The salivary microbiota is altered in cervical dysplasia patients and influenced by conization

This study supports the correlation between salivary microbiota and cervical dysplasia suggests that smoking influences microbiota. Cervical cancer is fourth most commonly detected in women worldwide [1]. The World Health Organization recently launched a global strategy to accelerate elimination of [2, 3]. Despite availability vaccines for human papillomavirus (HPV) infection, which primary cause cancer, it important note current do not cover all oncogenic HPV types [4-6]. testing cytology (e.g., Pap smear) currently serve as early screening methods after histology can accurately identify occurrence stages dysplasia. have reported significantly increased false rate compared with invasive diagnostic [7]. Hence, worth searching potential novel biomarker improve accuracy detection. Early detection, broader surveillance, access medical care treatment would greatly assist prevention, especially low-income countries [8]. Emerging evidence vaginal correlate infection [9-12]. However, data related oral or are still limited. Whether play role at different how lifestyle evaluate. Poor hygiene tooth loss associated an risk squamous cell [13]. Several specific bacterial pathogens, including Fusobacterium, Campylobacter, Prevotella, Pseudomonas, Capnocytophaga, correlated lung, oral, esophageal, stomach, pancreatic, colorectal cancers [14-16]. Notably, saliva collection noninvasive procedure quicker, cheaper, more convenient patient processes such blood collection, cytology, testing. also serves systemic diseases diagnosis [14-19]. Other known factors include smoking, parity, immunodeficiency virus (HIV) [20]. Numerous studies shown significant factor developing abnormalities, [21, 22]. Furthermore, lifestyles, diet, alcohol intake, been suggested affect [23-27]. Herein, samples were collected from both individuals undergoing examination volunteers visiting dental clinic examinations. investigated its relation Additionally, microbes differed volunteer controls participants without pre- postconization (Figure 1A). through questionnaire was investigated, may potentially influence identified. Significantly decreased microbial community richness diversity observed when control group 1B, Supporting Information: Figure S1A). Moreover, predysplasia (+) postdysplasia (−) groups histological 1C, Figures S1B–D). A dissimilarity we (PANOSIM = 0.005) 0.002) 1D,E). no differences identified among (Supporting S1E). All tested negative. PERMANOVA analysis considered overall effect participants' lifestyles on beta found age could Tables S1 S2, S2). top five main genera species belong Streptococcus, Veillonella, Haemophilus, Actinomyces S3 S4). When comparing healthy participants, up 16 having relative abundance 1F). similar trend (−). Haemophlius Alloprevotella than those 1G). Based reconstruction unobserved states 2 (PICRUSt2) analysis, pathways immunodeficiency, viral carcinogenesis, proteoglycans retinoic acid-inducible gene I (RIG-I)-like receptor signaling changed predicted function 1H), well pre-ysplasia participants. high-relative-abundance genera, Streptococcus negatively cancer. While other Alloprevotella, positively carcinogenesis RIG-I-like (RLR) 1I). dysplasia, Haemophilus be latter S5A–B). higher S5C–D). area under curve (AUC) (p 0.0039), 0.0076), Prevotella 0.0350) reached 0.748, 0.730, 0.682, respectively, indicates these three support patients condition S5E–G). similarities samples, generated 2A). Type contained Actinomyces, closely interacted according our network analysis. 3 1 type major 2B–D). Participants had percentages 2E). 2F) low-grade intraepithelial lesion (LSIL) high-grade (HSIL) 2G). tightly interactive formed by indicating relationship their distinct contributions various outcomes 2D). comparisons further evaluated 3A–D S6). carried out consider composition Only altered 3D). In addition, group, bacteria, Aggregatibacter, 3E). Further, comparison Stomatobaculum 3F). RLR signaling, elevated (Figures 3G,H). ability alone 0.700 0.0370), help diagnose ones 3I). combining abundance, improved classification AUC 0.945 0.0003) conventional combined (AUC 0.928, p 0.0005) 3J–K). Since influenced diversity, revealed S2), alpha affected (both previous smokers) S7). No smoker nonsmoking S7A–D). smokers who never smoked S7E,F). distribution nonsmokers analyzed. (containing Actinomyces) S7G). Interestingly, bacteria nonsmokers, once again S7H,I). Our visited hospital examination. line earlier link kinds cancers, throat lung head neck [28-31]. We recovered conization before treatment. distinguished diversities By