PDF(Pubmed)
Abstract：
Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.
摘要：
种植体周围炎是一种复杂的传染病，表现为牙种植体周围牙槽骨的进行性丧失和与微生物菌群失调相关的过度炎症。使用抗生素治疗种植周炎是有争议的，因为抗生素耐药性的威胁，微生物群落内病原体和共生的非选择性抑制，和潜在严重的全身性后遗症。因此,种植体周围炎的常规治疗包括通过非手术或手术方法与辅助局部杀菌剂进行机械清创。因此,目前的治疗方案可能无法预防复发，因为病原体要么不受影响，要么在治疗后迅速重新出现。成功缓解种植体周围炎的疾病进展需要一种特定的治疗模式，该模式能够靶向主要病原体并恢复细菌群落平衡向共生物种。抗微生物肽（AMP）通过其细菌特异性和靶向抑制活性有望作为替代疗法。然而,肽序列空间表现出复杂的关系，如稀疏载体编码序列，包括描述肽抗菌活性的组合和离散功能。在本文中,我们生成了一个透明的机器学习（ML）模型，该模型基于粗糙集理论，使用AMP的亲水特征的简单摘要来识别序列-函数关系。根据肽对不同类别细菌的差异活性比较肽的亲水特征增强了抗微生物靶向的可预测性。通过遗传算法丰富序列多样性，我们产生了许多用于选择性靶向病原体的候选AMP,并使用分类粗糙集预测了它们的活性.经验生长抑制数据被迭代地反馈到我们的ML训练中，以生成新的肽，导致肽与特定细菌菌株的目标抑制水平匹配的规则越来越严格。随后的得分最高的候选人对其对梯形石和附属种植体周围炎病原体以及口腔共生细菌的抑制作用进行了经验性测试。一种新的肽，VL-13被证实对梯形病原体具有选择性活性。考虑到每年放置的口腔植入物数量不断增加以及疾病进展的复杂性，种植体周围疾病的患病率持续上升.我们的方法提供了透明的ML支持路径，以开发基于抗菌肽的疗法，针对微生物群落的变化，可以有益地影响疾病进展。
