Salivary Microbiome(唾液微生物组)研究综述
Salivary Microbiome 唾液微生物组 - To address this, we performed 16S rRNA gene sequencing to investigate the salivary microbiome in 85 patients with drug-naïve first-episode schizophrenia (FES), 43 individuals at CHR, and 80 healthy controls (HCs). [1] Background Emerging evidence demonstrates that the salivary microbiome could serve as a biomarker for various diseases. [2] The core salivary microbiome comprised nine genera (Actinomyces, Capnocytophaga, Gemella, Granulicatella, Lachnoanaerobaculum, Neisseria, Porphyromonas, Rothia,and Streptococcus). [3] Fecal microbiome from AxSpA patients showed a trend towards increased alpha diversity of the IgA+ fraction and decreased diversity in the IgA− fraction in comparison with HCs, while the salivary microbiome exhibits a significant decrease in alpha diversity in both IgA+ and IgA− fractions. [4] Here, we aimed to examine the salivary microbiomes of children with AAE and assess the relationship with adrenal androgens and metabolic parameters. [5] Although a relationship between the inhabited microbiome and carcinogenesis has been proposed, no detailed information regarding the oral microbiome of patients with OSF exists; the changes of the salivary microbiome during cancer formation remain unclear. [6] ABSTRACT Background: A few recent studies have characterized the salivary microbiome in association with Autism Spectrum Disorder (ASD). [7] This study examines the structure and composition of the salivary microbiome for the first time in young adults who met the DSM-IV criteria for depression (n = 40) and matched controls (n = 43) using 16S rRNA gene-based next generation sequencing. [8] Our results demonstrate that, within the limits of our cohort, IgA role is not critical for maintaining a rather functional salivary microbiome and suggest that IgA is not a major influence on the composition of abundant commensal microbes. [9] Alterations in gut microbiota have been explored in Celiac Disease (CD), but fewer studies investigated the characteristics of salivary microbiome in these patients, despite the potential implications in its pathogenesis. [10] After removal of dead bacteria by propidium monoazide (PMA), changes in the profile of salivary microbiome were detected using 16S rRNA sequencing technology, and differences among age groups were compared subsequently. [11] We showed for the first time that the esophageal microbiome is distinct from the salivary microbiome and the enrichment of Campylobacter species as a consistent signature in disease across two independent cohorts. [12] We also investigated the potential of characterizing individual salivary microbiomes from non‐human DNA fragments found in saliva. [13] We hypothesized that presence of GERD was linked to a modified microbial profile in untreated GERD patients and that the use of proton pump inhibitor (PPI) drugs: potent disruptors of gut microbiome, in GERD patients might result in a salivary microbiome that is further distinct. [14] Salivary microbiome assessment has emerged as a potential non-invasive tool to identify patients at risk for esophageal cancer, but key host and environmental factors that may affect the salivary microbiome have not been