Genomic Signatures and Functional Pathways Underlying 5-Fluorouracil Resistance in Head and Neck Cancer associated Streptococci

Genomic Signatures and Functional Pathways Underlying 5-Fluorouracil Resistance in Head and Neck Cancer associated Streptococci | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genomic Signatures and Functional Pathways Underlying 5-Fluorouracil Resistance in Head and Neck Cancer associated Streptococci Linh Mai, George Bouras, Kenny Yeo, Emma Barry, Bhavya Kulathunga, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8825984/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The chemotherapeutic agent 5-fluorouracil (5-FU), widely used in the treatment of head and neck cancer (HNC), also exhibits broad antimicrobial activity, yet fluoropyrimidine resistance within HNC-associated microbiota remains poorly characterised. We assessed 5-FU susceptibility and resistance-associated genomic features in 101 Streptococcus isolates obtained from tumour tissue and oral swabs of 31 HNC patients using minimum inhibitory concentration assays integrated with whole-genome sequencing and pangenome analysis. Resistance to 5-FU was prevalent across multiple Streptococcus species and was primarily associated with species identity rather than resistance phenotype or anatomical niche. Resistant isolates showed functional convergence in pathways related to multidrug efflux, stress response, DNA repair, cell-envelope biosynthesis, and virulence, whereas sensitive isolates were enriched for genes involved in core metabolism, nutrient acquisition, and colonisation. Species-resolved analyses revealed heterogeneous, polygenic resistance architectures rather than conserved resistance determinants. Together, these findings suggest that 5-FU exposure may act as an ecological selective pressure shaping microbial functional potential within tumour- and oral communities in HNC. Biological sciences/Microbiology/Microbial genetics/Bacterial genetics Biological sciences/Cancer/Head and neck cancer/Oral cancer Biological sciences/Microbiology/Clinical microbiology 5-Fluorouracil Streptococcus Head and Neck Cancer Oral microbiome Cancer microbiome Chemoresistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Among chemotherapeutic agents, 5-fluorouracil (5-FU) is widely used because it inhibits thymidylate synthase and disrupts DNA synthesis. However, resistance to 5-FU remains a major barrier to effective treatment for many cancer types 1 . Interestingly, 5-FU has broad-spectrum antimicrobial activity and has been proposed as a scaffold for the development of next-generation antibiotics 2 . This dual functionality suggests that, in addition to tumour inhibition, 5-FU may substantially alter the microbiome of treated patients, with potential and underappreciated consequences for patient health and disease progression. Bacteria have evolved multiple strategies to survive fluoropyrimidines, such as 5-FU through modifications in uracil permease-mediated transport and rewiring of pyrimidine salvage pathways. In Gram-positive bacteria, efflux pumps belonging to the Major Facilitator Superfamily (MFS) reduce intracellular drug accumulation, while in Gram-negative species, Resistance-Nodulation-Division (RND) family transporters perform similar roles 3 , 4 . Additional resistance mechanisms arise through mutations in genes like upp (uracil phosphoribosyltransferase), tdk (thymidine kinase), and thyA (thymidylate synthase), which disrupt fluoropyrimidine metabolism or drug-target interactions 2 , 5 , 6 . Beyond genetic adaptations, resistance can be mediated by physiological responses including SOS-driven DNA repair, reduced uptake via altered transporters, and biofilm-mediated protection 7 . Notably, recent work on colorectal cancer (CRC) associated gut bacteria harbouring the preTA operon has shown that these microbes enzymatically degrade 5-FU by converting it into the inactive metabolite dihydrofluorouracil (DHFU). This metabolic inactivation not only promotes bacterial survival but can also reduce chemotherapy efficacy and alter toxicity profiles 8 , 9 . While most cancer-microbiome research on 5-FU has focused on its effects on the gut microbiome in CRC 8 , 9 , far less is known about how tumour-associated microbiota relevant to head and neck cancer (HNC) respond to fluoropyrimidines. In HNC, 5-FU is administered systemically 9 , which likely leads to drug exposure in both tumour- and oral cavity-resident bacteria. Furthermore, abnormal enrichment of oral cavity-resident bacterial taxa in the gut is associated with several disease states including CRC 10 . Oral-derived taxa such as Fusobacterium , Peptostreptococcus , and Parvimonas are highly enriched in the gut microbiome of CRC patients 11 . In HNC, multiple sequencing-based studies have demonstrated that oral commensal bacteria are detectable within tumour tissues, indicating that oral-derived microbes may also play an important role in HNC pathology 12 . However, the functional consequences of this microbial presence, especially in how they respond to and potentially modulate 5-FU remains poorly understood. Among oral cavity and tumour-infiltrating bacteria in HNC, the Streptococcus genus is among the most dominant 13 – 16 . These include common oral commensals such as S. mitis , S. parasanguinis , and S. salivarius , which can become opportunistic pathogens in the context of mucosal damage or immunosuppression 17 . Streptococci have been implicated in immune modulation, TME remodelling, and chemotherapy responsiveness 18 – 22 . The probiotic S. salivarius K12 strain has also been shown to alleviate radiation-induced mucositis in HNC patients further underscoring its clinical relevance 23 . However, despite their prevalence and potential impact in HNC pathobiology, 5-FU resistance and its underlying mechanisms in Streptococci is currently unknown. Thus, it is critical to understand how prevalent 5-FU resistance is within Streptococci . In this study, we systematically investigated the 5-FU resistance profiles of Streptococcus isolates from the oral cavities and tumour tissues of HNC patients. We utilised minimum inhibitory concentration (MIC) assays to categorise our isolates as resistant or sensitive and subsequently utilised paired whole-genome sequencing analysis to identify the genetic associations underlying resistance. This study provides the first comprehensive insight into 5-FU resistance among tumour-associated Streptococcus and establishes a foundation for understanding how fluoropyrimidines may interact with microbiomes relevant to HNC. Results 5-FU resistance is observed across Streptococcus lineages independent of anatomical site. We tested 101 unique Streptococcus strains collected from 31 HNC patients, comprising 17 tumour-derived and 84 oral-mucosa–derived isolates 24 for 5-FU susceptibility. MIC assays revealed a broad range, from highly sensitive (≤ 8 µg/mL) to highly resistant (> 1,000 µg/mL) ( Supplementary Data S2 ). The isolate collection was taxonomically diverse and dominated by members of the S. mitis group with S. mitis, S. parasanguinis , and S. oralis being the most frequently represented species. Notably, high-level 5-FU resistance was observed across multiple species, including S. salivarius, S. parasanguinis and S. oralis . The MIC distributions of tumour- and oral-derived isolates were comparable, both with median values of 64 µg/mL (Wilcoxon test comparing tumour vs oral isolates, p = 0.38; Fig. 1 A). An alluvial plot linking species, sampling origin (oral cavity vs tumour tissue), and resistance category showed that resistant isolates were present at similar proportions in both oral and tumour sites (46/84 vs 10/17; Fisher’s exact p = 0.80), indicating no evidence for enrichment of 5-FU resistance among tumour-derived isolates ( Fig. S1 A ). For downstream genomic association analyses, resistant isolates were defined by retained viability at 64 µg/mL 5-FU, a threshold previously applied to gut bacteria 37 . Using this definition, resistant isolates showed markedly higher viability than sensitive isolates at this concentration across all species and anatomical sites (Wilcoxon p <1x10 − 16 ; Supplementary Fig. S1 B ), confirming robust phenotype separation. In contrast, no significant differences in viability or MIC distributions were observed between tumour- and oral-derived associated isolates (Fig. 1 A and Fig. 1 B). There was wide resistance heterogeneity among HNC-associated isolates, with S. salivarius isolates consistently exhibiting high resistance (Fig. 1 C). Together, these findings indicate that 5-FU resistance in Streptococcus is not preferentially enriched in tumour sites and is highly heterogeneous. Global pangenome variation and resistance-associated genes. Next, we evaluated how bacterial 5-FU resistance status and anatomical origin affects genome diversity. Principal coordinates analysis (PcoA) of Jaccard distances computed from gene presence–absence data (retaining genes present in 5–95% of isolates to reduce noise from rare genes) revealed broad genomic variation across isolates. The first two principal coordinates (PCoA1 and PCoA2), which represent the main dimensions of genomic dissimilarity, together explained 55.3% of the total variance (PCoA1 = 32.8%, PcoA2 = 22.5%, respectively; Fig. 2 A). Across the global dataset, resistant and sensitive isolates were widely intermingled, and oral cavity and tumour-derived isolates showed extensive overlap, indicating that neither resistance status nor anatomical origin drives major shifts in overall gene content (Fig. 2 A). Faceting the PCoA by anatomical site similarly failed to reveal site-specific clustering of resistant isolates ( Supplementary Fig. S2A ). To formally quantify drivers of genomic variation, we performed permutational multivariate analysis of variance (PERMANOVA) on the same distance matrix. PERMANOVA confirmed that species identity explained the vast majority of variation (R² = 0.925, p < 0.0001), whereas resistance and location contributed negligibly (R² = 0.00088, p = 0.312 and R² = 0.0014, p = 0.110, respectively ( Supplementary Data S3 ). Species-resolved PERMANOVA summaries further illustrated that, across individual taxa, resistance and location explained only modest proportions of accessory genome variation relative to species identity ( Supplementary Fig. S2B ) A test for homogeneity of multivariate dispersion (PERMDISP/betadisper) further supported the validity of these results, showing no significant differences in dispersion between resistance groups (F = 1.57, p = 0.206) or anatomical sites (F = 0.33, p = 0.564). In contrast, dispersion differed significantly between species (F = 9.15, p < 0.0001), consistent with strong species-level genomic heterogeneity ( Supplementary Fig. S3A-C ). These effects are summarised visually in Fig. 2 B, which shows PERMANOVA R² values for each factor, highlighting the dominant contribution of species identity relative to resistance and site. To investigate whether resistance-associated structure might be masked at the genus level, we performed species-specific PCoA analyses for all species represented by ≥ 6 isolates and both resistance phenotypes (n = 7 species). Species-specific PERMANOVA summary results displays the proportion of variance explained (R²) by resistance status and sampling location within each species. Across taxa, resistance effects were modest and heterogeneous (typically R² ≈ 0.10–0.20), whereas location effects were consistently weak or negligible ( Supplementary Fig. S4; Supplementary Data S3 ). Consistent with these quantitative results, species-specific PCoA plots revealed variable patterns of resistance-associated separation, with partial segregation observed in S. mitis but substantial overlap between resistant and sensitive isolates in S. parasanguinis , S. oralis , and S. anginosus ( Supplementary Fig. S4 ). Importantly, no species exhibited consistent segregation by anatomical site, indicating that tumour versus oral origin does not impose a dominant constraint on accessory genome composition. Gene-level associations identify shared functional features linked to resistance Despite the absence of clear resistance- or site-associated clustering at the level of the full Streptococcus pangenome, we identified specific accessory genes whose presence was associated with 5-FU resistance status. The pangenome-wide Fisher’s exact screen of accessory gene presence–absence detected 122 loci which demonstrated statistically significant relationships (unadjusted p < 0.05) between resistant and sensitive isolates, comprising 26 genes enriched in resistant isolates and 96 enriched in sensitive isolates (Fig. 3 ; Supplementary Data S4 ). Although none of these associations remained significant after false discovery rate (FDR) correction, the uncorrected signals were analysed in an exploratory, hypothesis-generating framework. At the cohort level, resistance-enriched loci were disproportionately associated with five major functional themes including: (1) multidrug efflux ( lmrB and ybhR ), (2) DNA repair ( sbcDI ), (3) stress response and dormancy( stbD ), (4) capsule and envelope biosynthesis ( cpsE, wcaJ, epsL and wbbJ ), and (5) metabolic redox balance ( pdxS ) 41 – 49 . In contrast, loci enriched among sensitive isolates were predominantly annotated as housekeeping or core metabolic functions, consistent with baseline growth. To further determine whether these global patterns masked species-specific resistance mechanisms, we repeated these association analyses within individual taxa. Species-level analyses were restricted to taxa represented by ≥ 5 isolates and both resistance phenotypes, resulting in evaluable dataset for S. parasanguinis (n = 9) and S. mitis (n = 15) met the minimum threshold (p 0.2) for within-species analysis ( Supplementary Fig. S4) . The PCoA analysis of S. mitis accessory genes content revealed partial separation