Canonical A-to-I and C-to-U RNA Editing Is Enriched at 3′UTRs and microRNA Target Sites in Multiple Mouse Tissues

RNA editing is a process that modifies RNA nucleotides and changes the efficiency and fidelity of the central dogma. Enzymes that catalyze RNA editing are required for life, and defects in RNA editing are associated with many diseases. Recent advances in sequencing have enabled the genome-wide identification of RNA editing sites in mammalian transcriptomes. Here, we demonstrate that canonical RNA editing (A-to-I and C-to-U) occurs in liver, white adipose, and bone tissues of the laboratory mouse, and we show that apparent non-canonical editing (all other possible base substitutions) is an artifact of current high-throughput sequencing technology. Further, we report that high-confidence canonical RNA editing sites can cause non-synonymous amino acid changes and are significantly enriched in 3′ UTRs, specifically at microRNA target sites, suggesting both regulatory and functional consequences for RNA editing

miTAR: a hybrid deep learning-based approach for predicting miRNA targets

Abstract Background microRNAs (miRNAs) have been shown to play essential roles in a wide range of biological processes. Many computational methods have been developed to identify targets of miRNAs. However, the majority of these methods depend on pre-defined features that require considerable efforts and resources to compute and often prove suboptimal at predicting miRNA targets. Results We developed a novel hybrid deep learning-based (DL-based) approach that is capable of predicting miRNA targets at a higher accuracy. This approach integrates convolutional neural networks (CNNs) that excel in learning spatial features and recurrent neural networks (RNNs) that discern sequential features. Therefore, our approach has the advantages of learning both the intrinsic spatial and sequential features of miRNA:target. The inputs for our approach are raw sequences of miRNAs and genes that can be obtained effortlessly. We applied our approach on two human datasets from recently miRNA target prediction studies and trained two models. We demonstrated that the two models consistently outperform the previous methods according to evaluation metrics on test datasets. Comparing our approach with currently available alternatives on independent datasets shows that our approach delivers substantial improvements in performance. We also show with multiple evidences that our approach is more robust than other methods on small datasets. Our study is the first study to perform comparisons across multiple existing DL-based approaches on miRNA target prediction. Furthermore, we examined the contribution of a Max pooling layer in between the CNN and RNN and demonstrated that it improves the performance of all our models. Finally, a unified model was developed that is robust on fitting different input datasets. Conclusions We present a new DL-based approach for predicting miRNA targets and demonstrate that our approach outperforms the current alternatives. We supplied an easy-to-use tool, miTAR, at https://github.com/tjgu/miTAR . Furthermore, our analysis results support that Max Pooling generally benefits the hybrid models and potentially prevents overfitting for hybrid models.http://deepblue.lib.umich.edu/bitstream/2027.42/173437/1/12859_2021_Article_4026.pd

Distinctive genes and signaling pathways associated with type 2 diabetes-related periodontitis: Preliminary study.

The biological mechanisms underlying the pathogenesis of type 2 diabetes (T2DM)-related periodontitis remain unclear. This cross-sectional study evaluated the distinctive transcriptomic changes between tissues with periodontal health and with periodontitis in patients with T2DM. In this cross-sectional study, whole transcriptome sequencing was performed on gingival biopsies from non-periodontitis and periodontitis tissues from non-diabetic and diabetic patients. A differentially expressed gene (DEG) analysis and Ingenuity Pathway Analysis (IPA) assessed the genes and signaling pathways associated with T2DM-related periodontitis. Immunohistochemistry was performed to validate selected DEGs possibly involved in T2DM-related periodontitis. Four hundred and twenty and one thousand five hundred and sixty-three DEGs (fold change ≥ 2) were uniquely identified in the diseased tissues of non-diabetic and diabetic patients, respectively. The IPA predicted the activation of Phagosome Formation, Cardiac β-adrenergic, tRNA Splicing, and PI3K/AKT pathways. The IPA also predicted the inhibition of Cholesterol Biosynthesis, Adrenomedullin, and Inositol Phosphate Compounds pathways in T2DM-related periodontitis. Validation of DEGs confirmed changes in protein expression of PTPN2, PTPN13, DHCR24, PIK3R2, CALCRL, IL1RN, IL-6R and ITGA4 in diseased tissues in diabetic subjects. Thus, these preliminary findings indicate that there are specific genes and functional pathways that may be involved in the pathogenesis of T2DM-related periodontitis