well-defined. [15] The present study used 16S rRNA gene amplicon sequencing to assess the impact on salivary microbiome of different grades of dental and periodontal disease and the combination of both (hereinafter referred to as oral disease), in terms of bacterial diversity, co-occurrence network patterns and predictive models. [16] Here, we performed 16S ribosomal RNA gene sequencing of the fecal and salivary microbiomes in patients under different long-term ART. [17] The test group demonstrated significantly lower microbial richness and diversity, and less abundant Porphyromonas than the control group in at three months for both subgingival microbiome and salivary microbiome. [18] Hence, the aim of the present study was to structurally and functionally profile the salivary microbiome of 103 women in reproductive age with regular menstrual cycle, while evaluating the modifying influences of hormonal contraceptives, sex hormones, diet, and smoking. [19] (2021) provided new insights into salivary microbiome and microbial metabolite alterations during the malignant transformation of oral submucous fibrosis (OSF). [20] However, the salivary microbiome is yet unexplored in PSC. [21] While the underlying causes of innate immune dysregulation are incompletely understood, genetics, sex hormones, infections, and alterations in both the gut and salivary microbiome likely contribute to disease susceptibility. [22] Therefore, we investigated the fecal and salivary microbiomes of BD patients compared to those of recurrent aphthous ulcer (RAU) patients, as well as dietary habit-matched healthy controls (HCs) selected from immediate family members using 16S rRNA gene sequencing. [23] Conclusions Core of the salivary microbiome and genera diversity are dependent on the sequencing approaches. [24] The potential role of the salivary microbiome in human diseases has increasingly been explored. [25] Evaluating the effect of metabolic syndrome on the salivary microbiome, data presented herein support the hypothesis that the salivary bacterial profile is altered in metabolic syndrome patients compared with healthy patients. [26] The salivary microbiome is comprised of indigenous oral microorganisms that are specific to each person, exhibits long-term stability. [27] The objective of this study was to investigate the potential roles of the free salivary microbiome in different periodontal statuses, their reaction to nonsurgical periodontal therapy, and differences between diseased individuals after treatment and healthy persons. [28] Linear discriminant analysis of Bray-Curtis dissimilarity distances revealed significant class separation between the salivary microbiome and aerosol microbiota deposited on the operator, patient, assistant, or the environment (P < 0. [29] albicans in saliva and dental plaque of children with varying caries statuses, as well as the salivary microbiome of these children. [30] The salivary microbiome is influenced by biofilm of shedding (epithelial) and non-shedding (tooth) surfaces. [31] This study aims to test the ability of salivary microbiome and electrolytes in diagnosing ECC, and their interplays within the same population. [32] This review discusses the current and emerging insights into