of resistant and sensitive isolates ( Supplementary Fig. S4E ). The patterns were supported by PERMANOVA which identified resistance status as a significant contributor to gene-content variation ( p 0.05; Supplementary Data S4) . Correspondingly, resistance-associated loci in S. mitis ( n = 15 ) were enriched for genes involved in extracellular polysaccharide and capsule biosynthesis (multiple glycosyltransferases including EpsE and dTDP-sugar biosynthesis enzymes), surface anchoring and envelope-associated proteins (LPXTG-anchored and choline-binding proteins), regulatory systems (the blpS–lytR–lytT bacteriocin-associated regulatory locus and XRE-family transcriptional regulators), and stress-associated DNA repair (DNA alkylation repair enzymes) ( Supplementary Data S4 ). Notably, the blpS–lyrR–lytT operon, which produce a bacteriocin and associated lysis-regulatory system, were detected 4/7 resistant S. mitis isolates (approximately 60%) but was absent in all sensitive isolates (n = 0/8) ( Supplementary Fig. S5A ). This operon was not detected in S. parasanguinis isolates, indicating species-specific enrichment. These findings implicate interbacterial competition, quorum sensing and envelope-associated functions may contribute to differential tolerance to 5-FU 50 . In contrast, sensitive S. mitis isolates were enriched for efeO , a component of ferrous-iron uptake systems, which was predominantly detected in oral cavity isolates (> 50%) and markedly depleted in resistant tumour-associated isolates, suggesting a greater reliance on nutrient acquisition rather than stress-resilience or competitive traits 51 . In S. parasanguinis (n = 9 ) , resistance-associated patterns differed markedly. Sensitive isolates showed enrichment of hsdM , encoding a DNA methyltransferase component of restriction–modification systems, as well as lysR -family transcriptional regulators, fctA (fimbrial adhesin), and several conserved but unknown function loci ( aF2118 ) ( Supplementary Fig. S5B; Supplementary Data S4 ) 52 – 54 . Notably, hsdM was detected in all sensitive isolates (n = 3/3) but was absent from all resistant isolates (n = 0/6), consistent with a phenotype favouring colonisation and genome defence rather than chemotherapeutic stress tolerance. Resistant S. parasanguinis isolates, in contrast, were enriched for multiple uncharacterised accessory genes which encode predicted proteins of unknown function, some of which contain domains consistent with membrane association or regulatory activity based on annotation databases ( Supplementary Data S4 ). To assess whether resistance status corresponded to discrete segregation in ordination space, we tested for non-random distributions of resistant and sensitive isolates across the primary ordination axis (PCoA1) within each species. No species showed a significant association after correction for multiple testing (all adjusted p = 1; Supplementary Data S4 ), indicating that resistance-associated differences in accessory gene content reflect subtle, continuous shifts rather than categorical clustering. Together, our species-resolved analyses show that Streptococcus does not have a single genetic factor that makes it resistant to 5-FU. Instead, different accessory genome configurations lead to the development of multiple functional resistance strategies. Differential representation of antimicrobial resistance, efflux systems, and virulence-associated genes. Following our unbiased gene-level screen, loci associated with resistance were assessed for functional enrichment. Genes were classified based on homology to CARD, MFS, and VFDB to define antimicrobial resistance, efflux/transport, and virulence-associated categories, respectively. Enrichment analysis (Fisher’s exact test with p < 0.05) was used to compare the distribution of these gene categories between resistant and sensitive isolates (Fig. 4 ; Supplementary Fig. S6; and Supplementary Data S5 ). CARD-based resistance annotation revealed multiple antimicrobial resistance determinants associated with 5-FU resistance (Fig. 4 A; Supplementary Fig. S6A ). Resistant bacterial isolates contained a broader repertoire of antibiotic resistance gene families which included erm(32), ErmS, VatI, abcA, norB, norC, mdeA, catB variants, vanS (within vanF -associated clusters) and multiple mprF homologues ( Bsub_mprF, Cper_mprF, Lmon_mprF , and Saga_mprF ). The genes in this group produce efflux pumps, acetyltransferases, regulatory kinases and membrane charge–modifying enzymes which decrease drug levels inside cells and modify cell wall structure to provide protection against various stresses 55 – 65 . Efflux and metal/stress exporters were also highly enriched in resistant isolates (Fig. 4 B; Supplementary Fig. S6B). These strains contained fieF together with multiple unclassified MFS/RND transporter clusters ( group_17153, group_19822, group_19968, group_21568 ) , several of which correspond to CARD-annotated multidrug transporters such as norB and mdeA . Notably, fieF resided inside an integrative and conjugative element (ICE) structure suggesting that 5-FU resistance traits could spread through horizontal gene transfer. Finally, analysis of VFDB-annotated genes highlighted differences in virulence-associated profiles between resistant and sensitive isolates. Resistant isolates were enriched for genes implicated in host invasion and immune-evasion genes, including ply (pneumolysin), piuA (iron/heme uptake transporter) and STR_RS06905 (predicted surface adhesin) (Fig. 4 C; Supplementary Fig. S6C ). These factors are known to influence host–tissue interactions and nutrient availability, processes that may indirectly shape the local tumour-associated microenvironment 66 – 71 . In contrast, the sensitive isolates contained more genes which support colonization and stress adaptation such as cppA, eno, htrA/degP, lmb, psaA and slrA genes at higher levels 66 – 71 . Together, these findings indicate that 5-FU resistant isolates harbour a diverse genetic repertoire that may contribute to survival under 5-FU pressure. Resistance does not appear to be driven by a single dominating mechanism and these isolates also carry virulence genes that may facilitate local environmental modification further supporting their persistence. Pathway-level analysis of resistance- and sensitive- associated genes. To further characterise the potential functions of genes distinguishing resistant from sensitive isolates, loci were mapped to bacterial biological processes using KEGG, GO and COG annotations restricted to prokaryotic functional categories. At the gene level, resistance- and sensitive-associated loci showed clearly distinct functional profiles across KEGG, GO and COG annotations (Fig. 5 A; Supplementary Data S6 ). In total, 316 functional connections were identified, of which 301 (95.6%) were enriched in sensitive isolates. Resistant isolates were enriched for a limited set of functions, including galactose metabolism ( galK ), multidrug efflux ( lmrB ), and stress-associated loci ( stbD ), indicating a resistance-linked emphasis on transport and adaptive stress tolerance rather than expansion of anabolic capacity 72 – 76 . In contrast, sensitive isolates were enriched in genes involved in core metabolic and housekeeping processes, including nucleotide biosynthesis ( guaB, carA ), carbohydrate utilisation ( amyA, pgl, gpmB2 ), and amino-acid biosynthesis ( lysA, metA ) 77 – 83 . Additional enrichment of genes involved in DNA replication and repair ( recF, recX, holA ), translation ( rpmH, trpS, def ), and chromosome organisation ( scpA, scpB ) indicates a greater reliance on intact growth-associated cellular machinery in sensitive isolates 84 – 88 . Majority of enriched genes from both sensitive and resistant isolates were assigned to the “transporter” category accounting for 80–90% of annotated genes. The remaining genes were predominantly associated with detoxification-related functions. Notably, genes classified under the “virulence” category were detected exclusively in 5-FU resistant bacterial isolates (Fig. 5 B). This pattern is consistent with the efflux- and virulence- signatures identified in the preceding gene-level analysis (Fig. 4 ). Statistical analysis of KEGG and GO and COG categories further supported these patterns (Fig. 5 C). Modules related to DNA replication and repair, including mismatch repair and Okazaki-fragment processing, together with transport systems (ABC-2 permeases and MFS pumps) and regulatory functions (sigma factors and AgrA -like regulators) exhibited higher log₂ fold changes amongst resistant-enriched modules. In contrast, majority of sensitive-enriched loci identified in this study corresponded to amino-acid biosynthesis (glutamate racemase), carbohydrate metabolism (phosphoglycerate mutases), RNA modification (pseudouridylate synthases) and lipid biosynthesis ( Supplementary Data S6 ). To further characterise functional divergence, genome-wide gene counts, and enrichment scores were compared across annotation frameworks ( Fig. S7A). Gene-count distributions showed that metabolic and transport-related functions comprised the majority of annotated genes, while resistant genomes exhibited modest but consistent enrichment of defence- and stress-response-related functions as well as regulatory categories. Pathway-level enrichment analyses identified KEGG pathways (e.g. peptidoglycan and teichoic-acid biosynthesis, phosphotransferase system, Fig. S7B ), COG functional classes (translation and ion transport, Fig. S7C ) and GO biological processes (nucleic-acid and macromolecule metabolism, Fig. S7D ) that differentiated resistant from sensitive isolates. Collectively, these results indicate that 5-FU–resistant isolates exhibit a distinct functional composition characterised by reduced representation of broad metabolic categories and increased representation of transport-, stress-response-, cell-envelope-, and regulatory-associated modules. Discussion In this study, we systematically characterised bacterial 5-FU resistance among Streptococcus isolates from HNC patients and show that 5-FU resistance is common, occurring in approximately two-thirds of isolates (69/101) examined. Resistant phenotypes were observed across diverse Streptococcus species and did not occur more frequently in tumour-associated samples than in adjacent oral mucosa. This distribution suggests that bacterial 5-FU tolerance is more likely to reflect pre-existing functional diversity within the oral microbiome, with genomic analysis identifying genetic features potentially associated with resistance. Previous work analysing the sensitivity of oral bacteria to 5-FU including multiple Streptococcus species, suggested that S. mitis , S. oralis and S. salivarius exhibit intrinsic tolerance to 5-FU 89 . By analysing multiple strains per species, we extend these observations by demonstrating substantial within-species heterogeneity, with isolates of the same species displaying different levels of 5-FU sensitivity. Consistent with this, 5-FU resistance status and anatomical site minimally contributed to accessory genome diversity in our pangenome analyses. Resistant and sensitive isolates showed substantial overlap in genus-level ordinations, indicating that acquisition of 5-FU tolerance is not associated with large-scale reconfiguration of overall gene content. Species-specific analysis further revealed only modest and heterogeneous resistance-associated signals. These findings are consistent with previous bacterial genetic screens demonstrating that 5-FU resistance can arise through multiple distinct genetic mechanisms 90 . Pyrimidine metabolism, mediated by the pre-TA operon, is a key resistance mechanism across many bacterial taxa, prevalent within the Firmicutes phylum 8 . In contrast, our genomic analysis show that Streptococcus isolates lack the pre-TA operon, consistent with prior reports 8 , 89 and further supported by the absence of detectable conversion of 5-FU to DHFU in our samples (data not shown). Instead, resistant isolates in our cohort were enriched for genes associated with multidrug transport ( lmrB and ybhR) , stress response and dormancy ( stbD) , DNA repair ( sbcDI) , capsule and cell-envelope biosynthesis, and redox balance (c psE, wcaJ, epsL, wbbJ ) 41 , 91 – 99 . Rather than representing discrete resistance determinants, the distribution of these loci supports a multifactorial resistance strategy centred on stress adaptation, envelope modulation, and metabolic buffering. Functional profiling across KEGG, GO, and COG categories corroborated these gene-level signals, with resistant isolates exhibiting relative enrichment of transport systems (ABC-2 permeases, MFS pumps), regulatory functions (sigma factors, AgrA -like), DNA repair/replication modules (mismatch repair, Okazaki processing), and cell-envelope functions, alongside modest enrichment of defence and detoxification functions 100 – 104 . Notably, similar pathway level signatures have been reported in prior in vitro evolution and multi-omic studies of bacterial 5-FU resistance supporting the broad relevance of these adaptive processes 89 , 90 . Our genomic analysis revealed two additional features associated with our resistant isolates. First, extending on our previous analysis showing enrichment for ICEs in HNC-associated bacterial genomes, we found that several 5-FU associated resistance genes, most notably the cation efflux transporter fief , were frequently embedded within ICEs in resistant isolates suggesting HGT mediated dissemination of these genes 24 . Second, resistant isolates were enriched for genes commonly implicated in virulence-related functions, including host tissue interaction and immune evasion ( ply ), iron uptake ( piuA) , and predicted adhesins 66 – 71 . Here, these loci likely reflect general stress tolerance, surface remodelling, and ecological competitiveness rather than direct pathogenicity. Notably, in