RNAcentral: a hub of information for non-coding RNA sequences

Fast analysis of Spatial Transcriptomics (FaST): an ultra lightweight and fast pipeline for the analysis of high resolution spatial transcriptomics

Recently, several protocols repurposing the Illumina flow cells or DNA nanoballs as an RNA capture device for spatial transcriptomics have been reported. These protocols yield high volumes of sequencing data which are usually analyzed through the use of high-performance computing clusters. I report Fast analysis of Spatial Transcriptomic (FaST), a novel pipeline for the analysis of subcellular resolution spatial transcriptomics datasets based on barcoding. FaST is compatible with OpenST, seq-scope, Stereo-seq, and potentially other protocols. It allows full reconstruction of the spatially resolved transcriptome, including cell segmentation, of datasets consisting of >500 M million reads in as little as 1 h on a standard multi core workstation with 32 Gb of RAM. The FaST pipeline returns RNA segmented Spatial Transcriptomics datasets suitable for subsequent analysis through commonly used packages (e.g scanpy or seurat). Notably, the pipeline I present relies on the spateo-release package for RNA segmentation and does not require hematoxylin/eosin or any other imaging procedure to guide cell segmentation. Nevertheless, integration with other software for imaging-guided cell segmentation is still possible. FaST is publicly available on github (https://github.com/flcvlr/FaST).

Dissecting the Emerging Regulatory and Mechanistic Paradigms of Transcribed Conserved Non-Coding Elements in Breast Cancer

Transcribed conserved non-coding elements (TCNEs), which are non-coding genomic elements that can regulate vital gene expression, play an unclear role in the development of severe diseases mainly associated with carcinogenesis. Currently, there are no mature tools for the identification of TCNEs. To compensate for the lack of a systematic interpretation of the functional characterization and regulatory mechanisms of TCNE spatiotemporal activities, we developed a flexible pipeline, called captureTCNE, to depict the landscape of TCNEs and applied it to our breast cancer cohort (SEU-BRCA). Meanwhile, we investigated the genome-wide characteristics of TCNEs and unraveled that TCNEs harbor enhancer-like chromatin signatures as well as participate in the transcriptional machinery to regulate essential genes or architect biological regulatory networks of breast cancer. Specifically, the TCNE transcripts could recruit RBPs, such as ENOX1 and PTBP1, which are involved in gene expression regulation, to participate in the formation of regulatory networks and the association with altered splicing patterns. In particular, the presence of a non-classical secondary structure, called RNA G-quadruplex, on TCNE transcripts contributed to the recruitment of RBPs associated with subtype-specific transcriptional processes related to the estrogen response in breast cancer. Ultimately, we also analyzed the mutational signatures of variant-containing TCNEs and discerned twenty-one genes as essential components of the regulatory mechanism of TCNEs in breast cancer. Our study provides an effective TCNE identification pipeline and insights into the regulatory mechanisms of TCNEs in breast cancer, contributing to further knowledge of TCNEs and the emergence of innovative therapeutic strategies for breast cancer.

Profiling the cell-specific small non-coding RNA transcriptome of the human placenta

The human placenta is the composite of multiple cell types, each which contributes uniquely to placental function. Small non-coding RNAs (sncRNAs) are regulators of gene expression and can be cell-specific. The sncRNA transcriptome of individual placental cell types has not yet been investigated due to difficulties in their procurement and isolation. Using a custom sequencing method, we explored the expression of seven sncRNA species (miRNA, piRNA, rRNA, scaRNA, snRNA, snoRNA, tRNA) from whole chorionic villi and four major sample-matched FACS-sorted cell type (cytotrophoblast, stromal, endothelial, Hofbauer) samples from 9 first trimester and 17 term placentas. After normalization for technical variables, samples clustered primarily by cell type lineage. No sncRNAs were uniquely expressed by cell type, however, mean expression differed by cell type for 115 sncRNAs. Known placentally-expressed sncRNAs showed differing expression by cell type and trimester. Expression of few sncRNAs varied by sex. Lastly, sample-matched sncRNA expression and DNA methylation correlation was not significant, although high correlation (> R2 ± 0.6) was observed for some sncRNA-CpG pairs. This study represents the first exploration of the sncRNA transcriptome of bulk placental villi and placental cell types, informing about the expression and regulatory patterns underlying human placental development.