Transfer of oral bacteria to the fetus during late gestation

Abstract The fetus develops in a privileged environment, as the placenta serves as both a gateway for nutrients and a barrier for pathogen transfer to the fetus. Regardless, recent evidence suggests the presence of bacterial DNA in both placenta and fetus, and we have reported that DNA and protein from small numbers of bacteria gain access to the fetus from the maternal bloodstream. Other routes of environmental bacterial transfer from the mother to fetus remain unknown, as well as the physiological relevance of their presence. In these experiments, we examine multiple routes by which bacterial cellular components can enter the fetus and the fetal response to influx of bacterial DNA and protein. We inoculated maternal sheep with genetically-labeled S. aureus (Staphylococcus aureus) using three routes: intravenously, orally, and intra-vaginally. The inoculum did not produce sepsis or fever in the ewes, therefore mimicking incidental exposure to bacteria during pregnancy. 3–5 days post inoculation, we assessed the presence of bacterial components in the fetal tissues and analyzed fetal brain tissue to identify any alterations in gene expression. Our results demonstrate that components of bacteria that were introduced into the maternal mouth were detected in the fetal brain and that they stimulated changes in gene expression. We conclude that an oral route of transmission is relevant for transfer of bacterial cellular components to the fetus

Pla2g12b and Hpn Are Genes Identified by Mouse ENU Mutagenesis That Affect HDL Cholesterol.

Despite considerable progress understanding genes that affect the HDL particle, its function, and cholesterol content, genes identified to date explain only a small percentage of the genetic variation. We used N-ethyl-N-nitrosourea mutagenesis in mice to discover novel genes that affect HDL cholesterol levels. Two mutant lines (Hlb218 and Hlb320) with low HDL cholesterol levels were established. Causal mutations in these lines were mapped using linkage analysis: for line Hlb218 within a 12 Mbp region on Chr 10; and for line Hlb320 within a 21 Mbp region on Chr 7. High-throughput sequencing of Hlb218 liver RNA identified a mutation in Pla2g12b. The transition of G to A leads to a cysteine to tyrosine change and most likely causes a loss of a disulfide bridge. Microarray analysis of Hlb320 liver RNA showed a 7-fold downregulation of Hpn; sequencing identified a mutation in the 3\u27 splice site of exon 8. Northern blot confirmed lower mRNA expression level in Hlb320 and did not show a difference in splicing, suggesting that the mutation only affects the splicing rate. In addition to affecting HDL cholesterol, the mutated genes also lead to reduction in serum non-HDL cholesterol and triglyceride levels. Despite low HDL cholesterol levels, the mice from both mutant lines show similar atherosclerotic lesion sizes compared to control mice. These new mutant mouse models are valuable tools to further study the role of these genes, their affect on HDL cholesterol levels, and metabolism

Demographic characteristics, glycemic and periodontal parameters (mean ± SD) of the study population.

Demographic characteristics, glycemic and periodontal parameters (mean ± SD) of the study population.</p

Ingenuity canonical pathways related to DEGs expressed exclusively in non-diabetic patients and the respective genes that function within each of these signaling pathways.

Ingenuity canonical pathways related to DEGs expressed exclusively in non-diabetic patients and the respective genes that function within each of these signaling pathways.</p

The immunohistochemical staining using antibodies against PTPN2, PTPN13, DHCR24 (Seladin), and PIK3R2 in representative gingival biopsies from the HH, HP, DH, and DP groups.

Bar length = 50 μm, original magnification × 20. HH: non-diabetic patients without periodontitis; HP: non-diabetic patients with periodontitis; DH: T2DM patients without periodontitis; DP: T2DM patients with periodontitis.</p