Metagenomic analysis of healthy and diseased peri-implant microbiome under different periodontal conditions: a cross-sectional study

BACKGROUND

Peri-implantitis is a polybacterial infection that can lead to the failure of dental implant rehabilitation. This study aimed to profile the microbiome of the peri-implant plaque and estimate the effect of periodontitis on it among 40 Chinese participants with dental implant prostheses and presenting with varying peri-implant and periodontal health states.

METHODS

Submucosal plaque samples were collected from four distinct clinical categories based on both their implant and periodontal health status at sampling point. Clinical examinations of dental implant and remaining teeth were carried out. Metagenomic analysis was then performed.

RESULTS

The microbiome of the peri-implantitis sites differed from that of healthy implant sites, both taxonomically and functionally. Moreover, the predominant species in peri-implantitis sites were slightly affected by the presence of periodontitis. T. forsythia, P. gingivalis, T. denticola, and P. endodontalis were consistently associated with peri-implantitis and inflammatory clinical parameters regardless of the presence of periodontitis. Prevotella spp. and P. endodontalis showed significant differences in the peri-implantitis cohorts under different periodontal conditions. The most distinguishing function between diseased and healthy implants is related to flagellar assembly, which plays an important role in epithelial cell invasion.

CONCLUSIONS

The composition of the peri-implant microbiome varied in the diseased and healthy states of implants and is affected by individual periodontal conditions. Based on their correlations with clinical parameters, certain species are associated with disease and healthy implants. Flagellar assembly may play a vital role in the process of peri-implantitis.

Human striatal organoids derived from pluripotent stem cells recapitulate striatal development and compartments

The striatum links neuronal circuits in the human brain, and its malfunction causes neuronal disorders such as Huntington's disease (HD). A human striatum model that recapitulates fetal striatal development is vital to decoding the pathogenesis of striatum-related neurological disorders and developing therapeutic strategies. Here, we developed a method to construct human striatal organoids (hStrOs) from human pluripotent stem cells (hPSCs), including hStrOs-derived assembloids. Our hStrOs partially replicated the fetal striatum and formed striosome and matrix-like compartments in vitro. Single-cell RNA sequencing revealed distinct striatal lineages in hStrOs, diverging from dorsal forebrain fate. Using hStrOs-derived assembloids, we replicated the striatal targeting projections from different brain parts. Furthermore, hStrOs can serve as hosts for striatal neuronal allografts to test allograft neuronal survival and functional integration. Our hStrOs are suitable for studying striatal development and related disorders, characterizing the neural circuitry between different brain regions, and testing therapeutic strategies.

Effects of PKM2 on global metabolic changes and prognosis in hepatocellular carcinoma: from gene expression to drug discovery

BACKGROUND

Hepatocellular carcinoma (HCC) is a malignant tumor that threatens global human health. High PKM2 expression is widely reported in multiple cancers, especially in HCC. This study aimed to explore the effects of PKM2 on global gene expression, metabolic damages, patient prognosis, and multiple transcriptional regulation relationships, as well as to identify several key metabolic genes and screen some small-molecule drugs.

METHODS

Transcriptome and clinical HCC data were downloaded from the NIH-GDC repository. Information regarding the metabolic genes and subsystems was collected from the Recon 2 human metabolic model. Drug-protein interaction data were obtained from the DrugBank and UniProt databases. We defined patients with PKM2 expression levels ≥11.25 as the high-PKM2 group, and those with low PKM2 expression (< 11.25) were defined as the low-PKM2 group.

RESULTS

The results showed that the global metabolic gene expression levels were obviously divided into the high- or low-PKM2 groups. In addition, a greater number of affected metabolic subsystems were observed in the high-PKM2 group. Furthermore, we identified 98 PKM2-correlated deregulated metabolic genes that were associated with poor overall patient survival. Together, these findings suggest more comprehensive influences of PKM2 on HCC. In addition, we screened several small-molecule drugs that target these metabolic enzymes, some of which have been used in antitumor clinical studies.

CONCLUSIONS

HCC patients with high PKM2 expression showed more severe metabolic damage, transcriptional regulation imbalance and poor prognosis than low-PKM2 individuals. We believe that our study provides valuable information for pathology research and drug development for HCC.