S. mitis , resistant isolates showed the enrichment of the blpS–lyrR–lytT bacteriocin operon, which mediates quorum-sensing-based bacteriocin production involved in interbacterial competition 105 , 106 . Collectively, these suggest that 5-FU resistant strains not only can tolerate chemotherapeutic stress but also harbour genetic features that influence microbial interactions and local microenvironment dynamics. Conversely, sensitive isolates maintained greater representation of core anabolic pathways, including amino acid, carbohydrate, nucleotide, and lipid biosynthesis consistent with a growth-oriented physiological profile 100 – 104 . This pattern suggest that resistant isolates prioritise specialised modules for drug export, stress resilience, and ecological persistence, potentially at the expense of broad metabolic versatility 107 , 108 . To the best of our knowledge, this is the first comprehensive study to characterise 5-FU resistance profiles in Streptococcus isolates from HNC patients. While 5-FU is commonly administered in combination with platinum-based chemotherapy (e.g. cisplatin) in metastatic HNC, patients in our cohort were treated with curative-intent surgery and are therefore unlikely to have received prior 5-FU exposure, suggesting that the resistance profiles observed are unlikely to reflect direct selection from previous 5-FU treatment 109 . Instead, these findings raise the possibility that features of the oral and tumour-associated microenvironments may contribute to the enrichment of 5-FU-tolerant populations, or that 5-FU resistance is inherently prevalent across oral microbiomes. A limitation of this study is the absence of a matched healthy control isolates, which precludes direct assessment of these possibilities. Furthermore, while our analyses identify genetic features associated with resistance, establishing causality will require future studies incorporating transcriptomic and proteomic approaches to confirm pathway activation in response to 5-FU exposure, as well as targeted gene-knockout studies to assess loss of resistance phenotypes. Moreover, reliance on existing functional annotation databases may have biased pathway-level interpretation, as uncharacterised genes assigned to COG category S (function unknown) were common in our dataset and may harbour novel resistance mechanisms not captured in the current study. Finally, our study has important clinical implications in the broader cancer context. 5-FU remains one of the most widely used chemotherapeutic agents for the treatment of multiple malignancies and is a well-recognised contributor to oral mucositis, with detectable levels present in salivary secretions following treatment 1 , 110 – 112 . While prior work has largely focused on chemotherapy-induced tissue injury, increasing evidence indicates that disruption of the oral microbiome plays a significant role in the severity and persistence of mucositis 113 . Our findings suggest that exposure to chemotherapeutic pressure may preferentially select for oral bacterial populations with enhanced stress tolerance, survival capacity, and pathogenic potential. Given that oral infections are a common complication during chemotherapy and that oral cavity infection represents a potential source of microbial translocation into the bloodstream and distant tissues, including tumours, the emergence of such resistant populations may have implications for infection risk, treatment tolerance, and patient outcomes 114 , 115 . Conclusion In conclusion, we report for the first time the 5-FU resistance profiles of HNC-associated Streptococcus isolates. We show that 5-FU resistance is highly prevalent within this genus and suggest that chemotherapy may select for bacterial strains with increased virulence potential, leading to shifts in the oral microbial community. These bacteria may not only persist in chemotherapy-altered environments but also carry virulence genes that can actively shape their local niche. Our findings highlight the potential value of microbiome-directed monitoring or supportive interventions alongside chemotherapy, while underscoring the need for further investigation. Materials and Methods Study Population and Sample Collection Microbial isolates analysed in this study were derived from the same cohort of 31 patients with head and neck cancer (HNC) previously described in our earlier work 24 . Patients underwent surgical resection at three hospitals in Adelaide, South Australia, between 2021 and 2022 (Table 1 , Supplementary Data S1 ). Table 1 Summary of Head and Neck Cancer Patient Characteristics. Cancer Type Count Mean Age Male Female Smoking (Yes) Alcohol (Yes) Stages (Top Counts) Oropharyngeal SCC 6 65.5 6 (100%) 0 (0%) 6 (100%) 2 (33%) Stage III: 2; Stage IVa (Recurrent): 1; Unknown: 1; Stage IVb: 1; Stage I: 1 Laryngeal SCC 6 71.5 6 (100%) 0 (0%) 1 (17%) 1 (17%) Unknown: 2; Stage Ivb: 1; Stage IVa: 1; Stage III: 1; Stage IVb: 1 Oral Tongue SCC 4 73.5 4 (100%) 0 (0%) 2 (50%) 3 (75%) Unknown: 1; Stage IVa: 1; Stage III: 1; Stage II: 1 HNC Unspecified 4 67.3 3 (75%) 1 (25%) 3 (75%) 4 (100%) Unknown: 1; Stage I: 1; Stage IVa: 1; Stage II: 1 Oral SCC 2 69.5 1 (50%) 1 (50%) 2 (100%) 1 (50%) Stage III: 2 Floor of Mouth SCC 2 65 1 (50%) 1 (50%) 1 (50%) 1 (50%) Stage II: 1; Unknown: 1 Buccal Mucosa SCC 2 62 1 (50%) 1 (50%) 0 (0%) 0 (0%) Unknown: 1; Stage III: 1 Salivary Gland Carcinoma 1 71 1 (100%) 0 (0%) 1 (100%) 0 (0%) Stage IVa (T4a): 1 Other HNC 1 76 1 (100%) 0 (0%) 0 (0%) 0 (0%) Unknown: 1 Sinonasal Carcinoma 1 75 1 (100%) 0 (0%) 1 (100%) 0 (0%) Unknown: 1 Gingiva/Alveolar Ridge SCC 1 83 1 (100%) 0 (0%) 0 (0%) 0 (0%) Stage IVa: 1 Nasopharyngeal Carcinoma 1 61 1 (100%) 0 (0%) 1 (100%) 1 (100%) Unknown: 1 Tumour tissue and matched macroscopically normal oral mucosal samples were collected intraoperatively under sterile conditions. All samples were transported on ice and processed within 24 hours of collection. Written informed consent was obtained from all participants prior to surgery. Ethical approval was granted by the Central Adelaide Local Health Network Human Research Ethics Committee (CALHN Ref No. 14116). All samples were de-identified prior to downstream analysis. Bacterial Isolation and Culturing The isolation and taxonomic characterization of bacterial strains from tumour and oral cavity samples of 31 HNC patients were previously described in detail 24 . Briefly, oral swabs and surgically resected tumour tissues were processed under aerobic and anaerobic conditions using a range of selective and non-selective media. Colonies were subcultured for purity, identified by MALDI-TOF mass spectrometry, and cryopreserved at − 80°C. Whole-genome sequencing (WGS) of all isolates was performed using a hybrid approach combining Oxford Nanopore long-read and Illumina short-read sequencing platforms, and genome assemblies were functionally annotated as described in our earlier study 24 . For this study, a subset of Streptococcus isolates was selected from the existing WGS dataset for further phenotypic and functional analyses. The selection was guided by taxonomic assignment (via MALDI-TOF and WGS), clinical source (tumour vs oral), and the high prevalence of Streptococcus species in the HNC microbiome observed in our dataset 24 . Selected isolates were revived from glycerol stocks and cultured under appropriate conditions for minimum inhibitory concentration (MIC) testing. Aerobically recovered Streptococcus isolates were grown on tryptic soy–based media (TBS) and tryptic soy–based agar (TBA) under standard aerobic conditions, while tumour-derived anaerobic isolates were cultured on Wilkins–Chalgren agar and broth (WCA/WCB) in an anaerobic chamber at 37°C (oxygen-free environment). Genome Annotation and Functional Analysis WGS of Streptococcus isolates used in this study were generated as part of larger HNC isolate collection described 24 . Genomes had been assembled using a hybrid sequencing approach combining Oxford Nanopore long reads and Illumina short reads, as described previously 24 . For the present study, bioinformatic analyses focused on characterizing genomic determinants of 5-fluorouracil (5-FU) resistance. Genome annotation was performed using Prokka v1.14.6 25 , followed by refinement with BLASTX v2.13.0 + 26 against curated databases including the Comprehensive Antibiotic Resistance Database (CARD v3.2.7) 27 , Virulence Factor Database (VFDB; accessed March 2024) 28 , PlasmidFinder v2.1 29 , and custom databases related to chemoresistance, fluoropyrimidine metabolism, and mobile genetic elements. Gene presence/absence matrices were generated using Panaroo v1.3.4 30 and used for comparative genomic analysis between resistant and sensitive isolates. Specific attention was given to genes implicated in 5-FU resistance, including the preTA operon, DPYD homologs, uracil salvage pathway genes ( upp , tdk , udk ), and efflux pump components 8 , 31 – 34 . Annotations were cross-referenced with isolate metadata, including species, anatomical origin (tumour vs oral), and MIC-derived susceptibility phenotypes (defined in section below) to identify patterns associated with drug resistance. Minimum Inhibitory Concentration (MIC) Assay The susceptibility of Streptococcus isolates to 5-fluorouracil (5-FU) was assessed using a broth microdilution method. Aerobic isolates were cultured overnight in Tryptic Soy Broth (TSB), whereas anaerobic isolates were grown in Wilkins–Chalgren Broth (WCB). Cultures were inoculated into 96-well plates containing two-fold serial dilutions of 5-FU ranging from 8 µg/mL to 1,000 µg/mL. Aerobic plates were incubated at 37°C under ambient atmospheric conditions, and anaerobic plates were incubated at 37°C in an oxygen-free chamber. Bacterial growth was assessed at 8 h, 16 h, 24 h and 48 h by both visual inspection and optical density (OD) measurement. The minimum inhibitory concentration (MIC) was defined as the lowest 5-FU concentration that completely inhibited visible growth, consistent with standard broth microdilution practices 35 , 36 . For visualisation, MIC values were log₂-transformed prior to plotting. Isolates showing no growth at the lowest tested concentration (8 µg/mL) were assigned an MIC of 8 µg/mL for graphical display. To categorize phenotypes, isolates were classified as resistant if growth was observed at or above 64 µg/mL, which corresponded to the median MIC value across all tumour- and oral-derived isolates. This threshold provided a biologically meaningful midpoint in the overall susceptibility distribution and aligns with previously reported intermediate resistance levels in gut commensals 37 . OD measurements were used to confirm growth inhibition kinetics and ensure consistency across time points but were not used to redefine MIC values. All MIC assays were performed in biological triplicates. Pathway and Functional Enrichment Analysis Functional annotation of all coding sequences was performed using eggNOG-mapper (v2.1.9) 38 to assign Cluster of Orthologous Groups (COG) categories and KEGG orthologs (KOs). Enrichment analyses were conducted separately for 5-FU-resistant and -sensitive groups using the clusterProfiler package in R (v4.2.2) 39 . Overrepresented functions were determined using Fisher’s exact test with Benjamini–Hochberg correction (FDR < 0.05). Gene-level annotations were aggregated into pathway categories including nucleotide metabolism, DNA repair, transport systems, and transcriptional regulation. Functional differences were visualized using bar plots, dot plots, and heatmaps generated with ggplot2 and ComplexHeatmap 40 . Statistical Analysis All statistical analyses were conducted in R (version 2024.12.1 + 563) and GraphPad Prism (version 10.4.0). Minimum inhibitory concentration (MIC) values were compared between groups using non-parametric Wilcoxon rank-sum tests. For categorical gene presence/absence data, enrichment was assessed using Fisher’s exact test or chi-square test where appropriate. Multiple testing correction was applied using the Benjamini–Hochberg method. Principal coordinates analysis (PCoA), volcano plots, and violin plots were generated to visualize genomic features stratified by resistance phenotype. Declarations Data Availability Statement Experimental data are included in the supplementary information. Microbial genomic sequencing data generated in this study are available in the NCBI BioProject database under accession number PRJNA1403646 . Author Contributions L.M.: Conceptualization, Methodology, Investigation, Data Analysis, Writing – Original draft, Writing – review and editing. G.B.: Investigation, Data Analysis, Writing – Original draft, Writing – review and editing. K.Y, E.B, B.K. : Investigation, Writing – review and editing. J.H: Resources, Writing – review and editing. P.W., R.V., A.P., S.V.: Resources, Writing – review and editing, Supervision, Funding Acquisition. K.F: Conceptualization, Investigation, Writing – Original draft, Writing – review and editing, Supervision, Project Administration, Funding Acquisition. Acknowledgments This work was supported by an HSCGB Ray and Shirl Norman Cancer Research Grant (A.P., K.F., S.V., and R.V.), an NHMRC investigator grant APP1196832 (P.W.), a The Garnett Passe and Rodney Williams Senior Fellowship (S.V.), a Cancer Council SA Research Fellowship (K.F) and a The University of Adelaide Research Training Program Scholarship (L.M). We would like to thank the medical staff from The Royal Adelaide Hospital and The Memorial Hospital for their assistance in sample collection. Conflict of Interests The authors declare that there are no conflicts of interest. 