Shift in the submucosal microbiome of diseased peri-implant sites after non-surgical mechanical debridement treatment

How Nanoparticles Help in Combating Chronic Wound Biofilms Infection?

Chronic wound infection has become a global health problem, with bacterial biofilms, which are difficult to penetrate using traditional antibiotics, considered the primary cause of recurrent infection and delayed healing in chronic wounds. In recent years, the outstanding performance of nanomaterials in controlling biofilm infections has been widely acknowledged, and these materials are regarded as highly promising for chronic wound infection management. The formation and structure of chronic wound biofilms undergo complex dynamic changes. Therefore, a deep understanding of the underlying causes of repeated wound infections and the specific antibacterial mechanisms of nanomaterials at different stages of biofilm formation is crucial for effective "chronic wound infection management". This review first reveals the relationship between biofilms, wound chronicity, and recurrent infections. Secondly, it focuses on the four stages of chronic wound biofilm formation: (1) adhesion stage, (2) aggregation and promotion stage, (3) maturation stage, and (4) regeneration and dissemination stage. It also comprehensively summarizes the specific antibacterial mechanisms of nanomaterials. This study analyzes essential factors affecting the control of chronic wound biofilms by nanoparticles from various perspectives, such as the material itself, the local wound environment, and the systemic host response. Finally, the limitations and potential future trends in current research are discussed. In summary, nanoparticles represent a promising strategy for combating chronic wound biofilm infections, and this review provides new insights for alternative adjuvant therapies in managing bacterial biofilm infections in chronic wounds.

Assessing peri-implant bacterial community structure: the effect of microbiome sample collection method

BACKGROUND

Oral microbiota comprises polymicrobial communities shaped by mutualistic coevolution with the host, contributing to homeostasis and regulating immune function. Nevertheless, dysbiosis of oral bacterial communities is associated with a number of clinical symptoms that ranges from infections to oral cancer. Peri-implant diseases are biofilm-associated inflammatory conditions affecting the soft and hard tissues around dental implants. Characterization and identification of the biofilm community are essential for the understanding of the pathophysiology of such diseases. For that sampling methods should be representative of the biofilm communities Therefore, there is a need to know the effect of different sampling strategies on the biofilm characterization by next generation sequencing.

METHODS

With the aim of selecting an appropriate microbiome sampling procedure for periimplant biofilms, next generation sequencing was used for characterizing the bacterial communities obtained by three different sampling strategies two months after transepithelial abutment placement: adjacent periodontal crevicular fluid (ToCF), crevicular fluid from transepithelial abutment (TACF) and transepithelial abutment (TA).

RESULTS

Significant differences in multiple alpha diversity indices were detected at both the OTU and the genus level between different sampling procedures. Differentially abundant taxa were detected between sample collection strategies, including peri-implant health and disease related taxa. At the community level significant differences were also detected between TACF and TA and also between TA and ToCF. Moreover, differential network properties and association patterns were identified.

CONCLUSIONS

The selection of sample collection strategy can significantly affect the community composition and structure.

TRIAL REGISTRATION

This research is part of a randomized clinical trial that was designed to assess the effect of transepithelial abutment surface on the biofilm formation. The trial was registered at Trial Registration ClinicalTrials.gov under the number NCT03554876.

Favorable subgingival plaque microbiome shifts are associated with clinical treatment for peri-implant diseases

We performed a longitudinal shotgun metagenomic investigation of the plaque microbiome associated with peri-implant diseases in a cohort of 91 subjects with 320 quality-controlled metagenomes. Through recently improved taxonomic profiling methods, we identified the most discriminative species between healthy and diseased subjects at baseline, evaluated their change over time, and provided evidence that clinical treatment had a positive effect on plaque microbiome composition in patients affected by mucositis and peri-implantitis.

Mucosal bleeding correlates with submucosal microbial dysbiosis in peri-implant mucositis of patients with periodontitis

OBJECTIVES

This study aimed to investigate the relationship between microbial communities and the severity of peri-implant mucosal bleeding in peri-implant mucositis.

MATERIALS AND METHODS

Submucosal plaque samples were collected from 54 implants divided into the healthy implant (HI) group, peri-implant mucositis (PM) group, and peri-implantitis (PI) group. Sequencing of 16S rRNA was performed using the Illumina MiSeq platform. Alpha diversity (i.e., Shannon and Chao index) and beta diversity were used to measure microbial diversity within and between microbial communities, respectively. Differences in microbial taxa between groups were assessed via linear discriminate analysis effect size. Correlation between the modified sulcus bleeding index (mSBI) and microbial dysbiosis index (MDI) was examined using Spearman correlation analysis and linear models.

RESULTS

The submucosal bacterial richness (Chao index) was positively correlated with the mean mSBI in the PM group. As the mean mSBI increased in the PM group, the beta diversity became closer to that of the PI group. In the PM group, the abundances of 47 genera were significantly correlated with the mean mSBI, and the MDI was positively associated with the mean mSBI. Fourteen of the forty-seven genera were discriminative taxa between the HI and PI groups, and the abundances of these biomarkers became closer to those in the PI group in the progression of peri-implant disease.

CONCLUSIONS

A higher mSBI value corresponded to a higher risk of microbial dysbiosis in peri-implant mucositis. The biomarkers identified may be useful for monitoring the progression of peri-implant disease.