Comparative analysis of the in vivo kinetic properties of various bone substitutes filled into a peri-implant canine defect model.

PURPOSE: Deproteinized bovine bone or synthetic hydroxyapatite are 2 prevalent bone grafting materials used in the clinical treatment of peri-implant bone defects. However, the differences in bone formation among these materials remain unclear. This study evaluated osteogenesis kinetics in peri-implant defects using 2 types of deproteinized bovine bone (Bio-Oss® and Bio-Oss/Collagen®) and 2 types of synthetic hydroxyapatite (Apaceram-AX® and Refit®). We considered factors including newly generated bone volume; bone, osteoid, and material occupancy; and bone-to-implant contact. METHODS: A beagle model with a mandibular defect was created by extracting the bilateral mandibular third and fourth premolars. Simultaneously, an implant was inserted into the defect, and the space between the implant and the surrounding bone walls was filled with Bio-Oss, Bio-Oss/Collagen, Apaceram-AX, Refit, or autologous bone. Micro-computed tomography and histological analyses were conducted at 3 and 6 months postoperatively (Refit and autologous bone were not included at the 6-month time point due to their rapid absorption). RESULTS: All materials demonstrated excellent biocompatibility and osteoconductivity. At 3 months, Bio-Oss and Apaceram-AX exhibited significantly greater volumes of formation than the other materials, with Bio-Oss having a marginally higher amount. However, this outcome was reversed at 6 months, with no significant difference between the 2 materials at either time point. Apaceram-AX displayed notably slower bioresorption and the largest quantity of residual material at both time points. In contrast, Refit had significantly greater bioresorption, with complete resorption and rapid maturation involving cortical bone formation at the crest at 3 months, Refit demonstrated the highest mineralized tissue and osteoid occupancy after 3 months, albeit without statistical significance. CONCLUSIONS: Overall, the materials demonstrated varying post-implantation behaviors in vivo. Thus, in a clinical setting, both the properties of these materials and the specific conditions of the defects needing reinforcement should be considered to identify the most suitable material.

Discrimination of Bacterial Community Structures among Healthy, Gingivitis, and Periodontitis Statuses through Integrated Metatranscriptomic and Network Analyses.

Periodontal disease is an inflammatory condition caused by polymicrobial infection. The inflammation is initiated at the gingiva (gingivitis) and then extends to the alveolar bone, leading to tooth loss (periodontitis). Previous studies have shown differences in bacterial composition between periodontal healthy and diseased sites. However, bacterial metabolic activities during the health-to-periodontitis microbiome shift are still inadequately understood. This study was performed to investigate the bacterial characteristics of healthy, gingivitis, and periodontitis statuses through metatranscriptomic analysis. Subgingival plaque samples of healthy, gingivitis, and periodontitis sites in the same oral cavity were collected from 21 patients. Bacterial compositions were then determined based on 16S rRNA reads; taxonomic and functional profiles derived from genes based on mRNA reads were estimated. The results showed clear differences in bacterial compositions and functional profiles between healthy and periodontitis sites. Co-occurrence networks were constructed for each group by connecting two bacterial species if their mRNA abundances were positively correlated. The clustering coefficient values were 0.536 for healthy, 0.600 for gingivitis, and 0.371 for periodontitis sites; thus, network complexity increased during gingivitis development, whereas it decreased during progression to periodontitis. Taxa, including Eubacterium nodatum, Eubacterium saphenum, Filifactor alocis, and Fretibacterium fastidiosum, showed greater transcriptional activities than those of red complex bacteria, in conjunction with disease progression. These taxa were associated with periodontal disease progression, and the health-to-periodontitis microbiome shift was accompanied by alterations in bacterial network structure and complexity. IMPORTANCE The characteristics of the periodontal microbiome influence clinical periodontal status. Gingivitis involves reversible gingival inflammation without alveolar bone resorption. In contrast, periodontitis is an irreversible disease characterized by inflammatory destruction in both soft and hard tissues. An imbalance of the microbiome is present in both gingivitis and periodontitis. However, differences in microbiomes and their functional activities in the healthy, gingivitis, and periodontitis statuses are still inadequately understood. Furthermore, some inflamed gingival statuses do not consistently cause attachment loss. In this study, metatranscriptomic analyses were used to investigate the specific bacterial composition and gene expression patterns of the microbiomes of the healthy, gingivitis, and periodontitis statuses. In addition, co-occurrence network analysis revealed that the gingivitis site included features of networks observed in both the healthy and periodontitis sites. These results provide transcriptomic evidence to support gingivitis as an intermediate state between the healthy and periodontitis statuses.