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Inhibition of mutation and combating the evolution of antibiotic resistance. PLoS Biol 3 , e176 (2005). https://doi.org/10.1371/journal.pbio.0030176 Baharoglu, Z. & Mazel, D. SOS, the formidable strategy of bacteria against aggressions. FEMS Microbiol Rev 38 , 1126-1145 (2014). https://doi.org/10.1111/1574-6976.12077 Reddy, V. S., Shlykov, M. A., Castillo, R., Sun, E. I. & Saier, M. H., Jr. The major facilitator superfamily (MFS) revisited. Febs j 279 , 2022-2035 (2012). https://doi.org/10.1111/j.1742-4658.2012.08588.x Kazmierczak, M. J., Wiedmann, M. & Boor, K. J. Alternative sigma factors and their roles in bacterial virulence. Microbiol Mol Biol Rev 69 , 527-543 (2005). https://doi.org/10.1128/mmbr.69.4.527-543.2005 Le, K. Y. & Otto, M. Quorum-sensing regulation in staphylococci-an overview. Front Microbiol 6 , 1174 (2015). https://doi.org/10.3389/fmicb.2015.01174 Fontaine, L. et al. Quorum-sensing regulation of the production of Blp bacteriocins in Streptococcus thermophilus. J Bacteriol 189 , 7195-7205 (2007). https://doi.org/10.1128/jb.00966-07 Renye, J., Somkuti, G., Garabal, J. I. & Steinberg, D. Bacteriocin production by Streptococcus thermophilus in complex growth media. Biotechnology Letters 38 (2016). https://doi.org/10.1007/s10529-016-2184-2 Davies, J. & Davies, D. Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev 74 , 417-433 (2010). https://doi.org/10.1128/mmbr.00016-10 Engelberg-Kulka, H., Amitai, S., Kolodkin-Gal, I. & Hazan, R. Bacterial programmed cell death and multicellular behavior in bacteria. PLoS Genet 2 , e135 (2006). https://doi.org/10.1371/journal.pgen.0020135 Forastiere, A. A. Chemotherapy of head and neck cancer. Ann Oncol 3 Suppl 3 , 11-14 (1992). https://doi.org/10.1093/annonc/3.suppl_3.s11 Kumagai, A. et al. A pilot study of the clinical evidence for the methodology for prevention of oral mucositis during cancer chemotherapy by measuring salivary excretion of 5-fluorouracil. BDJ Open 4 , 17041 (2018). https://doi.org/10.1038/s41405-018-0008-2 Sonis, S. T. The pathobiology of mucositis. Nat Rev Cancer 4 , 277-284 (2004). https://doi.org/10.1038/nrc1318 Joulia, J. M. et al. Plasma and salivary pharmacokinetics of 5-fluorouracil (5-FU) in patients with metastatic colorectal cancer receiving 5-FU bolus plus continuous infusion with high-dose folinic acid. Eur J Cancer 35 , 296-301 (1999). https://doi.org/10.1016/s0959-8049(98)00318-9 Min, Z., Yang, L., Hu, Y. & Huang, R. Oral microbiota dysbiosis accelerates the development and onset of mucositis and oral ulcers. Front Microbiol 14 , 1061032 (2023). https://doi.org/10.3389/fmicb.2023.1061032 Abed, J. et al. Colon Cancer-Associated Fusobacterium nucleatum May Originate From the Oral Cavity and Reach Colon Tumors via the Circulatory System. Frontiers in Cellular and Infection Microbiology Volume 10 - 2020 (2020). https://doi.org/10.3389/fcimb.2020.00400 Stephen T. Sonis, D. a. J. W. C., Jr, DMD. Oral Complications of Cancer Chemotherapy . Vol. 6th edition (2003). Additional Declarations There is no conflict of interest Supplementary Files SupDataS1Patientclinicalmetadata.xlsx Supp Dataset 1 SupDataS2Metadata.xlsx Supp Dataset 2 SupDataS5CARDMFSVFDB.xlsx Supp Dataset 5 MaiStrepResistanceSuppFigures.docx Supplementary Figures SupDataS6pathwayenrichment.xlsx Supp Dataset 6 SupDataS3PCoAanalysis.xlsx Supp Dataset 3 SupDataS4Genecomparison.xlsx Supp Dataset 4 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8825984","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594208147,"identity":"6d18e18a-9020-4237-a965-393563c15114","order_by":0,"name":"Linh Mai","email":"","orcid":"","institution":"Adelaide University","correspondingAuthor":false,"prefix":"","firstName":"Linh","middleName":"","lastName":"Mai","suffix":""},{"id":594208148,"identity":"8f6fffb5-707c-4f1b-9465-4eb737fa8ce2","order_by":1,"name":"George Bouras","email":"","orcid":"","institution":"Adelaide 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05:20:37","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8825984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8825984/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103400293,"identity":"91358121-1a5f-4ab7-ba2e-d3220db9aee6","added_by":"auto","created_at":"2026-02-25 09:18:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":278452,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e5-FU resistance in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eStreptococcus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e isolates across anatomical sites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Distribution of minimum inhibitory concentration (MIC) values across 101 Streptococcus isolates from oral cavity (blue) and tumour (red) sites. MICs are displayed on a log₂ scale with a dashed line indicating the 64 µg/mL resistance threshold. MIC values are displayed on a log₂ scale; isolates with no growth at 8 µg/mL were assigned an MIC of 8 µg/mL\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Viability scatterplots at 64 µg/mL grouped by species. Points represent individual isolates, with error bars showing mean ± SD. Colores indicate resistance group, and shapes indicate anatomical site.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C)\u003c/strong\u003e Heatmap of assigned MIC values for all isolates, ordered by species. Colour scale indicates MIC categories (≤8, 16, 32, 64, 125, 250, 500, 1000, \u0026gt;1000 µg/mL).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/ed02b5f64f5e2837466065cc.png"},{"id":104397563,"identity":"866d540d-5674-4fc4-aaa8-4af5da1783c2","added_by":"auto","created_at":"2026-03-11 11:51:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal and species-specific patterns of accessory genome variation in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eStreptococcus\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Principal coordinates analysis (PCoA) of Panaroo accessory gene presence/absence using Jaccard distance. Each point represents a single \u003cem\u003eStreptococcus\u003c/em\u003eisolate. Points are coloured by resistance × anatomical site group and shaped by sampling location. Ellipses indicate 95% confidence regions for each group. Only genes present in 5–95% of isolates were retained. The percentage of variance explained by each axis is shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Dot plot summarising PERMANOVA R² values for resistance status and anatomical location within individual \u003cem\u003eStreptococcus\u003c/em\u003e species. Each point represents the proportion of variance in accessory gene content explained by the corresponding factor within a species. Point size reflects the number of isolates per species. Statistical significance was assessed by permutation testing.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/2ffc3ca3e4ee5f5746974b63.png"},{"id":103507258,"identity":"282db1d9-fa78-4644-a1b1-7b629be24be6","added_by":"auto","created_at":"2026-02-26 13:40:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226013,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal accessory gene associations with 5-FU resistance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVolcano plot summarising Fisher’s exact tests comparing accessory gene presence–absence between resistant and sensitive \u003cem\u003eStreptococcus\u003c/em\u003e isolates across all species. The x-axis shows the frequency difference (Resistant − Sensitive), and the y-axis shows −log₁₀(p-value). Vertical dashed lines indicate an absolute frequency difference of 0.2, and the horizontal dashed line indicates nominal p = 0.05. Genes meeting both criteria (nominal p \u0026lt; 0.05 and |Δfrequency| \u0026gt; 0.2) are coloured as enriched in resistant (red) or enriched in sensitive (green) isolates; all other genes are shown in blue. Only genes retained after prevalence filtering (5–95% of isolates) are included. Selected loci are labelled for clarity.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/d01677bcfbe6c5afcb12fd0f.png"},{"id":103506578,"identity":"02b565b7-8459-4783-aa8e-5d7eed098327","added_by":"auto","created_at":"2026-02-26 13:37:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":358415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNominal enrichment of resistance-, transporter-, and virulence-associated genes in 5-FU–resistant Streptococcus.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeat maps show the presence of genes nominally associated with 5-FU resistance across \u003cem\u003eStreptococcus\u003c/em\u003especies, grouped by functional annotation framework: (\u003cstrong\u003eA\u003c/strong\u003e) CARD antibiotic resistance genes, (\u003cstrong\u003eB\u003c/strong\u003e) VFDB virulence-associated genes, and (\u003cstrong\u003eC\u003c/strong\u003e) MFS and related transporter genes. Genes were identified using Fisher’s exact tests comparing resistant and sensitive isolates, applying nominal significance (p \u0026lt; 0.05) without multiple-testing correction. Rows represent individual isolates, ordered by \u003cstrong\u003eresistance status,\u003c/strong\u003e and columns represent genes within each functional category. \u003cstrong\u003eHeatmap colour indicates binary \u003c/strong\u003egene presence \u003cstrong\u003e(dark green) or absence (white)\u003c/strong\u003e, with isolate resistance status shown in the left annotation bar (red = resistant; blue = sensitive). These heat maps visualise convergent functional enrichment patterns associated with 5-FU resistance rather than species-specific gene signatures.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/21a00e616da6299341bfe0b5.png"},{"id":103400290,"identity":"c31b1eea-80cd-40fb-b085-701b6388daca","added_by":"auto","created_at":"2026-02-25 09:18:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":443720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment of KEGG, COG, and GO categories in resistant and sensitive \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eStreptococcus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eisolates.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Chord diagram linking resistance-associated genes to resistance group (Resistant vs Sensitive) and to functional annotation types (e.g., GO/KEGG/COG or other specified annotation classes, depending on your input tables). Each sector represents a gene, group, or annotation type; ribbons indicate gene–group and gene–annotation links. Sector colours distinguish groups and annotation types; gene sectors inherit group colouring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Stacked bar plot showing the proportional distribution of functional categories among resistance-associated genes, stratified by resistance group. Each bar represents a group (Resistant or Sensitive), and segments represent functional categories (e.g., transporter, detoxification, virulence); y-axis shows percentages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C) \u003c/strong\u003eDot plot summarizing enriched functional annotations derived from eggNOG-based GO/KEGG/COG mappings, grouped by annotation type. Each point represents one functional term; the x-axis shows log2 fold change (Resistant vs Sensitive) computed from gene counts mapped to that term, and point size indicates the number of genes contributing to the term. Facets show GO, KEGG, and COG terms\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/64a2672ddd08b5575345d565.png"},{"id":106824060,"identity":"cb8fd8a8-e3de-4362-8fa2-ac410abb0fb2","added_by":"auto","created_at":"2026-04-13 20:00:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3079726,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/c7d548fc-e598-468c-bdaa-be78a1890c9a.pdf"},{"id":103400288,"identity":"457c4818-099e-4c60-b654-5c62ae89c2fd","added_by":"auto","created_at":"2026-02-25 09:18:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13098,"visible":true,"origin":"","legend":"Supp Dataset 1","description":"","filename":"SupDataS1Patientclinicalmetadata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/24bb6f2ff32ec2e0abda990b.xlsx"},{"id":103507973,"identity":"4299b6e1-6f63-4bd4-96de-d1b56afa0f99","added_by":"auto","created_at":"2026-02-26 13:46:44","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":30397,"visible":true,"origin":"","legend":"Supp Dataset 2","description":"","filename":"SupDataS2Metadata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/261dccfdf9f73858566b1b13.xlsx"},{"id":103400289,"identity":"0e383b78-f1d6-4bb9-953b-59ceee845ba5","added_by":"auto","created_at":"2026-02-25 09:18:43","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":193672,"visible":true,"origin":"","legend":"Supp Dataset 5","description":"","filename":"SupDataS5CARDMFSVFDB.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/5f26ef269bd7d9f16c200c39.xlsx"},{"id":103400292,"identity":"1069137d-c56a-41f1-aa77-a978425354b9","added_by":"auto","created_at":"2026-02-25 09:18:43","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4051409,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"MaiStrepResistanceSuppFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/f53fd913e73ee9b4c77fc761.docx"},{"id":103400295,"identity":"70a359a6-072b-4225-b07b-4b0b84d95376","added_by":"auto","created_at":"2026-02-25 09:18:43","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2842158,"visible":true,"origin":"","legend":"Supp Dataset 6","description":"","filename":"SupDataS6pathwayenrichment.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/eaabacc62afaf9e68ceea4e0.xlsx"},{"id":103400296,"identity":"bc8adc43-8f5d-4a8c-bf88-657c77e82400","added_by":"auto","created_at":"2026-02-25 09:18:43","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":36508,"visible":true,"origin":"","legend":"Supp Dataset 3","description":"","filename":"SupDataS3PCoAanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/e99503abad1b09d99f339c14.xlsx"},{"id":103400294,"identity":"759fcddd-4d5c-4062-842c-4cffad5b2afa","added_by":"auto","created_at":"2026-02-25 09:18:43","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":3948853,"visible":true,"origin":"","legend":"Supp Dataset 4","description":"","filename":"SupDataS4Genecomparison.