vaginal, skin, and salivary microbiome-modulating factors during life (e. [33] This study aimed to characterize the oral phenotype, including salivary parameters, and the salivary microbiome of three PLS sisters, comparatively. [34] This study aimed to explore the association between the salivary microbiome in adults and serum TSH levels. [35] Background Many factors can contribute to the exact makeup of the salivary microbiome. [36] Salivary microbiome diversity and composition were analyzed so as to establish a diagnostic model for type 2 diabetes. [37] Furthermore, next-generation sequencing was used to investigate the salivary microbiome. [38] Our findings pave the way for further hypothesis testing to gain insight into the association between host factors and salivary microbiome. [39] The putative biological functions of the salivary microbiome of the different groups were predicted by PICRUSt. [40] No significant differences were found regarding the salivary microbiome. [41] This study determined the ability of Streptococcus salivarius to inhibit IL-6 and IL-8 production by gingival fibroblasts when activated by periodontal pathogens and their effect on the salivary microbiome. [42] Salivary microbiomes of the same persons were characterized by shotgun metagenome sequencing. [43] With the background of the salivary microbiome as a rich source of biomarkers for systemic diseases, we herein primarily aimed to investigate the salivary microbiome as a tool for the non-invasive diagnosis of IgA nephropathy. [44] In this study, we explored the association between the salivary microbiome and the concentration of IL-1β, IL-6 and IL-8 in the saliva of 12 healthy adults over a period of 24 h by studying the 16S rRNA gene followed by negative binomial mixed model regression analysis. [45] Gut and salivary microbiome, plasma vitamins, metals, amino acids, and hormones showed associations with the vagino-cervical microbiome. [46] No significant differences were found regarding the salivary microbiome. [47] METHODS We recruited 105 subjects (peanut allergic n=56, healthy subjects n=49) for salivary microbiome profiling using 16S rRNA sequencing, short chain fatty acid (SCFA) metabolite assays using liquid chromatography/mass spectrometry, and measurement of oral secreted cytokines using multiplex assays. [48] Examining the impact of Th17:Treg ratios (determined by epigenetic qPCR lymphocyte subset quantification) on the IgA-Biome across diabetes phenotypes identified a proportional relationship between Th17:Treg ratios and alpha diversity in the stool IgA-Biome of those with dysglycemia, significant changes in community composition of the stool and salivary microbiomes across glycemic profiles, and genera preferentially abundant by T-cell inflammatory phenotype. [49] albicans in saliva and dental plaque of children with varying caries statuses, and their salivary microbiome. [50]为了解决这个问题，我们进行了 16S rRNA 基因测序，以研究 85 名未接受药物治疗的首发精神分裂症 (FES) 患者、43 名 CHR 患者和 80 名健康对照 (HC) 患者的唾液微生物组。 [1] 背景 新出现的证据表明，唾液微生物组可以作为各种疾病的生物标志物。 [2] 核心唾液微生物群包括九个属（放线菌属、二氧化碳吞噬菌属、双胞菌属、粒状菌属、厌氧杆菌属、奈瑟菌属、卟啉单胞菌属、罗氏菌属和链球菌属）。 [3] 与 HCs 相比，来自 AxSpA 患者的粪便微生物组显示出 IgA+ 部分的 alpha 多样性增加和 IgA- 部分的多样性降低的趋势，而唾液微生物组的 IgA+ 和 IgA- 部分的 alpha 多样性显着降低。 [4] 在这里，我们旨在检查 AAE 儿童的唾液微生物组，并评估与肾上腺雄激素和代谢参数的关系。 [5] 尽管有人提出了居住微生物组与致癌作用之间的关系，但没有关于 OSF 患者口腔微生物组的详细信息；癌症形成过程中唾液微生物组的变化仍不清楚。 [6] 摘要背景：最近的一些研究已经描述了与自闭症谱系障碍 (ASD) 相关的唾液微生物组。 [7] 本研究首次使用基于 16S rRNA 基因的下一代测序在满足 DSM-IV 抑郁症标准 (n = 40) 和匹配对照 (n = 43) 的年轻人中检查唾液微生物组的结构和组成。 [8] 我们的结果表明，在我们的队列范围内，IgA 的作用对于维持功能相当强大的唾液微生物组并不重要，并表明 IgA 对丰富的共生微生物的组成不是主要影响。 [9] 已经在腹腔疾病 (CD) 中探索了肠道微生物群的改变，但很少有研究调查这些患者唾液微生物群的特征，尽管其发病机制可能具有潜在影响。 [10] 通过单叠氮化丙啶（PMA）去除死菌后，使用16S rRNA测序技术检测唾液微生物组谱的变化，并随后比较年龄组之间的差异。 [11] 我们首次表明，食管微生物组不同于唾液微生物组，并且弯曲杆菌属物种的富集是两个独立队列中疾病的一致特征。 [12] 我们还研究了从唾液中发现的非人类 DNA 片段表征单个唾液微生物组的潜力。 [13] 我们假设 GERD 的存在与未经治疗的 GERD 患者的微生物谱改变有关，并且质子泵抑制剂 (PPI) 药物的使用：GERD 患者肠道微生物组的强效破坏物可能导致唾液微生物组更加明显。 [14] 唾液微生物组评估已成为一种潜在的非侵入性工具，可用于识别有食管癌风险的患者，但可能影响唾液微生物组的关键宿主和环境因素尚未明确定义。 [15] 本研究采用 16S rRNA 基因扩增子测序，从细菌多样性、共现网络模式和预测模型。 [16] 在这里，我们对接受不同长期 ART 的患者的粪便和唾液微生物组进行了 16S 核糖体 RNA 基因测序。 [17] 在三个月内，试验组的牙龈下微生物组和唾液微生物组的微生物丰富度和多样性显着低于对照组，而卟啉单胞菌的丰度低于对照组。 [18] 因此，本研究的目的是在结构和功能上分析 103 名具有规律月经周期的育龄妇女的唾液微生物组，同时评估激素避孕药、性激素、饮食和吸烟的改变影响。 [19] （2021）为口腔黏膜下纤维化（OSF）恶性转化过程中唾液微生物组和微生物代谢物的变化提供了新的见解。 [20] 然而，唾液微生物组在 PSC 中尚未被探索。 [21] 虽然先天免疫失调的根本原因尚不完全清楚，但遗传、性激素、感染以及肠道和唾液微生物组的改变可能会导致疾病易感性。 [22] 因此，我们使用 16S rRNA 基因测序研究了 BD 患者的粪便和唾液微生物组与复发性口疮 (RAU) 患者的粪便和唾液微生物组，以及从直系亲属中选择的饮食习惯匹配的健康对照 (HCs)。 [23] 结论 唾液微生物组的核心和属多样性取决于测序方法。 [24] 人们越来越多地探索唾液微生物组在人类疾病中的潜在作用。 [25] 评估代谢综合征对唾液微生物组的影响，本文提供的数据支持这样的假设，即与健康患者相比，代谢综合征患者的唾液细菌谱发生了改变。 [26] 唾液微生物组由每个人特有的本地口腔微生物组成，具有长期稳定性。 [27] 本研究的目的是调查游离唾液微生物组在不同牙周状态中的潜在作用，它们对非手术牙周治疗的反应，以及治疗后患病个体与健康人之间的差异。 [28] Bray-Curtis 相异距离的线性判别分析揭示了唾液微生物群和沉积在操作员、患者、助手或环境中的气溶胶微生物群之间的显着类别分离（P < 0. [29] 不同龋齿状态儿童唾液和牙菌斑中的白色念珠菌，以及这些儿童的唾液微生物组。 [30] 唾液微生物组受脱落（上皮）和非脱落（牙齿）表面的生物膜的影响。 [31] 本研究旨在测试唾液微生物组和电解质在诊断 ECC 中的能力，以及它们在同一人群中的相互作用。 [32] 这篇综述讨论了对生活中阴道、皮肤和唾液微生物组调节因子的当前和新兴见解（例如。 [33] 本研究旨在比较表征三个 PLS 姐妹的口腔表型，包括唾液参数和唾液微生物组。 [34] 本研究旨在探讨成人唾液微生物组与血清 TSH 水平之间的关系。 [35] 背景 许多因素可以影响唾液微生物组的确切组成。 [36] 分析唾液微生物组的多样性和组成，以建立2型糖尿病的诊断模型。 [37] 此外，下一代测序被用于研究唾液微生物组。 [38] 我们的研究结果为进一步的假设检验铺平了道路，以深入了解宿主因素与唾液微生物组之间的关联。 [39] PICRUSt预测了不同组唾液微生物组的推定生物学功能。 [40] 唾液微生物组没有发现显着差异。 [41] 本研究确定了唾液链球菌在被牙周病原体激活时抑制牙龈成纤维细胞产生 IL-6 和 IL-8 的能力及其对唾液微生物组的影响。 [42] 同一个人的唾液微生物组通过鸟枪法宏基因组测序进行表征。 [43] 在唾液微生物组作为全身性疾病生物标志物丰富来源的背景下，我们主要旨在研究唾液微生物组作为 IgA 肾病非侵入性诊断的工具。 [44] 在本研究中，我们通过研究 16S rRNA 基因和负二项式研究了 24 小时内 12 名健康成人唾液中 IL-1β、IL-6 和 IL-8 浓度之间的关联。混合模型回归分析。 [45] 肠道和唾液微生物组、血浆维生素、金属、氨基酸和激素与阴道-宫颈微生物组有关。 [46] 唾液微生物组没有发现显着差异。 [47] 方法 我们招募了 105 名受试者（花生过敏 n=56，健康受试者 n=49）使用 16S rRNA 测序进行唾液微生物组分析，使用液相色谱/质谱法进行短链脂肪酸 (SCFA) 代谢物测定，并使用多重测量口腔分泌的细胞因子化验。 [48] 检查 Th17:Treg 比率（由表观遗传 qPCR 淋巴细胞亚群量化确定）对糖尿病表型 IgA-Biome 的影响确定了 Th17:Treg 比率与高血糖患者粪便 IgA-Biome 中的α多样性之间的比例关系，显着变化在跨血糖谱的粪便和唾液微生物群落的群落组成中，T 细胞炎症表型优先丰富的属。 [49] 不同龋齿状态儿童唾液和牙菌斑中的白色念珠菌及其唾液微生物组。 [50]
salivary microbiome may
CONCLUSION These changes in the salivary microbiome may have potential applications as a novel diagnostic tool for the early detection of OSCC. [1] Moreover, the composition of salivary microbiome may lead to the risk that the overweight group is at risk of future obesity. [2]salivary microbiome diversity
Salivary microbiome diversity and composition were analyzed so as to establish a diagnostic model for type 2 diabetes. [1] Salivary microbiome diversity was assessed longitudinally by 16S sequencing and the inverse Simpson index. [2]分析唾液微生物组的多样性和组成，以建立2型糖尿病的诊断模型。 [1] nan [2]