TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequences.

BACKGROUND: MicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. Conventional experimental methods for identifying microRNA targets are both time-consuming and expensive, prompting the development of computational tools for target prediction. However, the existing computational tools exhibit limited performance in meeting the demands of practical applications, highlighting the need to improve the performance of microRNA target prediction models. RESULTS: In this paper, we utilize the most popular natural language processing and computer vision technologies to propose a novel approach, called TEC-miTarget, for microRNA target prediction based on transformer encoder and convolutional neural networks. TEC-miTarget treats RNA sequences as a natural language and encodes them using a transformer encoder, a widely used encoder in natural language processing. It then combines the representations of a pair of microRNA and its candidate target site sequences into a contact map, which is a three-dimensional array similar to a multi-channel image. Therefore, the contact map's features are extracted using a four-layer convolutional neural network, enabling the prediction of interactions between microRNA and its candidate target sites. We applied a series of comparative experiments to demonstrate that TEC-miTarget significantly improves microRNA target prediction, compared with existing state-of-the-art models. Our approach is the first approach to perform comparisons with other approaches at both sequence and transcript levels. Furthermore, it is the first approach compared with both deep learning-based and seed-match-based methods. We first compared TEC-miTarget's performance with approaches at the sequence level, and our approach delivers substantial improvements in performance using the same datasets and evaluation metrics. Moreover, we utilized TEC-miTarget to predict microRNA targets in long mRNA sequences, which involves two steps: selecting candidate target site sequences and applying sequence-level predictions. We finally showed that TEC-miTarget outperforms other approaches at the transcript level, including the popular seed match methods widely used in previous years. CONCLUSIONS: We propose a novel approach for predicting microRNA targets at both sequence and transcript levels, and demonstrate that our approach outperforms other methods based on deep learning or seed match. We also provide our approach as an easy-to-use software, TEC-miTarget, at https://github.com/tingpeng17/TEC-miTarget . Our results provide new perspectives for microRNA target prediction.

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing.

De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model $\pi$-HelixNovo based on the Transformer architecture. We first demonstrated that $\pi$-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized $\pi$-HelixNovo to de novo gut metaproteome peptides for the first time. The results show $\pi$-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful $\pi$-HelixNovo utilizing a larger training dataset, and as expected, $\pi$-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful $\pi$-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and $\pi$-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.

A Whole Exon Screening-Based Score Model Predicts Prognosis and Immune Checkpoint Inhibitor Therapy Effects in Low-Grade Glioma.

Objective: This study aims to identify prognostic factors for low-grade glioma (LGG) via different machine learning methods in the whole genome and to predict patient prognoses based on these factors. We verified the results through in vitro experiments to further screen new potential therapeutic targets. Method: A total of 940 glioma patients from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) were included in this study. Two different feature extraction algorithms - LASSO and Random Forest (RF) - were used to jointly screen genes significantly related to the prognosis of patients. The risk signature was constructed based on these screening genes, and the K-M curve and ROC curve evaluated it. Furthermore, we discussed the differences between the high- and low-risk groups distinguished by the signature in detail, including differential gene expression (DEG), single-nucleotide polymorphism (SNP), copy number variation (CNV), immune infiltration, and immune checkpoint. Finally, we identified the function of a novel molecule, METTL7B, which was highly correlated with PD-L1 expression on tumor cell, as verified by in vitro experiments. Results: We constructed an accurate prediction model based on seven genes (AUC at 1, 3, 5 years= 0.91, 0.85, 0.74). Further analysis showed that extracellular matrix remodeling and cytokine and chemokine release were activated in the high-risk group. The proportion of multiple immune cell infiltration was upregulated, especially macrophages, accompanied by the high expression of most immune checkpoints. According to the in vitro experiment, we preliminarily speculate that METTL7B affects the stability of PD-L1 mRNA by participating in the modification of m6A. Conclusion: The seven gene signatures we constructed can predict the prognosis of patients and identify the potential benefits of immune checkpoint inhibitors (ICI) therapy for LGG. More importantly, METTL7B, one of the risk genes, is a crucial molecule that regulates PD-L1 and could be used as a new potential therapeutic target.

Down-regulation of EPB41L4A-AS1 mediated the brain aging and neurodegenerative diseases via damaging synthesis of NAD+ and ATP.

