Baraniya Divyashri, Do Thuy, Chen Tsute, Albandar Jasim M, Chialastri Susan M, Devine Deirdre A, Marsh Philip D, Al-Hebshi Nezar N
Oral Microbiome Research Laboratory, Maurice H. Kornberg School of Dentistry, Temple University, Philadelphia, PA, United States.
Division of Oral Biology, School of Dentistry, University of Leeds, Leeds, United Kingdom.
Front Microbiol. 2022 Nov 3;13:1031029. doi: 10.3389/fmicb.2022.1031029. eCollection 2022.
Modeling subgingival microbiome in health and disease is key to identifying the drivers of dysbiosis and to studying microbiome modulation. Here, we optimize growth conditions of our previously described subgingival microbiome model. Subgingival plaque samples from healthy and periodontitis subjects were used as inocula to grow normobiotic and dysbiotic microbiomes in MBEC assay plates. Saliva supplemented with 1%, 2%, 3.5%, or 5% (v/v) heat-inactivated human serum was used as a growth medium under shaking or non-shaking conditions. The microbiomes were harvested at 4, 7, 10 or 13 days of growth (384 microbiomes in total) and analyzed by 16S rRNA gene sequencing. Biomass significantly increased as a function of serum concentration and incubation period. Independent of growth conditions, the health- and periodontitis-derived microbiomes clustered separately with their respective inocula. Species richness/diversity slightly increased with time but was adversely affected by higher serum concentrations especially in the periodontitis-derived microbiomes. Microbial dysbiosis increased with time and serum concentration. and were substantially enriched in higher serum concentrations at the expense of , and . An increase in , and accompanied by a decrease in , and were the most prominent changes over time. Shaking had only minor effects. Overall, the health-derived microbiomes grown for 4 days in 1% serum, and periodontitis-derived microbiomes grown for 7 days in 3.5%-5% serum were the most similar to the respective inocula. In conclusion, normobiotic and dysbiostic subgingival microbiomes can be grown reproducibly in saliva supplemented with serum, but time and serum concentration need to be adjusted differently for the health and periodontitis-derived microbiomes to maximize similarity to inocula. The optimized model could be used to identify drivers of dysbiosis, and to evaluate interventions such as microbiome modulators.
对健康和疾病状态下的龈下微生物群进行建模，是识别生态失调驱动因素以及研究微生物群调节的关键。在此，我们优化了我们之前描述的龈下微生物群模型的生长条件。来自健康和牙周炎受试者的龈下菌斑样本被用作接种物，以在MBEC检测板中培养正常微生物群和失调微生物群。添加了1%、2%、3.5%或5%（v/v）热灭活人血清的唾液在振荡或非振荡条件下用作生长培养基。在生长4、7、10或l3天时收获微生物群（总共384个微生物群），并通过16S rRNA基因测序进行分析。生物量随血清浓度和培养时间的增加而显著增加。与生长条件无关，源自健康和牙周炎的微生物群与其各自的接种物分别聚集在一起。物种丰富度/多样性随时间略有增加，但受到较高血清浓度的不利影响，尤其是在源自牙周炎的微生物群中。微生物失调随时间和血清浓度的增加而增加。在较高血清浓度下，[具体微生物名称1]、[具体微生物名称2]和[具体微生物名称3]显著富集，而以[具体微生物名称4]、[具体微生物名称5]和[具体微生物名称6]为代价。[具体微生物名称7]、[具体微生物名称8]和[具体微生物名称9]的增加以及[具体微生物名称10]、[具体微生物名称11]和[具体微生物名称12]的减少是随时间最显著的变化。振荡的影响较小。总体而言，在1%血清中培养4天的源自健康的微生物群，以及在3.5%-5%血清中培养7天的源自牙周炎的微生物群与各自的接种物最为相似。总之，正常微生物群和失调的龈下微生物群可以在添加血清的唾液中可重复地生长，但对于源自健康和牙周炎的微生物群，需要不同地调整时间和血清浓度，以使与接种物的相似性最大化。优化后的模型可用于识别生态失调的驱动因素，并评估微生物群调节剂等干预措施。
