Peri-implantitis: effects of periodontitis and its risk factors—a narrative review

Iacono, Vincent J.; Bassir, Seyed Hossein; Wang, Howard H.; Myneni, Srinivas R.

doi:10.21037/fomm-21-63

Cited by 4 publications

(3 citation statements)

References 72 publications

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“…Despite high success rates for dental implants, their bacterial plaque-associated inflammatory lesions, known as peri-implant diseases, still occur ( 1 , 2 ). These lesions continue to degrade the stability of peri-implant soft and hard tissues, which can result in loss of the implant.…”

Section: Introductionmentioning

confidence: 99%

“…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 1 Introduction Despite high success rates for dental implants, their bacterial plaque-associated inflammatory lesions, known as peri-implant diseases, still occur (1,2). These lesions continue to degrade the stability of peri-implant soft and hard tissues, which can result in loss of the implant.…”

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confidence: 99%

“…Peri-implantitis is also reported to be in the range of 11%-47% among dental implants 10 years after their placement (4,5). These numbers further increase in periodontally compromised patients (1,6,7).…”

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confidence: 99%

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Machine learning-enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Boone,

Tjokro,

Chu

et al. 2024

Front. Dent. Med

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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.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Machine learning-enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Boone,

Tjokro,

Chu

et al. 2024

Front. Dent. Med

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Risk factors for Peri-implantitis: An umbrella review of meta-analyses of observational studies and assessment of biases

Giok,

Veettil,

Menon

2024

Journal of Dentistry

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CircRNA-mediated heterogeneous ceRNA regulation mechanism in periodontitis and peri-implantitis

Zhou,

Xiang,

Chen

et al. 2024

Eur J Med Res

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Background Performing a comprehensive study on the differential expression of mRNAs, miRNAs, and circRNAs in the context of peri-implantitis and periodontitis has beneficial advantages to identify unique molecular signatures and pathways that may contribute to our understanding of these conditions. Methods Gingival tissues from healthy individuals and peri-implantitis and periodontitis patients were obtained to identify differential expression genes (DEG) by Illumina HiSeq 2500 instrument. Differential expression analysis was conducted using R statistical software, with significance set at P < 0.05 and fold greater than 2. Functional enrichment analysis of the DEGs was conducted using the Reactome, Gene ontology and KEGG databases. Results Significant differences in mRNA, miRNA, and circRNA profiles were identified between healthy gingival tissues. The top DEGs comprising 6 circRNAs, 2 miRNAs, and 4 mRNAs were identified and the constructed ceRNA network, elucidates their involvement in key signaling pathways such as ErbB, Wnt, and mTOR, which are crucial for understanding the inflammatory progression of these conditions. Conclusions This study highlights a heterogeneous circRNA-mediated ceRNA regulatory mechanism in peri-implantitis and periodontitis, activating signaling pathways and regulating gene expression. Key findings including a detailed analysis of the transcriptional landscape and identification of unique molecular signatures, pathways and cellular components in gingival tissues, offering insights into the molecular differences between peri-implantitis and periodontitis. The study may contribute to the understanding of the pathological mechanisms of these diseases and may aid in the development of targeted therapies. Supplementary Information The online version contains supplementary material available at 10.1186/s40001-024-02153-3.

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Peri-implantitis: effects of periodontitis and its risk factors—a narrative review

Cited by 4 publications

References 72 publications

Machine learning-enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Machine learning-enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Risk factors for Peri-implantitis: An umbrella review of meta-analyses of observational studies and assessment of biases

CircRNA-mediated heterogeneous ceRNA regulation mechanism in periodontitis and peri-implantitis

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