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8825984/v1/1494e603469456f8bab83bc9.xlsx"}],"financialInterests":"There is no conflict of interest","formattedTitle":"\u003cp\u003eGenomic Signatures and Functional Pathways Underlying 5-Fluorouracil Resistance in Head and Neck Cancer associated Streptococci\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAmong chemotherapeutic agents, 5-fluorouracil (5-FU) is widely used because it inhibits thymidylate synthase and disrupts DNA synthesis. However, resistance to 5-FU remains a major barrier to effective treatment for many cancer types\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Interestingly, 5-FU has broad-spectrum antimicrobial activity and has been proposed as a scaffold for the development of next-generation antibiotics\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This dual functionality suggests that, in addition to tumour inhibition, 5-FU may substantially alter the microbiome of treated patients, with potential and underappreciated consequences for patient health and disease progression.\u003c/p\u003e \u003cp\u003eBacteria have evolved multiple strategies to survive fluoropyrimidines, such as 5-FU through modifications in uracil permease-mediated transport and rewiring of pyrimidine salvage pathways. In Gram-positive bacteria, efflux pumps belonging to the Major Facilitator Superfamily (MFS) reduce intracellular drug accumulation, while in Gram-negative species, Resistance-Nodulation-Division (RND) family transporters perform similar roles\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Additional resistance mechanisms arise through mutations in genes like \u003cem\u003eupp\u003c/em\u003e (uracil phosphoribosyltransferase), \u003cem\u003etdk\u003c/em\u003e (thymidine kinase), and \u003cem\u003ethyA\u003c/em\u003e (thymidylate synthase), which disrupt fluoropyrimidine metabolism or drug-target interactions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Beyond genetic adaptations, resistance can be mediated by physiological responses including SOS-driven DNA repair, reduced uptake via altered transporters, and biofilm-mediated protection\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Notably, recent work on colorectal cancer (CRC) associated gut bacteria harbouring the \u003cem\u003epreTA\u003c/em\u003e operon has shown that these microbes enzymatically degrade 5-FU by converting it into the inactive metabolite dihydrofluorouracil (DHFU). This metabolic inactivation not only promotes bacterial survival but can also reduce chemotherapy efficacy and alter toxicity profiles\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile most cancer-microbiome research on 5-FU has focused on its effects on the gut microbiome in CRC\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, far less is known about how tumour-associated microbiota relevant to head and neck cancer (HNC) respond to fluoropyrimidines. In HNC, 5-FU is administered systemically\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, which likely leads to drug exposure in both tumour- and oral cavity-resident bacteria. Furthermore, abnormal enrichment of oral cavity-resident bacterial taxa in the gut is associated with several disease states including CRC\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Oral-derived taxa such as \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003ePeptostreptococcus\u003c/em\u003e, and \u003cem\u003eParvimonas\u003c/em\u003e are highly enriched in the gut microbiome of CRC patients\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In HNC, multiple sequencing-based studies have demonstrated that oral commensal bacteria are detectable within tumour tissues, indicating that oral-derived microbes may also play an important role in HNC pathology\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, the functional consequences of this microbial presence, especially in how they respond to and potentially modulate 5-FU remains poorly understood.\u003c/p\u003e \u003cp\u003eAmong oral cavity and tumour-infiltrating bacteria in HNC, the \u003cem\u003eStreptococcus\u003c/em\u003e genus is among the most dominant\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These include common oral commensals such as \u003cem\u003eS. mitis\u003c/em\u003e, \u003cem\u003eS. parasanguinis\u003c/em\u003e, and \u003cem\u003eS. salivarius\u003c/em\u003e, which can become opportunistic pathogens in the context of mucosal damage or immunosuppression\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eStreptococci\u003c/em\u003e have been implicated in immune modulation, TME remodelling, and chemotherapy responsiveness\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The probiotic \u003cem\u003eS. salivarius\u003c/em\u003e K12 strain has also been shown to alleviate radiation-induced mucositis in HNC patients further underscoring its clinical relevance\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, despite their prevalence and potential impact in HNC pathobiology, 5-FU resistance and its underlying mechanisms in \u003cem\u003eStreptococci\u003c/em\u003e is currently unknown. Thus, it is critical to understand how prevalent 5-FU resistance is within \u003cem\u003eStreptococci\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we systematically investigated the 5-FU resistance profiles of \u003cem\u003eStreptococcus\u003c/em\u003e isolates from the oral cavities and tumour tissues of HNC patients. We utilised minimum inhibitory concentration (MIC) assays to categorise our isolates as resistant or sensitive and subsequently utilised paired whole-genome sequencing analysis to identify the genetic associations underlying resistance. This study provides the first comprehensive insight into 5-FU resistance among tumour-associated \u003cem\u003eStreptococcus\u003c/em\u003e and establishes a foundation for understanding how fluoropyrimidines may interact with microbiomes relevant to HNC.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003e5-FU resistance is observed across\u003c/b\u003e \u003cb\u003eStreptococcus\u003c/b\u003e \u003cb\u003elineages independent of anatomical site.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe tested 101 unique \u003cem\u003eStreptococcus\u003c/em\u003e strains collected from 31 HNC patients, comprising 17 tumour-derived and 84 oral-mucosa\u0026ndash;derived isolates\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e for 5-FU susceptibility. MIC assays revealed a broad range, from highly sensitive (\u0026le;\u0026thinsp;8 \u0026micro;g/mL) to highly resistant (\u0026gt;\u0026thinsp;1,000 \u0026micro;g/mL) (\u003cb\u003eSupplementary Data S2\u003c/b\u003e). The isolate collection was taxonomically diverse and dominated by members of the \u003cem\u003eS. mitis\u003c/em\u003e group with \u003cem\u003eS. mitis, S. parasanguinis\u003c/em\u003e, and \u003cem\u003eS. oralis\u003c/em\u003e being the most frequently represented species. Notably, high-level 5-FU resistance was observed across multiple species, including \u003cem\u003eS. salivarius, S. parasanguinis\u003c/em\u003e and \u003cem\u003eS. oralis\u003c/em\u003e. The MIC distributions of tumour- and oral-derived isolates were comparable, both with median values of 64 \u0026micro;g/mL (Wilcoxon test comparing tumour vs oral isolates, p\u0026thinsp;=\u0026thinsp;0.38; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). An alluvial plot linking species, sampling origin (oral cavity vs tumour tissue), and resistance category showed that resistant isolates were present at similar proportions in both oral and tumour sites (46/84 vs 10/17; Fisher\u0026rsquo;s exact p\u0026thinsp;=\u0026thinsp;0.80), indicating no evidence for enrichment of 5-FU resistance among tumour-derived isolates (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor downstream genomic association analyses, resistant isolates were defined by retained viability at 64 \u0026micro;g/mL 5-FU, a threshold previously applied to gut bacteria\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Using this definition, resistant isolates showed markedly higher viability than sensitive isolates at this concentration across all species and anatomical sites (Wilcoxon p \u0026lt;1x10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e; \u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/b\u003e), confirming robust phenotype separation. In contrast, no significant differences in viability or MIC distributions were observed between tumour- and oral-derived associated isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). There was wide resistance heterogeneity among HNC-associated isolates, with \u003cem\u003eS. salivarius\u003c/em\u003e isolates consistently exhibiting high resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Together, these findings indicate that 5-FU resistance in \u003cem\u003eStreptococcus\u003c/em\u003e is not preferentially enriched in tumour sites and is highly heterogeneous.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGlobal pangenome variation and resistance-associated genes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNext, we evaluated how bacterial 5-FU resistance status and anatomical origin affects genome diversity. Principal coordinates analysis (PcoA) of Jaccard distances computed from gene presence\u0026ndash;absence data (retaining genes present in 5\u0026ndash;95% of isolates to reduce noise from rare genes) revealed broad genomic variation across isolates. The first two principal coordinates (PCoA1 and PCoA2), which represent the main dimensions of genomic dissimilarity, together explained 55.3% of the total variance (PCoA1\u0026thinsp;=\u0026thinsp;32.8%, PcoA2\u0026thinsp;=\u0026thinsp;22.5%, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Across the global dataset, resistant and sensitive isolates were widely intermingled, and oral cavity and tumour-derived isolates showed extensive overlap, indicating that neither resistance status nor anatomical origin drives major shifts in overall gene content (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Faceting the PCoA by anatomical site similarly failed to reveal site-specific clustering of resistant isolates (\u003cb\u003eSupplementary Fig. S2A\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo formally quantify drivers of genomic variation, we performed permutational multivariate analysis of variance (PERMANOVA) on the same distance matrix. PERMANOVA confirmed that species identity explained the vast majority of variation (R\u0026sup2; = 0.925, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), whereas resistance and location contributed negligibly (R\u0026sup2; = 0.00088, p\u0026thinsp;=\u0026thinsp;0.312 and R\u0026sup2; = 0.0014, p\u0026thinsp;=\u0026thinsp;0.110, respectively (\u003cb\u003eSupplementary Data S3\u003c/b\u003e). Species-resolved PERMANOVA summaries further illustrated that, across individual taxa, resistance and location explained only modest proportions of accessory genome variation relative to species identity (\u003cb\u003eSupplementary Fig. S2B\u003c/b\u003e)\u003c/p\u003e \u003cp\u003eA test for homogeneity of multivariate dispersion (PERMDISP/betadisper) further supported the validity of these results, showing no significant differences in dispersion between resistance groups (F\u0026thinsp;=\u0026thinsp;1.57, p\u0026thinsp;=\u0026thinsp;0.206) or anatomical sites (F\u0026thinsp;=\u0026thinsp;0.33, p\u0026thinsp;=\u0026thinsp;0.564). In contrast, dispersion differed significantly between species (F\u0026thinsp;=\u0026thinsp;9.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), consistent with strong species-level genomic heterogeneity (\u003cb\u003eSupplementary Fig. S3A-C\u003c/b\u003e). These effects are summarised visually in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, which shows PERMANOVA R\u0026sup2; values for each factor, highlighting the dominant contribution of species identity relative to resistance and site.\u003c/p\u003e \u003cp\u003eTo investigate whether resistance-associated structure might be masked at the genus level, we performed species-specific PCoA analyses for all species represented by \u0026ge;\u0026thinsp;6 isolates and both resistance phenotypes (n\u0026thinsp;=\u0026thinsp;7 species). Species-specific PERMANOVA summary results displays the proportion of variance explained (R\u0026sup2;) by resistance status and sampling location within each species. Across taxa, resistance effects were modest and heterogeneous (typically R\u0026sup2; \u0026asymp; 0.10\u0026ndash;0.20), whereas location effects were consistently weak or negligible (\u003cb\u003eSupplementary Fig. S4; Supplementary Data S3\u003c/b\u003e). Consistent with these quantitative results, species-specific PCoA plots revealed variable patterns of resistance-associated separation, with partial segregation observed in \u003cem\u003eS. mitis\u003c/em\u003e but substantial overlap between resistant and sensitive isolates in \u003cem\u003eS. parasanguinis\u003c/em\u003e, \u003cem\u003eS. oralis\u003c/em\u003e, and \u003cem\u003eS. anginosus\u003c/em\u003e (\u003cb\u003eSupplementary Fig. S4\u003c/b\u003e). Importantly, no species exhibited consistent segregation by anatomical site, indicating that tumour versus oral origin does not impose a dominant constraint on accessory genome composition.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGene-level associations identify shared functional features linked to resistance\u003c/h2\u003e \u003cp\u003eDespite the absence of clear resistance- or site-associated clustering at the level of the full \u003cem\u003eStreptococcus\u003c/em\u003e pangenome, we identified specific accessory genes whose presence was associated with 5-FU resistance status. The pangenome-wide Fisher\u0026rsquo;s exact screen of accessory gene presence\u0026ndash;absence detected 122 loci which demonstrated statistically significant relationships (unadjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between resistant and sensitive isolates, comprising 26 genes enriched in resistant isolates and 96 enriched in sensitive isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cb\u003eSupplementary Data S4\u003c/b\u003e). Although none of these associations remained significant after false discovery rate (FDR) correction, the uncorrected signals were analysed in an exploratory, hypothesis-generating framework.