BACKGROUND: Aging and neurodegenerative diseases are typical metabolic-related processes. As a metabolism-related long non-coding RNA, EPB41L4A-AS has been reported to be potentially involved in the development of brain aging and neurodegenerative diseases. In this study, we sought to reveal the mechanisms of EPB41L4A-AS in aging and neurodegenerative diseases. METHODS: Human hippocampal gene expression profiles downloaded from the Genotype-Tissue Expression database were analyzed to obtain age-stratified differentially expressed genes; a weighted correlation network analysis algorithm was then used to construct a gene co-expression network of these differentially expressed genes to obtain gene clustering modules. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, protein-protein interaction network, and correlation analysis were used to reveal the role of EPB41L4A-AS1. The mechanism was verified using Gene Expression Omnibus dataset GSE5281 and biological experiments (construction of cell lines, Real-time quantitative PCR, Western blot, measurement of ATP and NAD+ levels, nicotinamide riboside treatment, Chromatin Immunoprecipitation) in neurons and glial-derived cells. RESULTS: EPB41L4A-AS1 was downregulated in aging and Alzheimer's disease. EPB41L4A-AS1 related genes were found to be enriched in the electron transport chain and NAD+ synthesis pathway. Furthermore, these genes were highly associated with neurodegenerative diseases and positively correlated with EPB41L4A-AS1. In addition, biological experiments proved that the downregulation of EPB41L4A-AS1 could reduce the expression of these genes via histone H3 lysine 27 acetylation, resulting in decreased NAD+ and ATP levels, while EPB41L4A-AS1 overexpression and nicotinamide riboside treatment could restore the NAD+ and ATP levels. CONCLUSIONS: Downregulation of EPB41L4A-AS1 not only disturbs NAD+ biosynthesis but also affects ATP synthesis. As a result, the high demand for NAD+ and ATP in the brain cannot be met, promoting the development of brain aging and neurodegenerative diseases. However, overexpression of EPB41L4A-AS1 and nicotinamide riboside, a substrate of NAD+ synthesis, can reduce EPB41L4A-AS1 downregulation-mediated decrease of NAD+ and ATP synthesis. Our results provide new perspectives on the mechanisms underlying brain aging and neurodegenerative diseases.

Downregulation of lncRNA EPB41L4A-AS1 Mediates Activation of MYD88-Dependent NF-κB Pathway in Diabetes-Related Inflammation.

PURPOSE: Long non-coding RNAs (lncRNAs) have been shown to be involved in many human diseases. In this study, we aimed to reveal the role and molecular mechanism of lncRNA EPB41L4A-AS1 in type 2 diabetic mellitus (T2DM)-related inflammation. METHODS: To explore the relationships between the expression of EPB41L4A-AS1 and inflammatory factors in the blood of T2DM patients, we analyzed peripheral blood mononuclear cell (PBMC) expression microarrays of T2DM patients and expression microarrays of PBMC treated with lipopolysaccharide (LPS) from the GEO database. The relationship between EPB41L4A-AS1 and phospho-p65 was explored by Western blotting (WB) and immunofluorescence. The interactions between EPB41L4A-AS1 and myeloid differentiation factor 88 (MYD88) were also verified through quantitative real-time PCR, WB, and chromatin immunoprecipitation. Glycolysis and mitochondrial stress were detected by Seahorse. RESULTS: EPB41L4A-AS1 showed very low expression, which was significantly negatively correlated with levels of inflammatory factors in PBMCs of T2DM patients and PBMCs treated with LPS. These results were verified by cell experiments on PBMC and THP-1 cells. Knockdown of EPB41L4A-AS1 led to the phosphorylation and nuclear translocation of p65 and thus activated the NF-κB signaling pathway; it also reduced the enrichment of H3K9me3 in the MYD88 promoter and increased expression of MYD88. Overall, EPB41L4A-AS1 knockdown promoted the level of glycolysis and ultimately enhanced the inflammatory response. CONCLUSION: EPB41L4A-AS1 knockdown activated the NF-κB signaling pathway through a MYD88-dependent regulatory mechanism, promoted glycolysis, and ultimately enhanced the inflammatory response. These results demonstrate that EPB41L4A-AS1 is closely associated with inflammation in T2DM, and that low expression of EPB41L4A-AS1 may be used as an indicator of chronic inflammation and possible diabetic vascular complications in T2DM patients.