Pattern Recognition Receptors in Periodontal Disease: Molecular Mechanisms, Signaling Pathways, and Therapeutic Implications

Periodontal disease remains a significant global health concern, characterized by complex host–pathogen interactions leading to tissue destruction. This review explored the role of pattern recognition receptors (PRRs) in the pathogenesis of periodontal disease, synthesizing current knowledge on their molecular mechanisms and potential as therapeutic targets. We examined the diverse family of PRRs, focusing on toll-like receptors (TLRs) and NOD-like receptors (NLRs), elucidating their activation by periodontal pathogens and subsequent downstream signaling cascades. This review highlights the intricate interplay between PRR-mediated pathways, including NF-κB and MAPK signaling, and their impact on inflammatory responses and bone metabolism in periodontal tissues. We discussed the emerging concept of PRR crosstalk and its implications for periodontal homeostasis and disease progression. Furthermore, this review addressed the potential of PRR-targeted therapies, exploring both challenges and opportunities in translating molecular insights into clinical applications. By providing an overview of PRRs in periodontal health and disease, this review aims to stimulate future research directions and inform the development of novel diagnostic and therapeutic strategies in periodontology.

Authors

Publication Details

Journal
Journal of Molecular Pathology
Published
2024-11-13
DOI
https://doi.org/10.3390/jmp5040033
Citations
7
Primary Topic
Oral microbiology and periodontitis research
Type
article
Field-Weighted Citation Impact
2.80
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pattern Recognition Receptors in Periodontal Disease: Molecular Mechanisms, Signaling Pathways, and Therapeutic Implications

Elisabetta Ferrara, Francesco Mastrocola
7 citations
Journal of Molecular Pathology
Oral microbiology and periodontitis research
2.80
article

Pattern Recognition Receptors in Periodontal Disease: Molecular Mechanisms, Signaling Pathways, and Therapeutic Implications

Elisabetta Ferrara, Francesco Mastrocola
article en
7 citations

Abstract

Periodontal disease remains a significant global health concern, characterized by complex host–pathogen interactions leading to tissue destruction. This review explored the role of pattern recognition receptors (PRRs) in the pathogenesis of periodontal disease, synthesizing current knowledge on their molecular mechanisms and potential as therapeutic targets. We examined the diverse family of PRRs, focusing on toll-like receptors (TLRs) and NOD-like receptors (NLRs), elucidating their activation by periodontal pathogens and subsequent downstream signaling cascades. This review highlights the intricate interplay between PRR-mediated pathways, including NF-κB and MAPK signaling, and their impact on inflammatory responses and bone metabolism in periodontal tissues. We discussed the emerging concept of PRR crosstalk and its implications for periodontal homeostasis and disease progression. Furthermore, this review addressed the potential of PRR-targeted therapies, exploring both challenges and opportunities in translating molecular insights into clinical applications. By providing an overview of PRRs in periodontal health and disease, this review aims to stimulate future research directions and inform the development of novel diagnostic and therapeutic strategies in periodontology.

Journal of Molecular PathologyVol. 5(4)
Good health and well-being
Openalex Percentile: Top 9%
Oral microbiology and periodontitis research
2.80
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.