotherapy and Oncology 129, 44–51 (2018).
Bruno, J. S. et al. Commensal oral microbiota impacts ulcerative oral mucositis clinical course in allogeneic stem cell transplant recipients. Sci Rep 12, 17527 (2022).
Caufield, P. W., Schön, C. N., Saraithong, P., Li, Y. & Argimón, S. Oral Lactobacilli and Dental Caries: A Model for Niche Adaptation in Humans. J Dent Res 94, 110S-118S (2015).
Santos, H. S. de B. et al. Acid tolerance of Lactobacillus spp. on root carious lesions: A complex and multifaceted response. Arch Oral Biol 156, 105820 (2023).
Akito, S. et al. Fusobacterium nucleatum Metabolically Integrates Commensals and Pathogens in Oral Biofilms. mSystems 7, e00170-22 (2022).
Lan, X. et al. Saliva electrolyte analysis and xerostomia-related quality of life in nasopharyngeal carcinoma patients following intensity-modulated radiation therapy. Radiotherapy and Oncology 150, 97–103 (2020).
Hynne, H. et al. Saliva Metabolomics in Dry Mouth Patients with Head and Neck Cancer or Sjögren’s Syndrome. Cells 11, (2022).
Downes, J. & Wade, W. G. Propionibacterium acidifaciens sp. nov., isolated from the human mouth. Int J Syst Evol Microbiol 59, 2778–2781 (2009).
Barbour, A., Elebyary, O., Fine, N., Oveisi, M. & Glogauer, M. Metabolites of the oral microbiome: important mediators of multikingdom interactions. FEMS Microbiol Rev 46, fuab039 (2022).
Du, Q. et al. Candida albicans promotes tooth decay by inducing oral microbial dysbiosis. ISME J 15, 894–908 (2021).
Sulyanto, R. M. et al. Fungi and bacteria occupy distinct spatial niches within carious dentin. PLoS Pathog 20, e1011865- (2024).
Mark Welch, J. L., Ramírez-Puebla, S. T. & Borisy, G. G. Oral Microbiome Geography: Micron-Scale Habitat and Niche. Cell Host Microbe 28, 160–168 (2020).
Hajishengallis, G., Lamont, R. J. & Koo, H. Oral polymicrobial communities: Assembly, function, and impact on diseases. Cell Host Microbe 31, 528–538 (2023).
Heidrich, V. et al. Dental Biofilm Microbiota Dysbiosis Is Associated With the Risk of Acute Graft-Versus-Host Disease After Allogeneic Hematopoietic Stem Cell Transplantation. Front Immunol 12, (2021).
Villa, A., Vollemans, M., De Moraes, A. & Sonis, S. Concordance of the WHO, RTOG, and CTCAE v4.0 grading scales for the evaluation of oral mucositis associated with chemoradiation therapy for the treatment of oral and oropharyngeal cancers. Supportive Care in Cancer 29, 6061–6068 (2021).
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583 (2016).
Beule, L. & Karlovsky, P. Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ 8, e9593 (2020).
Heidrich, V., Karlovsky, P. & Beule, L. ‘SRS’ R Package and ‘q2-srs’ QIIME 2 Plugin: Normalization of Microbiome Data Using Scaling with Ranked Subsampling (SRS). Applied Sciences 11, (2021).
Whittaker, R. H. Evolution and measurement of species diversity. Taxon 21, 213–251 (1972).
Shannon, C. E. A mathematical theory of communication. The Bell System Technical Journal 27, 379–423 (1948).
Bray, J. R. & Curtis, J. T. An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecol Monogr 27, 325–349 (1957).
Yoon, G., Gaynanova, I. & Müller, C. L. Microbial Networks in SPRING - Semi-parametric Rank-Based Correlation and Partial Correlation Estimation for Quantitative Microbiome Data. Front Genet 10, 516 (2019).
Peschel, S., Müller, C. L., von Mutius, E., Boulesteix, A.-L. & Depner, M. NetCoMi: network construction and comparison for microbiome data in R. Brief Bioinform 22, bbaa290 (2021).
Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 26, (2015).
Lin, H. & Peddada, S. Das. Analysis of compositions of microbiomes with bias correction. Nat Commun 11, 3514 (2020).
Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol 17, 1–27 (2021).
Blanco-Míguez, A. et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat Biotechnol 41, 1633–1644 (2023).
Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods 15, 962–968 (2018).

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Dental biofilm serves as an ecological reservoir of acid-producer pathogens in head and neck cancer patients with radiotherapy-related caries

Status:

Version 1

Abstract

Figures

INTRODUCTION

RESULTS

Altered potential of energy-related pathways on commensal bacteria

DISCUSSION

METHODS

Patient recruitment and sample collection

DNA extraction

Clinical data collection and analysis

Dental biofilm shotgun metagenomic sequencing and data analysis

Declarations

COMPETING INTERESTS

Author Contribution

ACKNOWLEDGEMENTS

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1