BACKGROUND: JUB1, a NAC domain containing hydrogen peroxide-induced transcription factor, plays a critical role in plant immunity. Little is known about how JUB1 responds to leaf rust disease in wheat. Recent discoveries in genomics have also unveiled a multitude of sORFs often assumed to be non-functional, to argue for the necessity of including them as potential regulatory players of translation. However, whether methylation on sORFs spanning the 3\'UTR of regulatory genes like JUB1 modulate gene expression, remains unclear.
RESULTS: In this study, we identified the methylation states of two sORFs in 3\'UTR of a homologous gene of JUB1 in wheat, TaJUB1-L, at cytosine residues in CpG, CHH and CHG sites at different time points of disease progression in two near-isogenic lines of wheat (HD2329), with and without Lr24 gene during leaf rust pathogenesis. Here, we report a significant demethylation of the CpG dinucleotides occurring in the sORFs of the 3\'UTR in the resistant isolines after 24 h post-infection. Also, the up-regulated gene expression observed through RT-qPCR was directly proportional to the demethylation of the CpG sites in the sORFs.
CONCLUSIONS: Our findings indicate that TaJUB1-L might be a positive regulator in providing tolerance during leaf rust pathogenesis and cytosine methylation at 3\'UTR might act as a switch for its expression control. These results enrich the potential benefit of conventional methylation assay techniques for unraveling the unexplored enigma in epigenetics during plant-pathogen interaction in a cost-effective and confidentially conclusive manner.

Traditionally, non-coding RNAs (ncRNAs) are regarded as a class of RNA transcripts that lack encoding capability; however, advancements in technology have revealed that some ncRNAs contain small open reading frames (sORFs) that are capable of encoding micropeptides of approximately 150 amino acids in length. sORF-encoded micropeptides (SEPs) have emerged as intriguing entities in hepatocellular carcinoma (HCC) research, shedding light on this previously unexplored realm. Recent studies have highlighted the regulatory functions of SEPs in the occurrence and progression of HCC. Some SEPs exhibit inhibitory effects on HCC, but others facilitate its development. This discovery has revolutionized the landscape of HCC research and clinical management. Here, we introduce the concept and characteristics of SEPs, summarize their associations with HCC, and elucidate their carcinogenic mechanisms in HCC metabolism, signaling pathways, cell proliferation, and metastasis. In addition, we propose a step-by-step workflow for the investigation of HCC-associated SEPs. Lastly, we discuss the challenges and prospects of applying SEPs in the diagnosis and treatment of HCC. This review aims to facilitate the discovery, optimization, and clinical application of HCC-related SEPs, inspiring the development of early diagnostic, individualized, and precision therapeutic strategies for HCC.

Long non-coding RNAs (LncRNAs) are non-protein coding transcripts more than 200 nucleotides in length. Deep sequencing technologies have unveiled lncRNAs can harbor translatable short open reading frames (sORFs). Yet the regulatory mechanisms governing lncRNA translation events remain poorly understood. Here, we exhaustively detected the sequence, functional element, and structure features relevant to lncRNA translation in human. Extensive identification and analysis reveal that translatable lncRNAs contain richer protein-coding related sequence features, cap-dependent and cap-independent translation initiation mechanisms, and more stable secondary structures, as compared to untranslatable lncRNAs. These findings strongly support lncRNAs serve as a repository for the production of new small peptides. Based on the feature fusion affecting translation and the extreme gradient boosting (XGBoost) algorithm, we developed the first computational tool that dedicated for predicting translatable lncRNAs, named TransLncPred. Benchmark experimental results show that our method outperforms several state-of-the-art RNA coding potential prediction tools on the same training and testing datasets. The 100-time 10-fold cross-validation tests also demonstrate that regulatory element-derived features, especially N7-methylguanosine (m7G) and internal ribosome entry site (IRES), contribute to the improvement in predictive performance.

Recently, small open reading frames (sORFs) in long noncoding RNA (lncRNA) have been demonstrated to encode small peptides that can help study the mechanisms of growth and development in organisms. Since machine learning-based computational methods are less costly compared with biological experiments, they can be used to identify sORFs and provide a basis for biological experiments. However, few computational methods and data resources have been exploited for identifying sORFs in plant lncRNA. Besides, machine learning models produce underperforming classifiers when faced with a class-imbalance problem. In this study, an alternative method called SMOTE based on weighted cosine distance (WCDSMOTE) which enables interaction with feature selection is put forward to synthesize minority class samples and weighted edited nearest neighbor (WENN) is applied to clean up majority class samples, thus, hybrid sampling WCDSMOTE-ENN is proposed to deal with imbalanced datasets with the multi-angle feature. A heterogeneous classifier ensemble is introduced to complete the classification task. Therefore, a novel computational method that is based on class-imbalance learning to identify the sORFs with coding potential in plant lncRNA (sORFplnc) is presented. Experimental results manifest that sORFplnc outperforms existing computational methods in identifying sORFs with coding potential. We anticipate that the proposed work can be a reference for relevant research and contribute to agriculture and biomedicine.