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the cohort level, resistance-enriched loci were disproportionately associated with five major functional themes including: (1) multidrug efflux (\u003cem\u003elmrB\u003c/em\u003e and \u003cem\u003eybhR\u003c/em\u003e), (2) DNA repair (\u003cem\u003esbcDI\u003c/em\u003e), (3) stress response and dormancy(\u003cem\u003estbD\u003c/em\u003e), (4) capsule and envelope biosynthesis (\u003cem\u003ecpsE, wcaJ, epsL\u003c/em\u003e and \u003cem\u003ewbbJ\u003c/em\u003e), and (5) metabolic redox balance (\u003cem\u003epdxS\u003c/em\u003e)\u003csup\u003e\u003cspan additionalcitationids=\"CR42 CR43 CR44 CR45 CR46 CR47 CR48\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. In contrast, loci enriched among sensitive isolates were predominantly annotated as housekeeping or core metabolic functions, consistent with baseline growth.\u003c/p\u003e \u003cp\u003eTo further determine whether these global patterns masked species-specific resistance mechanisms, we repeated these association analyses within individual taxa. Species-level analyses were restricted to taxa represented by \u0026ge;\u0026thinsp;5 isolates and both resistance phenotypes, resulting in evaluable dataset for \u003cem\u003eS. parasanguinis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;9) and \u003cem\u003eS. mitis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;15) met the minimum threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |frequency difference| \u0026gt; 0.2) for within-species analysis (\u003cb\u003eSupplementary Fig. S4)\u003c/b\u003e. The PCoA analysis of \u003cem\u003eS. mitis\u003c/em\u003e accessory genes content revealed partial separation of resistant and sensitive isolates (\u003cb\u003eSupplementary Fig. S4E\u003c/b\u003e). The patterns were supported by PERMANOVA which identified resistance status as a significant contributor to gene-content variation \u003cem\u003e(\u003c/em\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003cem\u003e)\u003c/em\u003e, whereas anatomical site showed no significant association (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; \u003cb\u003eSupplementary Data S4)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eCorrespondingly, resistance-associated loci in \u003cem\u003eS. mitis (\u003c/em\u003en\u0026thinsp;=\u0026thinsp;15\u003cem\u003e)\u003c/em\u003e were enriched for genes involved in extracellular polysaccharide and capsule biosynthesis (multiple glycosyltransferases including \u003cem\u003eEpsE\u003c/em\u003e and dTDP-sugar biosynthesis enzymes), surface anchoring and envelope-associated proteins (LPXTG-anchored and choline-binding proteins), regulatory systems (the \u003cem\u003eblpS\u0026ndash;lytR\u0026ndash;lytT\u003c/em\u003e bacteriocin-associated regulatory locus and XRE-family transcriptional regulators), and stress-associated DNA repair (DNA alkylation repair enzymes) (\u003cb\u003eSupplementary Data S4\u003c/b\u003e). Notably, the \u003cem\u003eblpS\u0026ndash;lyrR\u0026ndash;lytT\u003c/em\u003e operon, which produce a bacteriocin and associated lysis-regulatory system, were detected 4/7 resistant \u003cem\u003eS. mitis\u003c/em\u003e isolates (approximately 60%) but was absent in all sensitive isolates (n\u0026thinsp;=\u0026thinsp;0/8) (\u003cb\u003eSupplementary Fig. S5A\u003c/b\u003e). This operon was not detected in \u003cem\u003eS. parasanguinis\u003c/em\u003e isolates, indicating species-specific enrichment. These findings implicate interbacterial competition, quorum sensing and envelope-associated functions may contribute to differential tolerance to 5-FU\u003csup\u003e50\u003c/sup\u003e. In contrast, sensitive \u003cem\u003eS. mitis\u003c/em\u003e isolates were enriched for \u003cem\u003eefeO\u003c/em\u003e, a component of ferrous-iron uptake systems, which was predominantly detected in oral cavity isolates (\u0026gt;\u0026thinsp;50%) and markedly depleted in resistant tumour-associated isolates, suggesting a greater reliance on nutrient acquisition rather than stress-resilience or competitive traits\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eS. parasanguinis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;9\u003cem\u003e)\u003c/em\u003e, resistance-associated patterns differed markedly. Sensitive isolates showed enrichment of \u003cem\u003ehsdM\u003c/em\u003e, encoding a DNA methyltransferase component of restriction\u0026ndash;modification systems, as well as \u003cem\u003elysR\u003c/em\u003e-family transcriptional regulators, \u003cem\u003efctA\u003c/em\u003e (fimbrial adhesin), and several conserved but unknown function loci (\u003cem\u003eaF2118\u003c/em\u003e) (\u003cb\u003eSupplementary Fig. S5B; Supplementary Data S4\u003c/b\u003e)\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Notably, \u003cem\u003ehsdM\u003c/em\u003e was detected in all sensitive isolates (n\u0026thinsp;=\u0026thinsp;3/3) but was absent from all resistant isolates (n\u0026thinsp;=\u0026thinsp;0/6), consistent with a phenotype favouring colonisation and genome defence rather than chemotherapeutic stress tolerance. Resistant \u003cem\u003eS. parasanguinis\u003c/em\u003e isolates, in contrast, were enriched for multiple uncharacterised accessory genes which encode predicted proteins of unknown function, some of which contain domains consistent with membrane association or regulatory activity based on annotation databases (\u003cb\u003eSupplementary Data S4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo assess whether resistance status corresponded to discrete segregation in ordination space, we tested for non-random distributions of resistant and sensitive isolates across the primary ordination axis (PCoA1) within each species. No species showed a significant association after correction for multiple testing (all adjusted p\u0026thinsp;=\u0026thinsp;1; \u003cb\u003eSupplementary Data S4\u003c/b\u003e), indicating that resistance-associated differences in accessory gene content reflect subtle, continuous shifts rather than categorical clustering. Together, our species-resolved analyses show that \u003cem\u003eStreptococcus\u003c/em\u003e does not have a single genetic factor that makes it resistant to 5-FU. Instead, different accessory genome configurations lead to the development of multiple functional resistance strategies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferential representation of antimicrobial resistance, efflux systems, and virulence-associated genes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFollowing our unbiased gene-level screen, loci associated with resistance were assessed for functional enrichment. Genes were classified based on homology to CARD, MFS, and VFDB to define antimicrobial resistance, efflux/transport, and virulence-associated categories, respectively. Enrichment analysis (Fisher\u0026rsquo;s exact test with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was used to compare the distribution of these gene categories between resistant and sensitive isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; \u003cb\u003eSupplementary Fig. S6; and Supplementary Data S5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCARD-based resistance annotation revealed multiple antimicrobial resistance determinants associated with 5-FU resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; \u003cb\u003eSupplementary Fig. S6A\u003c/b\u003e). Resistant bacterial isolates contained a broader repertoire of antibiotic resistance gene families which included \u003cem\u003eerm(32), ErmS, VatI, abcA, norB, norC, mdeA, catB\u003c/em\u003e variants, \u003cem\u003evanS\u003c/em\u003e (within \u003cem\u003evanF\u003c/em\u003e-associated clusters) and multiple \u003cem\u003emprF\u003c/em\u003e homologues (\u003cem\u003eBsub_mprF, Cper_mprF, Lmon_mprF\u003c/em\u003e, and \u003cem\u003eSaga_mprF\u003c/em\u003e). The genes in this group produce efflux pumps, acetyltransferases, regulatory kinases and membrane charge\u0026ndash;modifying enzymes which decrease drug levels inside cells and modify cell wall structure to provide protection against various stresses\u003csup\u003e\u003cspan additionalcitationids=\"CR56 CR57 CR58 CR59 CR60 CR61 CR62 CR63 CR64\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEfflux and metal/stress exporters were also highly enriched in resistant isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB; \u003cb\u003eSupplementary Fig. S6B).\u003c/b\u003e These strains contained \u003cem\u003efieF\u003c/em\u003e together with multiple unclassified MFS/RND transporter clusters (\u003cem\u003egroup_17153, group_19822, group_19968, group_21568\u003c/em\u003e\u003cb\u003e)\u003c/b\u003e, several of which correspond to CARD-annotated multidrug transporters such as \u003cem\u003enorB\u003c/em\u003e and \u003cem\u003emdeA\u003c/em\u003e. Notably, \u003cem\u003efieF\u003c/em\u003e resided inside an integrative and conjugative element (ICE) structure suggesting that 5-FU resistance traits could spread through horizontal gene transfer.\u003c/p\u003e \u003cp\u003eFinally, analysis of VFDB-annotated genes highlighted differences in virulence-associated profiles between resistant and sensitive isolates. Resistant isolates were enriched for genes implicated in host invasion and immune-evasion genes, including \u003cem\u003eply\u003c/em\u003e (pneumolysin), \u003cem\u003epiuA\u003c/em\u003e (iron/heme uptake transporter) and \u003cem\u003eSTR_RS06905\u003c/em\u003e (predicted surface adhesin) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC; \u003cb\u003eSupplementary Fig. S6C\u003c/b\u003e). These factors are known to influence host\u0026ndash;tissue interactions and nutrient availability, processes that may indirectly shape the local tumour-associated microenvironment\u003csup\u003e\u003cspan additionalcitationids=\"CR67 CR68 CR69 CR70\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. In contrast, the sensitive isolates contained more genes which support colonization and stress adaptation such as \u003cem\u003ecppA, eno, htrA/degP, lmb, psaA\u003c/em\u003e and \u003cem\u003eslrA\u003c/em\u003e genes at higher levels\u003csup\u003e\u003cspan additionalcitationids=\"CR67 CR68 CR69 CR70\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Together, these findings indicate that 5-FU resistant isolates harbour a diverse genetic repertoire that may contribute to survival under 5-FU pressure. Resistance does not appear to be driven by a single dominating mechanism and these isolates also carry virulence genes that may facilitate local environmental modification further supporting their persistence.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePathway-level analysis of resistance- and sensitive- associated genes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further characterise the potential functions of genes distinguishing resistant from sensitive isolates, loci were mapped to bacterial biological processes using KEGG, GO and COG annotations restricted to prokaryotic functional categories.\u003c/p\u003e \u003cp\u003eAt the gene level, resistance- and sensitive-associated loci showed clearly distinct functional profiles across KEGG, GO and COG annotations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA; \u003cb\u003eSupplementary Data S6\u003c/b\u003e). In total, 316 functional connections were identified, of which 301 (95.6%) were enriched in sensitive isolates. Resistant isolates were enriched for a limited set of functions, including galactose metabolism (\u003cem\u003egalK\u003c/em\u003e), multidrug efflux (\u003cem\u003elmrB\u003c/em\u003e), and stress-associated loci (\u003cem\u003estbD\u003c/em\u003e), indicating a resistance-linked emphasis on transport and adaptive stress tolerance rather than expansion of anabolic capacity\u003csup\u003e\u003cspan additionalcitationids=\"CR73 CR74 CR75\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. In contrast, sensitive isolates were enriched in genes involved in core metabolic and housekeeping processes, including nucleotide biosynthesis (\u003cem\u003eguaB, carA\u003c/em\u003e), carbohydrate utilisation (\u003cem\u003eamyA, pgl, gpmB2\u003c/em\u003e), and amino-acid biosynthesis (\u003cem\u003elysA, metA\u003c/em\u003e)\u003csup\u003e\u003cspan additionalcitationids=\"CR78 CR79 CR80 CR81 CR82\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Additional enrichment of genes involved in DNA replication and repair (\u003cem\u003erecF, recX, holA\u003c/em\u003e), translation (\u003cem\u003erpmH, trpS, def\u003c/em\u003e), and chromosome organisation (\u003cem\u003escpA, scpB\u003c/em\u003e) indicates a greater reliance on intact growth-associated cellular machinery in sensitive isolates\u003csup\u003e\u003cspan additionalcitationids=\"CR85 CR86 CR87\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMajority of enriched genes from both sensitive and resistant isolates were assigned to the \u0026ldquo;transporter\u0026rdquo; category accounting for 80\u0026ndash;90% of annotated genes. The remaining genes were predominantly associated with detoxification-related functions. Notably, genes classified under the \u0026ldquo;virulence\u0026rdquo; category were detected exclusively in 5-FU resistant bacterial isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This pattern is consistent with the efflux- and virulence- signatures identified in the preceding gene-level analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Statistical analysis of KEGG and GO and COG categories further supported these patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Modules related to DNA replication and repair, including mismatch repair and Okazaki-fragment processing, together with transport systems (ABC-2 permeases and MFS pumps) and regulatory functions (sigma factors and \u003cem\u003eAgrA\u003c/em\u003e-like regulators) exhibited higher log₂ fold changes amongst resistant-enriched modules. In contrast, majority of sensitive-enriched loci identified in this study corresponded to amino-acid biosynthesis (glutamate racemase), carbohydrate metabolism (phosphoglycerate mutases), RNA modification (pseudouridylate synthases) and lipid biosynthesis (\u003cb\u003eSupplementary Data S6\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo further characterise functional divergence, genome-wide gene counts, and enrichment scores were compared across annotation frameworks (\u003cb\u003eFig. S7A).