Patient-specific establishment of bacterial composition within the peri-implant microbiota during the earliest weeks after implant uncovering.

BACKGROUND AND OBJECTIVE: Dysbiosis, a loss of balance in the microbiota, is a potential factor of peri-implantitis. However, compositional change of the peri-implant microbiota soon after implant uncovering is still unknown. In this study, bacterial composition in the peri-implant sulcus was examined to understand the establishment of bacterial composition within the peri-implant microbiota during the earliest weeks after implant uncovering. METHODS: Microbiota samples were collected at weeks 1, 2, 4, and 6 after stage-two surgery. Bacterial DNA was isolated from the samples, and a 16S rRNA gene library was constructed. Sequence reads were obtained using a high-throughput sequencing platform and were taxonomically assigned at the phylum and genus levels. RESULTS: Alpha diversity indices, which did not include taxonomic information, were at similar levels throughout the four time points. At 1 and 2 weeks, the bacterial composition was similar among patients with the predominance of Firmicutes and Proteobacteria. However, the composition was diverse at 4 and 6 weeks and significantly dissimilar to the composition at 1 week. CONCLUSIONS: At 1 week, the peri-implant microbiota was already formed with alpha diversity as high as that at the later time points. However, the bacterial composition was not highly dissimilar among patients at 1 week. The composition changed over the passage of several weeks and was specific for each patient.

Phylogenetic diversity in fim and mfa gene clusters between Porphyromonas gingivalis and Porphyromonas gulae, as a potential cause of host specificity.

BACKGROUND: Periodontopathic bacteria Porphyromonas gingivalis in humans and Porphyromonas gulae in animals are phylogenetically close and commonly have FimA and Mfa1 fimbriae. However, little is known about how fimA and mfa1 are phylogenetically different between P. gingivalis and P. gulae. Here, we examined phylogenetic diversity in their fim and mfa gene clusters. METHODS: Twenty P. gulae strains were isolated from the periodontal pocket of 20 dogs. For their genomic information, along with 64 P. gingivalis and 11 P. gulae genomes, phylogenetic relationship between the genotypes of fimA and mfa1 was examined. Variability of amino acid sequences was examined in the three-dimensional structure of FimA. The distance between strains was calculated for fim and mfa genes. RESULTS: Some fimA genotypes in P. gulae were close to particular types in P. gingivalis. Two types of mfa1 were classified as 70-kDa and 53-kDa protein-coding mfa1. The variable amino acid positions were primarily at the outer part of FimA. The genes encoding the structural proteins and the main component were similarly distant from the reference strain in P. gingivalis, but not in P. gulae. CONCLUSIONS: The differences in the gene clusters between P. gingivalis and P. gulae may result in their host specificity.

Discriminating Microbial Community Structure Between Peri-Implantitis and Periodontitis With Integrated Metagenomic, Metatranscriptomic, and Network Analysis.