The roles of short/small open reading frames (sORFs) have been increasingly recognized in recent years due to the rapidly growing number of sORFs identified in various organisms due to the development and application of the Ribo-Seq technique, which sequences the ribosome-protected footprints (RPFs) of the translating mRNAs. However, special attention should be paid to RPFs used to identify sORFs in plants due to their small size (~30 nt) and the high complexity and repetitiveness of the plant genome, particularly for polyploidy species. In this work, we compare different approaches to the identification of plant sORFs, discuss the advantages and disadvantages of each method, and provide a guide for choosing different methods in plant sORF studies.

Long non-coding RNAs (lncRNAs) are important regulators of biological processes. It has recently been shown that some lncRNAs include small open reading frames (sORFs) that can encode small peptides of no more than 100 amino acids. However, existing methods are commonly applied to human and animal datasets and still suffer from low feature representation capability. Thus, accurate and credible prediction of sORFs with coding ability in plant lncRNAs is imperative. This paper proposes a new method termed sORFPred, in which we design a model named MCSEN by combining multi-scale convolution and Squeeze-and-Excitation Networks to fully mine distinct information embedded in sORFs, integrate and optimize multiple sequence-based and physicochemical feature descriptors, and built a two-layer prediction classifier based on Bayesian optimization algorithm and Extra Trees. sORFPred has been evaluated on sORFs datasets of three species and experimentally validated sORFs dataset. Results indicate that sORFPred outperforms existing methods and achieves 97.28% accuracy, 97.06% precision, 97.52% recall, and 97.29% F1-score on Arabidopsis thaliana, which shows a significant improvement in prediction performance compared to various conventional shallow machine learning and deep learning models.

Small genes (<150 nucleotides) have been systematically overlooked in phage genomes. We employ a large-scale comparative genomics approach to predict >40,000 small-gene families in ∼2.3 million phage genome contigs. We find that small genes in phage genomes are approximately 3-fold more prevalent than in host prokaryotic genomes. Our approach enriches for small genes that are translated in microbiomes, suggesting the small genes identified are coding. More than 9,000 families encode potentially secreted or transmembrane proteins, more than 5,000 families encode predicted anti-CRISPR proteins, and more than 500 families encode predicted antimicrobial proteins. By combining homology and genomic-neighborhood analyses, we reveal substantial novelty and diversity within phage biology, including small phage genes found in multiple host phyla, small genes encoding proteins that play essential roles in host infection, and small genes that share genomic neighborhoods and whose encoded proteins may share related functions.

With the rapid development of computational biology and deep sequencing technology, more and more studies have shown that a large number of non-classical open reading frames that have not been annotated and hidden in non-coding RNA can encode functional micropeptide. This article reviewed the current research status and technology strategy of gene sources, biological properties, predicted methods and functional verification of micropeptide, providing theoretical and reference basis for the subsequent discovery of micropeptides, research on regulatory mechanisms and development of novel targets and biomarkers.
随着计算生物学和深度测序技术的飞速发展,愈来愈多的研究表明大量之前未被注释、隐藏在非编码RNA中的非经典开放阅读框(open reading frame, ORF)具有编码功能性微肽(micropeptide)的能力。本文对微肽的基因来源、生物性质、预测方法和功能验证的研究现状和技术策略展开综述,以期为后续开展微肽发现、研究调控机制以及新靶点、生物标志物的开发等提供理论基础和参考依据。.

An exciting emerging topic in the noncoding RNA (ncRNA) field is the discovery of short peptides called micropeptides (≤100 amino acids), whose novel therapeutic opportunities remain under-explored. Micropeptides have been suggested to play essential regulatory roles in diverse species of physiological and pathological processes. Genomics studies have revealed that these micropeptides are encoded by small open reading frames (sORFs) concealed in misannotated ncRNAs, generally lncRNAs (long noncoding RNAs) and circRNAs (circular RNAs). These ncRNA-encoded micropeptides have been shown to contribute to tumorigenesis but little is known about their pathological mechanism because of challenges in translated sORF identification techniques. Here, we review the best-validated micropeptides involved in the progression of human tumors and discuss their therapeutic and/or prognostic potential, at the same time, we also give our own suggestions on the concept of potential-coding RNA and micropeptides.

Advanced analytic techniques, such as ribosome profiling and mass spectrometry, as well as improved bioinformatics technology, have promoted the field of genome annotation forward and have identified thousands of likely coding short open reading frames (sORFs) in the human genome. The discovery of sORFs and their products allows us to realize that the complexity of the human genome is far greater than previously assumed. Here, we provide a review of human micropeptides encoded by various transcripts such as mitochondrial rRNAs, long noncoding RNAs, circular RNAs, upstream of mRNAs, and so on.