using eHOMD database, abundant parainfluenzae, Veillonella atypica, Rothia mucilaginosa present study, publications [32-34]. identified, percentage Prevotella-enriched Haemophilus-enriched group. key 2, positive showed increase past distinguish them distinguishing patients. Similar roles [14-19] results expanded use Elevated levels linked diseases, periodontal disease, tonsillar cavity carcinoma [35, 36]. These same gout, obesity, cardiovascular liver [37-41]. suppurative, granulomatous inflammatory lesions induce cervicofacial, pulmonary, abdominopelvic infections, [42-45]. imply four opportunistic pathogens reactions development [16, 39, 46-57]. has previously exudates [10, 58, 59]. finding transfer tracts. Oral sexual behavior one routes viruses tract. Previous match co-occurring types, two sites [61]. intake Lactobacillus probiotics vagina [60]. metabolites immune cells induced microorganisms virulent strains enter circulation blood, leading inflammation promoting chronic organs beyond tract [62, 63]. differential immune-related pathways, revealing Toll Imd RLR, activity pretreatment groups. [64]. indicated activate Th1 Th17 cells, bacteremia [17, 63, 65, 66]. microbiota, supported [67-70]. gingivitis cigarette [67-71]. Cigarette independent 59, 72, 73]. pathway without, nonsmokers. linking inflammation, development. Further mechanistic longitudinal needed track complicated interactions talk There several limitations study. First, limited number complex situation recruited identification biomarkers, must interpreting results. distribution, living habits, antibiotics, [74]. difference considering habits confounders. Stomatobaculum, contribute recovery detailed conditions underwent examination, disease decayed, missing, filled (DMF) index, [75, 76]. itself selection biases result. Thus, presented. It noting taxonomic level 16S rRNA sequencing entirely reliable, PICRUSt2 predict fully reflect actual metagenome changes. provide insights into functions search biomarkers [77]. given development, believe valuable information additional targets smoking-related Last, experienced might representativeness state reference. provides first association Given certain multiple comparisons, lays foundation research Additional focusing changes tracts during urgently gain deeper understanding involvement demonstrated profiles conization. marker. cross-sectional involved 47 Karolinska University Hospital Stockholm, Sweden. this 28 due 19 follow-up HSIL 1A Table S5). 20 volunteered routine Sweden, included asked fill regarding approved Regional Ethical Board provided written informed consent take part Detailed sample available Supplementary material. DNA extracted 67 used genotyping MAGPIX instrument, published papers [6, 12, 78, 79]. V3–V4 regions genes amplified Illumina index-binding primer pairs 341F/805 R sequenced MiSeq platform [37]. Thereafter, analyzed QIIME2 Human Microbiome Database (eHOMD) (V15.2). about extraction, genotyping, sequencing, bioinformatic analyses Mann–Whitney U test adjusted Benjamini–Hochberg FDR performed compare KEGG Kruskal–Wallis Tukey–Kramer post hoc ANOSIM based Bray–Curtis distance matrices adonis package “vegan” [80, 81]. pairwise correlations < 0.05) generate co-occurrence (Spearman's rank coefficient indices > 0.5) heatmaps 0.4). receiver operating characteristic calculated analyze sensitivity specificity power. Conception design: Shengru Wu, Lars Engstrand, Sonia Andersson, Juan Du. Sample collection: Liqin Cheng, Alexandra A. L. Pennhag, Miriam Mints, Development methodology: Maike Seifert, Acquisition data: Unnur Guðnadóttir, Analysis interpretation Writing manuscript: Review and/or revision authors. Du Wu Swedish Research Council (2021-01683, 2021-06112), Foundation Strategic (SSF) [ICA16-0050], Svenska Läkaresällskapet [SLS-784981, SLS-960584], Institute Foundation. special acknowledgment loved Dr. passed away. very grateful her commitment dedication project. Ferring Pharmaceuticals funded center where project out. thank Centre Translational (CTMR), Institute, Fadia Alkass colleagues really appreciate funding agencies samples. authors declare conflict interest. Sweden (ethical permission 2017/725-31 2019-04201). paper. reads Sequence Read Archive (SRA) NCBI accession PRJNA863336 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA863336). materials (figures, tables, scripts, graphical abstract, slides, videos, Chinese translated version, updated materials) online DOI iMeta Science http://www.imeta.science/. Please note: publisher responsible content functionality any supporting supplied Any queries (other missing content) should directed corresponding author article.