Among the main bacteria implicated in the pathology of periodontal disease, Aggregatibacter actinomycetemcomitans (Aa) is well known for causing loss of periodontal attachment and systemic disease. Recent studies have suggested that secreted extracellular RNAs (exRNAs) from several bacteria may be important in periodontitis, although their role is unclear. Emerging evidence indicates that exRNAs circulate in nanosized bilayered and membranous extracellular vesicles (EVs) known as outer membrane vesicles (OMVs) in gram-negative bacteria. In this study, we analyzed the small RNA expression profiles in activated human macrophage-like cells (U937) infected with OMVs from Aa and investigated whether these cells can harbor exRNAs of bacterial origin that have been loaded into the host RNA-induced silencing complex, thus regulating host target transcripts. Our results provide evidence for the cytoplasmic delivery and activity of microbial EV-derived small exRNAs in host gene regulation. The production of TNF-a was promoted by exRNAs via the TLR-8 and NF-kB signaling pathways. Numerous studies have linked periodontal disease to neuroinflammatory diseases but without elucidating specific mechanisms for the connection. We show here that intracardiac injection of Aa OMVs in mice showed successful delivery to the brain after crossing the blood-brain barrier, the exRNA cargos increasing expression of TNF-a in the mouse brain. The current study indicates that host gene regulation by microRNAs originating from OMVs of the periodontal pathogen Aa is a novel mechanism for host gene regulation and that the transfer of OMV exRNAs to the brain may cause neuroinflammatory diseases like Alzheimer's.

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Characterizing the microbial communities inhabiting specimens is one of the primary objectives of microbiome studies. A short-read sequencing platform for reading partial regions of the 16S rRNA gene is most commonly used by reducing the cost burden of next-generation sequencing (NGS), but misclassification at the species level due to its length being too short to consider sequence similarity remains a challenge. Loop Genomics recently proposed a new 16S full-length-based synthetic long-read sequencing technology (sFL16S). We compared a 16S full-length-based synthetic long-read (sFL16S) and V3-V4 short-read (V3V4) methods using 24 human GUT microbiota samples. Our comparison analyses of sFL16S and V3V4 sequencing data showed that they were highly similar at all classification resolutions except the species level. At the species level, we confirmed that sFL16S showed better resolutions than V3V4 in analyses of alpha-diversity, relative abundance frequency and identification accuracy. Furthermore, we demonstrated that sFL16S could overcome the microbial misidentification caused by different sequence similarity in each 16S variable region through comparison the identification accuracy of Bifidobacterium, Bacteroides, and Alistipes strains classified from both methods. Therefore, this study suggests that the new sFL16S method is a suitable tool to overcome the weakness of the V3V4 method.

Highly sensitive methods, such as PNA clamping, may be superior to direct sequencing for the detection of EGFR mutations in diagnostic specimens with a low proportion of tumor cells. Direct sequencing may be more representative when diagnostic specimens with a high proportion of tumor cells are available.

Background The selection of reference genes is essential for quantifying gene expression. Theoretically they should be expressed stably and not regulated by experimental or pathological conditions. However, identification and validation of reference genes for human cancer research are still being regarded as a critical point, because cancerous tissues often represent genetic instability and heterogeneity. Recent pan-cancer studies have demonstrated the importance of the appropriate selection of reference genes for use as internal controls for the normalization of gene expression; however, no stably expressed, consensus reference genes valid for a range of different human cancers have yet been identified. Results In the present study, we used large-scale cancer gene expression datasets from The Cancer Genome Atlas (TCGA) database, which contains 10,028 (9,364 cancerous and 664 normal) samples from 32 different cancer types, to confirm that the expression of the most commonly used reference genes is not consistent across a range of cancer types. Furthermore, we identified 38 novel candidate reference genes for the normalization of gene expression, independent of cancer type. These genes were found to be highly expressed and highly connected to relevant gene networks, and to be enriched in transcription-translation regulation processes. The expression stability of the newly identified reference genes across 29 cancerous and matched normal tissues were validated via quantitative reverse transcription PCR (RT-qPCR). Conclusions We reveal that most commonly used reference genes in current cancer studies cannot be appropriate to serve as representative control genes for quantifying cancer-related gene expression levels, and propose in this study three potential reference genes ( HNRNPL , PCBP1 , and RER1 ) to be the most stably expressed across various cancerous and normal human tissues. Electronic supplementary material The online version of this article (10.1186/s12859-019-2809-2) contains supplementary material, which is available to authorized users.