\u003c/b\u003e Gene-count distributions showed that metabolic and transport-related functions comprised the majority of annotated genes, while resistant genomes exhibited modest but consistent enrichment of defence- and stress-response-related functions as well as regulatory categories. Pathway-level enrichment analyses identified KEGG pathways (e.g. peptidoglycan and teichoic-acid biosynthesis, phosphotransferase system, \u003cb\u003eFig. S7B\u003c/b\u003e), COG functional classes (translation and ion transport, \u003cb\u003eFig. S7C\u003c/b\u003e) and GO biological processes (nucleic-acid and macromolecule metabolism, \u003cb\u003eFig. S7D\u003c/b\u003e) that differentiated resistant from sensitive isolates. Collectively, these results indicate that 5-FU\u0026ndash;resistant isolates exhibit a distinct functional composition characterised by reduced representation of broad metabolic categories and increased representation of transport-, stress-response-, cell-envelope-, and regulatory-associated modules.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we systematically characterised bacterial 5-FU resistance among \u003cem\u003eStreptococcus\u003c/em\u003e isolates from HNC patients and show that 5-FU resistance is common, occurring in approximately two-thirds of isolates (69/101) examined. Resistant phenotypes were observed across diverse \u003cem\u003eStreptococcus\u003c/em\u003e species and did not occur more frequently in tumour-associated samples than in adjacent oral mucosa. This distribution suggests that bacterial 5-FU tolerance is more likely to reflect pre-existing functional diversity within the oral microbiome, with genomic analysis identifying genetic features potentially associated with resistance.\u003c/p\u003e \u003cp\u003ePrevious work analysing the sensitivity of oral bacteria to 5-FU including multiple \u003cem\u003eStreptococcus\u003c/em\u003e species, suggested that \u003cem\u003eS. mitis\u003c/em\u003e, \u003cem\u003eS. oralis\u003c/em\u003e and \u003cem\u003eS. salivarius\u003c/em\u003e exhibit intrinsic tolerance to 5-FU\u003csup\u003e89\u003c/sup\u003e. By analysing multiple strains per species, we extend these observations by demonstrating substantial within-species heterogeneity, with isolates of the same species displaying different levels of 5-FU sensitivity. Consistent with this, 5-FU resistance status and anatomical site minimally contributed to accessory genome diversity in our pangenome analyses. Resistant and sensitive isolates showed substantial overlap in genus-level ordinations, indicating that acquisition of 5-FU tolerance is not associated with large-scale reconfiguration of overall gene content. Species-specific analysis further revealed only modest and heterogeneous resistance-associated signals. These findings are consistent with previous bacterial genetic screens demonstrating that 5-FU resistance can arise through multiple distinct genetic mechanisms\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePyrimidine metabolism, mediated by the \u003cem\u003epre-TA\u003c/em\u003e operon, is a key resistance mechanism across many bacterial taxa, prevalent within the \u003cem\u003eFirmicutes\u003c/em\u003e phylum\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In contrast, our genomic analysis show that \u003cem\u003eStreptococcus\u003c/em\u003e isolates lack the \u003cem\u003epre-TA\u003c/em\u003e operon, consistent with prior reports\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e and further supported by the absence of detectable conversion of 5-FU to DHFU in our samples (data not shown). Instead, resistant isolates in our cohort were enriched for genes associated with multidrug transport (\u003cem\u003elmrB\u003c/em\u003e and \u003cem\u003eybhR)\u003c/em\u003e, stress response and dormancy (\u003cem\u003estbD)\u003c/em\u003e, DNA repair (\u003cem\u003esbcDI)\u003c/em\u003e, capsule and cell-envelope biosynthesis, and redox balance (c\u003cem\u003epsE, wcaJ, epsL, wbbJ\u003c/em\u003e) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan additionalcitationids=\"CR92 CR93 CR94 CR95 CR96 CR97 CR98\" citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e. Rather than representing discrete resistance determinants, the distribution of these loci supports a multifactorial resistance strategy centred on stress adaptation, envelope modulation, and metabolic buffering. Functional profiling across KEGG, GO, and COG categories corroborated these gene-level signals, with resistant isolates exhibiting relative enrichment of transport systems (ABC-2 permeases, MFS pumps), regulatory functions (sigma factors, \u003cem\u003eAgrA\u003c/em\u003e-like), DNA repair/replication modules (mismatch repair, Okazaki processing), and cell-envelope functions, alongside modest enrichment of defence and detoxification functions\u003csup\u003e\u003cspan additionalcitationids=\"CR101 CR102 CR103\" citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. Notably, similar pathway level signatures have been reported in prior \u003cem\u003ein vitro\u003c/em\u003e evolution and multi-omic studies of bacterial 5-FU resistance supporting the broad relevance of these adaptive processes\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur genomic analysis revealed two additional features associated with our resistant isolates. First, extending on our previous analysis showing enrichment for ICEs in HNC-associated bacterial genomes, we found that several 5-FU associated resistance genes, most notably the cation efflux transporter \u003cem\u003efief\u003c/em\u003e, were frequently embedded within ICEs in resistant isolates suggesting HGT mediated dissemination of these genes\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Second, resistant isolates were enriched for genes commonly implicated in virulence-related functions, including host tissue interaction and immune evasion (\u003cem\u003eply\u003c/em\u003e), iron uptake (\u003cem\u003epiuA)\u003c/em\u003e, and predicted adhesins \u003csup\u003e\u003cspan additionalcitationids=\"CR67 CR68 CR69 CR70\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Here, these loci likely reflect general stress tolerance, surface remodelling, and ecological competitiveness rather than direct pathogenicity. Notably, in \u003cem\u003eS. mitis\u003c/em\u003e, resistant isolates showed the enrichment of the \u003cem\u003eblpS\u0026ndash;lyrR\u0026ndash;lytT\u003c/em\u003e bacteriocin operon, which mediates quorum-sensing-based bacteriocin production involved in interbacterial competition\u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e,\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e. Collectively, these suggest that 5-FU resistant strains not only can tolerate chemotherapeutic stress but also harbour genetic features that influence microbial interactions and local microenvironment dynamics.\u003c/p\u003e \u003cp\u003eConversely, sensitive isolates maintained greater representation of core anabolic pathways, including amino acid, carbohydrate, nucleotide, and lipid biosynthesis consistent with a growth-oriented physiological profile\u003csup\u003e\u003cspan additionalcitationids=\"CR101 CR102 CR103\" citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. This pattern suggest that resistant isolates prioritise specialised modules for drug export, stress resilience, and ecological persistence, potentially at the expense of broad metabolic versatility\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e,\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first comprehensive study to characterise 5-FU resistance profiles in \u003cem\u003eStreptococcus\u003c/em\u003e isolates from HNC patients. While 5-FU is commonly administered in combination with platinum-based chemotherapy (e.g. cisplatin) in metastatic HNC, patients in our cohort were treated with curative-intent surgery and are therefore unlikely to have received prior 5-FU exposure, suggesting that the resistance profiles observed are unlikely to reflect direct selection from previous 5-FU treatment\u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e. Instead, these findings raise the possibility that features of the oral and tumour-associated microenvironments may contribute to the enrichment of 5-FU-tolerant populations, or that 5-FU resistance is inherently prevalent across oral microbiomes. A limitation of this study is the absence of a matched healthy control isolates, which precludes direct assessment of these possibilities. Furthermore, while our analyses identify genetic features associated with resistance, establishing causality will require future studies incorporating transcriptomic and proteomic approaches to confirm pathway activation in response to 5-FU exposure, as well as targeted gene-knockout studies to assess loss of resistance phenotypes. Moreover, reliance on existing functional annotation databases may have biased pathway-level interpretation, as uncharacterised genes assigned to COG category S (function unknown) were common in our dataset and may harbour novel resistance mechanisms not captured in the current study.\u003c/p\u003e \u003cp\u003eFinally, our study has important clinical implications in the broader cancer context. 5-FU remains one of the most widely used chemotherapeutic agents for the treatment of multiple malignancies and is a well-recognised contributor to oral mucositis, with detectable levels present in salivary secretions following treatment \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR111\" citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. While prior work has largely focused on chemotherapy-induced tissue injury, increasing evidence indicates that disruption of the oral microbiome plays a significant role in the severity and persistence of mucositis\u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e. Our findings suggest that exposure to chemotherapeutic pressure may preferentially select for oral bacterial populations with enhanced stress tolerance, survival capacity, and pathogenic potential. Given that oral infections are a common complication during chemotherapy and that oral cavity infection represents a potential source of microbial translocation into the bloodstream and distant tissues, including tumours, the emergence of such resistant populations may have implications for infection risk, treatment tolerance, and patient outcomes\u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e,\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we report for the first time the 5-FU resistance profiles of HNC-associated \u003cem\u003eStreptococcus\u003c/em\u003e isolates. We show that 5-FU resistance is highly prevalent within this genus and suggest that chemotherapy may select for bacterial strains with increased virulence potential, leading to shifts in the oral microbial community. These bacteria may not only persist in chemotherapy-altered environments but also carry virulence genes that can actively shape their local niche. Our findings highlight the potential value of microbiome-directed monitoring or supportive interventions alongside chemotherapy, while underscoring the need for further investigation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Population and Sample Collection\u003c/h2\u003e\n \u003cp\u003eMicrobial isolates analysed in this study were derived from the same cohort of 31 patients with head and neck cancer (HNC) previously described in our earlier work\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Patients underwent surgical resection at three hospitals in Adelaide, South Australia, between 2021 and 2022 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e,\u0026nbsp;\u003cstrong\u003eSupplementary Data S1\u003c/strong\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Head and Neck Cancer Patient Characteristics.