Masunova Nadezhda, Tereschenko Maria, Alexandrov Georgy, Spirina Liudmila, Tarasenko Natalia
Siberian State Medical University of the Ministry of Health of Russia, 634050, Tomsk, Russia.
Cancer Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Tomsk, Russia.
Curr Drug Targets. 2023;24(14):1139-1149. doi: 10.2174/0113894501257011231030161427.
Amelogenesis imperfecta (AI) refers to a heterogeneous group of conditions with multiple factors which contribute to the hypomineralisation of enamel. Preventive measures are necessary to predict this pathology. Prospects for preventive medicine are closely related to the search for new informative methods for diagnosing a human disease. MicroRNAs are prominent for the non-invasive diagnostic platform.
The aim of the review is to review the heterogeneous factors involved in amelogenesis and to select the microRNA panel associated with the AI type.
We used DIANA Tools (algorithms, databases and software) for interpreting and archiving data in a systematic framework ranging from the analysis of expression regulation from deep sequencing data to the annotation of miRNA regulatory elements and targets (https://dianalab. e-ce.uth.gr/). In our study, based on a gene panel associated with the AI types, twenty-four miRNAs were identified for the hypoplastic type (supplement), thirty-five for hypocalcified and forty-- nine for hypomaturation AI. The selection strategy included the microRNA search with multiple targets using the AI type's gene panel.
Key proteins, calcium-dependent and genetic factors were analysed to reveal their role in amelogenesis. The role of extracellular non-coding RNA sequences with multiple regulatory functions seems to be the most attractive. We chose the list of microRNAs associated with the AI genes. We found four microRNAs (hsa-miR-27a-3p, hsa-miR-375, hsa-miR-16-5p and hsamiR- 146a-5p) for the gene panel, associated with the hypoplastic type of AI; five microRNAs (hsa- miR-29c-3p, hsa-miR-124-3p, hsa-miR-1343-3p, hsa-miR-335-5p, and hsa-miR-16-5p - for hypocalcified type of AI, and seven ones (hsa-miR-124-3p, hsa-miR-147a, hsa-miR-16-5p, hsamiR- 429, hsa-let-7b-5p, hsa-miR-146a-5p, hsa-miR-335-5p) - for hypomaturation. It was revealed that hsa-miR-16-5p is included in three panels specific for both hypoplastic, hypocalcified, and hypomaturation types. Hsa-miR-146a-5p is associated with hypoplastic and hypomaturation type of AI, which is associated with the peculiarities of the inflammatory response immune response. In turn, hsa-miR-335-5p associated with hypocalcified and hypomaturation type of AI.
Liquid biopsy approaches are a promising way to reduce the economic cost of treatment for these patients in modern healthcare. Unique data exist about the role of microRNA in regulating amelogenesis. The list of microRNAs that are associated with AI genes and classified by AI types has been uncovered. The target gene analysis showed the variety of functions of selected microRNAs, which explains the multiple heterogeneous mechanisms in amelogenesis. Predisposition to mineralisation problems is a programmed event. Many factors determine the manifestation of this problem. Additionally, it is necessary to remember the variable nature of the changes, which reduces the prediction accuracy. Therefore, models based on liquid biopsy and microRNAs make it possible to take into account these factors and their influence on the mineralisation. The found data needs further investigation.
牙釉质发育不全（AI）是一组由多种因素导致牙釉质矿化不足的异质性疾病。预防措施对于预测这种病理状况很有必要。预防医学的前景与寻找诊断人类疾病的新的信息丰富的方法密切相关。微小RNA在无创诊断平台方面表现突出。
本综述的目的是回顾参与牙釉质形成的异质性因素，并选择与AI类型相关的微小RNA组。
我们使用DIANA Tools（算法、数据库和软件）在一个系统框架中解释和存档数据，该框架涵盖从深度测序数据的表达调控分析到miRNA调控元件和靶标的注释（https://dianalab.e-ce.uth.gr/）。在我们的研究中，基于与AI类型相关的基因组，确定了24个与发育不全型（补充）相关的miRNA、35个与钙化不全型相关的miRNA以及49个与成熟不全型AI相关的miRNA。选择策略包括使用AI类型的基因组进行多靶点微小RNA搜索。
分析了关键蛋白、钙依赖性和遗传因素，以揭示它们在牙釉质形成中的作用。具有多种调控功能的细胞外非编码RNA序列的作用似乎最具吸引力。我们选择了与AI基因相关的微小RNA列表。我们发现基因组中有4个微小RNA（hsa-miR-27a-3p、hsa-miR-375、hsa-miR-16-5p和hsa-miR-146a-5p）与发育不全型AI相关；5个微小RNA（hsa-miR-29c-3p、hsa-miR-124-3p、hsa-miR-1343-3p、hsa-miR-335-5p和hsa-miR-16-5p）与钙化不全型AI相关，7个（hsa-miR-124-3p、hsa-miR-147a、hsa-miR-16-5p、hsa-miR-429、hsa-let-7b-5p, hsa-miR-146a-5p、hsa-miR-335-5p）与成熟不全型相关。结果显示，hsa-miR-16-5p包含在发育不全型、钙化不全型和成熟不全型的三个特定组中。Hsa-miR-146a-5p与发育不全型和成熟不全型AI相关，这与炎症反应免疫反应的特点有关。反过来，hsa-miR-335-5p与钙化不全型和成熟不全型AI相关。
液体活检方法是现代医疗保健中降低这些患者治疗经济成本的一种有前景的方法。关于微小RNA在调节牙釉质形成中的作用存在独特的数据。已发现与AI基因相关并按AI类型分类的微小RNA列表。靶基因分析显示了所选微小RNA的多种功能，这解释了牙釉质形成中的多种异质性机制。矿化问题的易感性是一个程序性事件。许多因素决定了这个问题的表现。此外，有必要记住变化的可变性质，这会降低预测准确性。因此，基于液体活检和微小RNA的模型能够考虑这些因素及其对矿化的影响。所发现的数据需要进一步研究。