Peri-implantitis and periodontitis are both polymicrobial diseases induced by subgingival plaque accumulation, with some differing clinical features. Studies on the microbial and gene transcription activity of peri-implantitis microbiota are limited. This study aimed to verify the hypothesis that disease-specific microbial and gene transcription activity lead to disease-specific clinical features, using an integrated metagenomic, metatranscriptomic, and network analysis. Metagenomic data in peri-implantitis and periodontitis were obtained from the same 21 subjects and metatranscriptomic data from 12 subjects were obtained from a database. The microbial co-occurrence network based on metagenomic analysis had more diverse species taxa and correlations than the network based on the metatranscriptomic analysis. Solobacterium moorei and Prevotella denticola had high activity and were core species taxa specific to peri-implantitis in the co-occurrence network. Moreover, the activity of plasmin receptor/glyceraldehyde-3-phosphate dehydrogenase genes was higher in peri-implantitis. These activity differences may increase complexity in the peri-implantitis microbiome and distinguish clinical symptoms of the two diseases. These findings should help in exploring a novel biomarker that assist in the diagnosis and preventive treatment design of peri-implantitis.

Phylogenetic relationship of prophages is affected by CRISPR selection in Group A Streptococcus.

BACKGROUND: Group A Streptococcus (GAS) is a major human pathogen, which is associated with a wide spectrum of invasive diseases, such as pharyngitis, scarlet fever, rheumatic fever, and streptococcal toxic shock syndrome (STSS). It is hypothesized that differences in GAS pathogenicity are related to the acquisition of diverse bacteriophages (phages). Nevertheless, the GAS genome also harbors clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (cas) genes, which play an important role in eliminating foreign DNA, including those of phages. However, the structure of prophages in GAS strains is mosaic, and the phylogenetic relationship between prophages and CRISPR is not clear. In this study, we analyzed CRISPR and prophage structure using 118 complete genome sequences of GAS strains to elucidate the relationship between two genomic elements. Additionally, phylogenetic and M-type analyses were performed. RESULTS: Of the 118 GAS strains, 80 harbored type I-C and/or II-A CRISPR/cas loci. A total of 553 spacer sequences were identified from CRISPR/cas loci and sorted into 229 patterns. We identified and classified 373 prophages into 14 groups. Some prophage groups shared a common integration site, and were related to M-type. We further investigated the correlation between spacer sequences and prophages. Of the 229 spacer sequence patterns, 203 were similar to that of other GAS prophages. No spacer showed similarity with that of a specific prophage group with mutL integration site. Moreover, the average number of prophages in strains with type II-A CRISPR was significantly less than that in type I-C CRISPR and non-CRISPR strains. However, there was no statistical difference between the average number of prophages in type I-C strains and that in non-CRISPR strains. CONCLUSIONS: Our results indicated that type II-A CRISPR may play an important role in eliminating phages and that the prophage integration site may be an important criterion for the acceptance of foreign DNA by GAS. M type, spacer sequence, and prophage group data were correlated with the phylogenetic relationships of GAS. Therefore, we hypothesize that genetic characteristics and/or phylogenetic relationships of GAS may be estimated by analyzing its spacer sequences.

Investigation of potential targets of Porphyromonas CRISPRs among the genomes of Porphyromonas species.

The oral bacterial species Porphyromonas gingivalis, a periodontal pathogen, has plastic genomes that may be driven by homologous recombination with exogenous deoxyribonucleic acid (DNA) that is incorporated by natural transformation and conjugation. However, bacteriophages and plasmids, both of which are main resources of exogenous DNA, do not exist in the known P. gingivalis genomes. This could be associated with an adaptive immunity system conferred by clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated (cas) genes in P. gingivalis as well as innate immune systems such as a restriction-modification system. In a previous study, few immune targets were predicted for P. gingivalis CRISPR/Cas. In this paper, we analyzed 51 P. gingivalis genomes, which were newly sequenced, and publicly available genomes of 13 P. gingivalis and 46 other Porphyromonas species. We detected 6 CRISPR/Cas types (classified by sequence similarity of repeat) in P. gingivalis and 12 other types in the remaining species. The Porphyromonas CRISPR spacers with potential targets in the genus Porphyromonas were approximately 23 times more abundant than those with potential targets in other genus taxa (1,720/6,896 spacers vs. 74/6,896 spacers). Porphyromonas CRISPR/Cas may be involved in genome plasticity by exhibiting selective interference against intra- and interspecies nucleic acids.

Evaluation of (99m)Tc-myocardial Perfusion SPECT Attenuation Correction by Hybrid SPECT/CT-Examination by Quantitative Analysis-.