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCancer Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Age\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSmoking (Yes)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlcohol (Yes)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStages (Top Counts)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOropharyngeal SCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III: 2; Stage IVa (Recurrent): 1; Unknown: 1; Stage IVb: 1; Stage I: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaryngeal SCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown: 2; Stage Ivb: 1; Stage IVa: 1; Stage III: 1; Stage IVb: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOral Tongue SCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown: 1; Stage IVa: 1; Stage III: 1; Stage II: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHNC Unspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown: 1; Stage I: 1; Stage IVa: 1; Stage II: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOral SCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III: 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFloor of Mouth SCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage II: 1; Unknown: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuccal Mucosa SCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown: 1; Stage III: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSalivary Gland Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IVa (T4a): 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther HNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSinonasal Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGingiva/Alveolar Ridge SCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IVa: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNasopharyngeal Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTumour tissue and matched macroscopically normal oral mucosal samples were collected intraoperatively under sterile conditions. All samples were transported on ice and processed within 24 hours of collection. Written informed consent was obtained from all participants prior to surgery. Ethical approval was granted by the Central Adelaide Local Health Network Human Research Ethics Committee (CALHN Ref No. 14116). All samples were de-identified prior to downstream analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBacterial Isolation and Culturing\u003c/h2\u003e\n \u003cp\u003eThe isolation and taxonomic characterization of bacterial strains from tumour and oral cavity samples of 31 HNC patients were previously described in detail\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Briefly, oral swabs and surgically resected tumour tissues were processed under aerobic and anaerobic conditions using a range of selective and non-selective media. Colonies were subcultured for purity, identified by MALDI-TOF mass spectrometry, and cryopreserved at \u0026minus;\u0026thinsp;80\u0026deg;C. Whole-genome sequencing (WGS) of all isolates was performed using a hybrid approach combining Oxford Nanopore long-read and Illumina short-read sequencing platforms, and genome assemblies were functionally annotated as described in our earlier study\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eFor this study, a subset of \u003cem\u003eStreptococcus\u003c/em\u003e isolates was selected from the existing WGS dataset for further phenotypic and functional analyses. The selection was guided by taxonomic assignment (via MALDI-TOF and WGS), clinical source (tumour vs oral), and the high prevalence of \u003cem\u003eStreptococcus\u003c/em\u003e species in the HNC microbiome observed in our dataset\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Selected isolates were revived from glycerol stocks and cultured under appropriate conditions for minimum inhibitory concentration (MIC) testing. Aerobically recovered \u003cem\u003eStreptococcus\u003c/em\u003e isolates were grown on tryptic soy\u0026ndash;based media (TBS) and tryptic soy\u0026ndash;based agar (TBA) under standard aerobic conditions, while tumour-derived anaerobic isolates were cultured on Wilkins\u0026ndash;Chalgren agar and broth (WCA/WCB) in an anaerobic chamber at 37\u0026deg;C (oxygen-free environment).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eGenome Annotation and Functional Analysis\u003c/h3\u003e\n\u003cp\u003eWGS of \u003cem\u003eStreptococcus\u003c/em\u003e isolates used in this study were generated as part of larger HNC isolate collection described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Genomes had been assembled using a hybrid sequencing approach combining Oxford Nanopore long reads and Illumina short reads, as described previously\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For the present study, bioinformatic analyses focused on characterizing genomic determinants of 5-fluorouracil (5-FU) resistance.\u003c/p\u003e\n\u003cp\u003eGenome annotation was performed using Prokka v1.14.6\u003csup\u003e25\u003c/sup\u003e, followed by refinement with BLASTX v2.13.0\u0026thinsp;+\u0026thinsp;\u003csup\u003e26\u003c/sup\u003e against curated databases including the Comprehensive Antibiotic Resistance Database (CARD v3.2.7)\u003csup\u003e27\u003c/sup\u003e, Virulence Factor Database (VFDB; accessed March 2024)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, PlasmidFinder v2.1\u003csup\u003e29\u003c/sup\u003e, and custom databases related to chemoresistance, fluoropyrimidine metabolism, and mobile genetic elements. Gene presence/absence matrices were generated using Panaroo v1.3.4\u003csup\u003e30\u003c/sup\u003e and used for comparative genomic analysis between resistant and sensitive isolates. Specific attention was given to genes implicated in 5-FU resistance, including the \u003cem\u003epreTA\u003c/em\u003e operon, \u003cem\u003eDPYD\u003c/em\u003e homologs, uracil salvage pathway genes (\u003cem\u003eupp\u003c/em\u003e, \u003cem\u003etdk\u003c/em\u003e, \u003cem\u003eudk\u003c/em\u003e), and efflux pump components\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Annotations were cross-referenced with isolate metadata, including species, anatomical origin (tumour vs oral), and MIC-derived susceptibility phenotypes (defined in section below) to identify patterns associated with drug resistance.\u003c/p\u003e\n\u003ch3\u003eMinimum Inhibitory Concentration (MIC) Assay\u003c/h3\u003e\n\u003cp\u003eThe susceptibility of \u003cem\u003eStreptococcus\u003c/em\u003e isolates to 5-fluorouracil (5-FU) was assessed using a broth microdilution method. Aerobic isolates were cultured overnight in Tryptic Soy Broth (TSB), whereas anaerobic isolates were grown in Wilkins\u0026ndash;Chalgren Broth (WCB). Cultures were inoculated into 96-well plates containing two-fold serial dilutions of 5-FU ranging from 8 \u0026micro;g/mL to 1,000 \u0026micro;g/mL. Aerobic plates were incubated at 37\u0026deg;C under ambient atmospheric conditions, and anaerobic plates were incubated at 37\u0026deg;C in an oxygen-free chamber.\u003c/p\u003e\n\u003cp\u003eBacterial growth was assessed at 8 h, 16 h, 24 h and 48 h by both visual inspection and optical density (OD) measurement. The minimum inhibitory concentration (MIC) was defined as the lowest 5-FU concentration that completely inhibited visible growth, consistent with standard broth microdilution practices\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. For visualisation, MIC values were log₂-transformed prior to plotting. Isolates showing no growth at the lowest tested concentration (8 \u0026micro;g/mL) were assigned an MIC of 8 \u0026micro;g/mL for graphical display. To categorize phenotypes, isolates were classified as resistant if growth was observed at or above 64 \u0026micro;g/mL, which corresponded to the median MIC value across all tumour- and oral-derived isolates. This threshold provided a biologically meaningful midpoint in the overall susceptibility distribution and aligns with previously reported intermediate resistance levels in gut commensals\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. OD measurements were used to confirm growth inhibition kinetics and ensure consistency across time points but were not used to redefine MIC values. All MIC assays were performed in biological triplicates.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePathway and Functional Enrichment Analysis\u003c/h2\u003e\n \u003cp\u003eFunctional annotation of all coding sequences was performed using eggNOG-mapper (v2.1.9)\u003csup\u003e38\u003c/sup\u003e to assign Cluster of Orthologous Groups (COG) categories and KEGG orthologs (KOs). Enrichment analyses were conducted separately for 5-FU-resistant and -sensitive groups using the clusterProfiler package in R (v4.2.2) \u003csup\u003e39\u003c/sup\u003e. Overrepresented functions were determined using Fisher\u0026rsquo;s exact test with Benjamini\u0026ndash;Hochberg correction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Gene-level annotations were aggregated into pathway categories including nucleotide metabolism, DNA repair, transport systems, and transcriptional regulation. Functional differences were visualized using bar plots, dot plots, and heatmaps generated with ggplot2 and ComplexHeatmap\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were conducted in R (version 2024.12.1\u0026thinsp;+\u0026thinsp;563) and GraphPad Prism (version 10.4.0). Minimum inhibitory concentration (MIC) values were compared between groups using non-parametric Wilcoxon rank-sum tests. For categorical gene presence/absence data, enrichment was assessed using Fisher\u0026rsquo;s exact test or chi-square test where appropriate. Multiple testing correction was applied using the Benjamini\u0026ndash;Hochberg method. Principal coordinates analysis (PCoA), volcano plots, and violin plots were generated to visualize genomic features stratified by resistance phenotype.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental data are included in the supplementary information.\u0026nbsp;Microbial genomic sequencing data generated in this study are available in the NCBI BioProject database under accession number\u0026nbsp;\u003cstrong\u003ePRJNA1403646\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.M.: Conceptualization, Methodology, Investigation, Data Analysis, Writing – Original draft, Writing – review and editing. G.B.: Investigation, Data Analysis, Writing – Original draft, Writing – review and editing. K.Y, E.B, B.K. : Investigation, Writing – review and editing. J.H: Resources, Writing – review and editing. P.W., R.V., A.P., S.V.: Resources, Writing – review and editing, Supervision, Funding Acquisition. K.F: Conceptualization, Investigation, Writing – Original draft, Writing – review and editing, Supervision, Project Administration, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by an HSCGB Ray and Shirl Norman Cancer Research Grant (A.P., K.F., S.V., and R.V.), an NHMRC investigator grant APP1196832 (P.W.), a The Garnett Passe and Rodney Williams Senior Fellowship (S.V.), a Cancer Council SA Research Fellowship (K.F) and a The University of Adelaide Research Training Program Scholarship (L.M). We would like to thank the medical staff from The Royal Adelaide Hospital and The Memorial Hospital for their assistance in sample collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval for the collection and storage of patient samples was granted by Central Adelaide Local Health Network Human Research Ethics Committee (Adelaide, South Australia) (HREC MYIP14116), and all patients had signed written informed consent.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLongley, D. B., Harkin, D. P. \u0026amp; Johnston, P. 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Oral microbiota dysbiosis accelerates the development and onset of mucositis and oral ulcers. \u003cem\u003eFront Microbiol\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1061032 (2023). https://doi.org/10.3389/fmicb.2023.1061032\u003c/li\u003e\n \u003cli\u003eAbed, J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Colon Cancer-Associated Fusobacterium nucleatum May Originate From the Oral Cavity and Reach Colon Tumors via the Circulatory System. \u003cem\u003eFrontiers in Cellular and Infection Microbiology\u003c/em\u003e \u003cstrong\u003eVolume 10 - 2020\u003c/strong\u003e (2020). https://doi.org/10.3389/fcimb.2020.00400\u003c/li\u003e\n \u003cli\u003eStephen T. Sonis, D. a. J. W. C., Jr, DMD. \u003cem\u003eOral Complications of Cancer Chemotherapy\u003c/em\u003e. Vol. 6th edition (2003).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"5-Fluorouracil, Streptococcus, Head and Neck Cancer, Oral microbiome, Cancer microbiome Chemoresistance","lastPublishedDoi":"10.21203/rs.3.rs-8825984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8825984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe chemotherapeutic agent 5-fluorouracil (5-FU), widely used in the treatment of head and neck cancer (HNC), also exhibits broad antimicrobial activity, yet fluoropyrimidine resistance within HNC-associated microbiota remains poorly characterised. We assessed 5-FU susceptibility and resistance-associated genomic features in 101 \u003cem\u003eStreptococcus\u003c/em\u003e isolates obtained from tumour tissue and oral swabs of 31 HNC patients using minimum inhibitory concentration assays integrated with whole-genome sequencing and pangenome analysis. Resistance to 5-FU was prevalent across multiple \u003cem\u003eStreptococcus\u003c/em\u003especies and was primarily associated with species identity rather than resistance phenotype or anatomical niche. Resistant isolates showed functional convergence in pathways related to multidrug efflux, stress response, DNA repair, cell-envelope biosynthesis, and virulence, whereas sensitive isolates were enriched for genes involved in core metabolism, nutrient acquisition, and colonisation. Species-resolved analyses revealed heterogeneous, polygenic resistance architectures rather than conserved resistance determinants. Together, these findings suggest that 5-FU exposure may act as an ecological selective pressure shaping microbial functional potential within tumour- and oral communities in HNC.\u003c/p\u003e","manuscriptTitle":"Genomic Signatures and Functional Pathways Underlying 5-Fluorouracil Resistance in Head and Neck Cancer associated Streptococci","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 09:18:34","doi":"10.21203/rs.3.rs-8825984/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b2bb9d0-9024-4e0f-8bbe-8ddc5b0786f6","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63381389,"name":"Biological sciences/Microbiology/Microbial genetics/Bacterial genetics"},{"id":63381390,"name":"Biological sciences/Cancer/Head and neck cancer/Oral cancer"},{"id":63381391,"name":"Biological sciences/Microbiology/Clinical microbiology"}],"tags":[],"updatedAt":"2026-02-25T09:18:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 09:18:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8825984","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8825984","identity":"rs-8825984","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}