{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T13:35:31Z","timestamp":1770471331502,"version":"3.49.0"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>The rising use of dental implants is accompanied by an expected increase in peri-implant diseases, particularly peri-implantitis (PI), which poses a significant threat to implant success and necessitates a thorough understanding of its pathogenesis for effective management.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To gain deeper insights into the role and impact of the peri-implant microbiome in the pathogenesis and progression of PI, we analyzed 100 samples of saliva and subgingival biofilm from 40 participants with healthy implants (HI group) or with co-occurrence of diagnosed PI-affected implants and healthy implants (PI group) using shotgun metagenomic sequencing. We identified the most discriminative species distinguishing healthy from diseased study groups through log ratios and differential ranking analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results and discussion<\/jats:title><jats:p><jats:italic>Mogibacterium timidum<\/jats:italic>, <jats:italic>Schaalia cardiffensis<\/jats:italic>, <jats:italic>Parvimonas micra<\/jats:italic>, <jats:italic>Filifactor alocis<\/jats:italic>, <jats:italic>Porphyromonas endodontalis<\/jats:italic>, <jats:italic>Porphyromonas gingivalis<\/jats:italic> and <jats:italic>Olsenella uli<\/jats:italic> were associated with the subgingival peri-implant biofilm. In contrast, Neisseria sp oral taxon 014, <jats:italic>Haemophilus parainfluenzae<\/jats:italic>, <jats:italic>Actinomyces naeslundii<\/jats:italic>, <jats:italic>Rothia mucilaginosa<\/jats:italic> and <jats:italic>Rothia aeria<\/jats:italic> were more prevalent in the healthy peri-implant biofilm. Functional pathways such as arginine and polyamine biosynthesis, including putrescine and citrulline biosynthesis, showed stronger correlations with PI-affected implants. In contrast, peri-implant health was characterized by the predominance of pathways involved in purine and pyrimidine deoxyribonucleotide de novo biosynthesis, glucose and glucose-1-phosphate degradation, and tetrapyrrole biosynthesis. Our findings reveal that healthy implants in PI-free oral cavities differ significantly in microbial composition and functional pathways compared to healthy implants co-occurring with PI-affected implants, which more closely resemble PI-associated profiles. This pattern extended to salivary samples, where microbial and functional biomarkers follow similar trends.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcimb.2025.1543100","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T05:25:15Z","timestamp":1744867515000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Linking peri-implantitis to microbiome changes in affected implants, healthy implants, and saliva: a cross-sectional pilot study"],"prefix":"10.3389","volume":"15","author":[{"given":"Lucinda J.","family":"Bessa","sequence":"first","affiliation":[]},{"given":"Concei\u00e7\u00e3o","family":"Egas","sequence":"additional","affiliation":[]},{"given":"Carolina","family":"Pires","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds","family":"Proen\u00e7a","sequence":"additional","affiliation":[]},{"given":"Paulo","family":"Mascarenhas","sequence":"additional","affiliation":[]},{"given":"Ricardo J.","family":"Pais","sequence":"additional","affiliation":[]},{"given":"Helena","family":"Barroso","sequence":"additional","affiliation":[]},{"given":"Vanessa","family":"Machado","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Botelho","sequence":"additional","affiliation":[]},{"given":"Gil","family":"Alcoforado","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9 Jo\u00e3o","family":"Mendes","sequence":"additional","affiliation":[]},{"given":"Ricardo","family":"Alves","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1111\/1755-0998.13014","article-title":"A guide to the application of Hill numbers to DNA-based diversity analyses","volume":"19","author":"Alberdi","year":"2019","journal-title":"Mol. 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