A non-uniform attenuation correction is necessary for the myocardial perfusion image (MPI) SPECT that is one of the images of the trunk. Simultaneous non-uniform attenuation correction during the process of SPECT reconstruction was enabled by developing hybrid SPECT/CT. Image acquisition of (99m)Tc MPI with hybrid SPECT/CT was performed in a phantom study and clinical cases. We evaluated the effect of non-uniform attenuation correction by Filtered Back Projection (FBP) or Ordered Subsets-Expectation Maximization (OS-EM) using visual analysis and quantitative analysis with a 17-segment model. The phantom study and the clinical cases differed somewhat as follows. In the phantom study, the count increased significantly with non-uniform attenuation correction in visual analysis and quantitative analysis. In the clinical cases, non-uniform attenuation correction increased the quantitative count in the basal and middle layer of the heart, and visual uniformity of the whole heart improved. However, the visual and quantitative count in the apex decreased with non-uniform attenuation correction. As a result, diagnostic performance for coronary heart disease is expected to be improved by this new technique using hybrid SPECT/CT.

[Examination of optimal imaging start time as determined from neighboring organ accumulation on (99m)Tc-tetrofosmin adenosine stress myocardial SPECT].

For ATP stress (99m)Tc-Tetrofosmin myocardial perfusion images (MPI), SPECT imaging is normally started 60 minutes after tracer injection. When the same procedure is applied to Adenosine (Ado) stress MPI, the frequency of the mask-out procedure after reconstruction (MOP) is increased. In this study, we examined the optimal imaging time from accumulation and distribution of isotope to neighboring organs. Time-count curves from dynamic and planer images of the heart, liver, and left upper abdomen were generated in 7 patients for optimal imaging time. MOP was evaluated in ATP stress MPI, in which imaging started 60 minutes after tracer injection (ATP 60), Ado stress MPI with imaging 60 minutes after injection (Ado 60), and Ado stress MPI with imaging 30 minutes after injection (Ado 30), in 575 patients. Up to 30 minutes after injection, washout from the liver was rapid, but after 30 minutes, it was slow. Washout from the left upper abdomen was not constant. MOP was 12.1% ATP 60, 23.1% Ado 60, and 13.2% Ado 30. Changing from Ado 60 to Ado 30 significantly decreased MOP (p<0.05). On adenosine stress MPI, it shortened examination time and enabled SPECT imaging to start 30 minutes after tracer injection.

[A newly developed maneuver, field change conversion (FCC), improved evaluation of the left ventricular volume more accurately on quantitative gated SPECT (QGS) analysis].

PURPOSE: To investigate whether a newly developed maneuver that reduces the reconstruction area by a half more accurately evaluates left ventricular (LV) volume on quantitative gated SPECT (QGS) analysis. METHODS: The subjects were 38 patients who underwent left ventricular angiography (LVG) followed by G-SPECT within 2 weeks. Acquisition was performed with a general purpose collimator and a 64 x 64 matrix. On QGS analysis, the field magnification was 34 cm in original image (Original: ORI), and furthermore it was changed from 34 cm to 17 cm to enlarge the re-constructed image (Field Change Conversion: FCC). End-diastolic volume (EDV) and end-systolic volume (ESV) of the left ventricle were also obtained using LVG. RESULTS: EDV was 71 +/- 19 ml, 83 +/- 20 ml and 98 +/- 23 ml for ORI, FCC and LVG, respectively (p < 0.001: ORI versus LVG, p < 0.001: ORI versus FCC, p < 0.001: FCC versus LVG). ESV was 28 +/- 12 ml, 34 +/- 13 ml and 41 +/- 14 ml for ORI, FCC and LVG, respectively (p < 0.001: ORI versus LVG, p < 0.001: ORI versus FCC, p < 0.001: FCC versus LVG). CONCLUSION: FCC was better than ORI for calculating LV volume in clinical cases. Furthermore, FCC is a useful method for accurately measuring the LV volume on QGS analysis.