Extracellular Vesicle-Derived microRNA Crosstalk Between Equine Chondrocytes and Synoviocytes-An In Vitro Approach

This study describes a novel technique to analyze the extracellular vesicle (EV)-derived microRNA (miRNA) crosstalk between equine chondrocytes and synoviocytes. Donor cells (chondrocytes, n = 8; synoviocytes, n = 9) were labelled with 5-ethynyl uridine (5-EU); EVs were isolated from culture media and incubated with recipient cells (chondrocytes [n = 5] were incubated with synoviocyte-derived EVs, and synoviocytes [n = 4] were incubated with chondrocyte-derived EVs). Total RNA was extracted from recipient cells; the 5-EU-labelled RNA was recovered and sequenced. Differential expression analysis, pathway analysis, and miRNA target prediction were performed. Overall, 198 and 213 miRNAs were identified in recipient synoviocytes and chondrocytes, respectively. The top five most abundant miRNAs were similar for synoviocytes and chondrocytes (eca-miR-21, eca-miR-221, eca-miR-222, eca-miR-100, eca-miR-26a), and appeared to be linked to joint homeostasis. There were nine differentially expressed (p < 0.05) miRNAs (eca-miR-27b, eca-miR-23b, eca-miR-31, eca-miR-191a, eca-miR-199a-5p, eca-miR-143, eca-miR-21, eca-miR-181a, and eca-miR-181b) between chondrocytes and synoviocytes, which appeared to be linked to migration of cells, apoptosis, cell viability of connective tissue cell, and inflammation. In conclusion, the reported technique was effective in recovering and characterizing the EV-derived miRNA crosstalk between equine chondrocytes and synoviocytes and allowed for the identification of EV-communicated miRNA patterns potentially related to cell viability, inflammation, and joint homeostasis.

Deep learning for RNA structure prediction

Predicting RNA structures from sequences with computational approaches is of vital importance in RNA biology considering the high costs of experimental determination. AI methods have revolutionized this field in recent years, enabling RNA structure prediction with increasingly higher accuracy and efficiency. With an increase in the number of models proposed for this task, this review presents a timely summary of the applications of AI, particularly deep learning, in RNA structure prediction, highlighting their methodology advances as well as the challenges and opportunities for further work in this field.

RNA structure prediction using deep learning - A comprehensive review

In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of RNA functions and RNA-based drug design. Implementing deep learning techniques for RNA structure prediction has led tremendous progress in this field, resulting in significant improvements in prediction accuracy. This comprehensive review aims to provide an overview of the diverse strategies employed in predicting RNA secondary structures, emphasizing deep learning methods. The article categorizes the discussion into three main dimensions: feature extraction methods, existing state-of-the-art learning model architectures, and prediction approaches. We present a comparative analysis of various techniques and models highlighting their strengths and weaknesses. Finally, we identify gaps in the literature, discuss current challenges, and suggest future approaches to enhance model performance and applicability in RNA structure prediction tasks. This review provides a deeper insight into the subject and paves the way for further progress in this dynamic intersection of life sciences and artificial intelligence.

ANNInter: A platform to explore ncRNA-ncRNA interactome of Arabidopsis thaliana

Eukaryotic transcriptomes are remarkably complex, encompassing not only protein-coding RNAs but also an expanding repertoire of noncoding RNAs (ncRNAs). In plants, ncRNA-ncRNA interactions (NNIs) have emerged as pivotal regulators of gene expression, orchestrating development and adaptive responses to stress. Despite their critical roles, the functional significance of NNIs remains poorly understood, largely due to a lack of comprehensive resources. Here, we present ANNInter, a comprehensive platform that integrates computational predictions with experimental datasets to systematically identify and analyze NNIs. The current version catalogs over 90,000 interactions spanning eight categories of sRNA-to-longer ncRNAs, each extensively annotated with interaction types, identification methods, and functional descriptions. The integrated schema and advanced visualization framework in ANNInter enable users to explore intricate interaction networks, providing system-wide insights into ncRNA-mediated regulation. These interaction data provide unparalleled opportunities to uncover the regulatory roles of NNIs in key biological processes such as growth regulation, stress adaptation, and cellular signaling. By providing an extensive, curated repository of computational and degradome-based interaction data, ANNInter will provide a platform to the study of ncRNA biology, elucidating the complex mechanisms of NNIs and supporting the concept of competing endogenous RNAs (ceRNAs) in gene regulation. The platform is freely accessible at https://www.nipgr.ac.in/ANNInter/.

fpocketR: A platform for identification and analysis of ligand-binding pockets in RNA

Small molecules that bind specific sites in RNAs hold promise for altering RNA function, manipulating gene expression, and expanding the scope of druggable targets beyond proteins. Identifying binding sites in RNA that can engage ligands with good physicochemical properties remains a significant challenge. fpocketR is a software package for identifying, characterizing, and visualizing ligand-binding sites in RNA. fpocketR was optimized, through comprehensive analysis of currently available RNA-ligand complexes, to identify pockets in RNAs able to bind small molecules possessing favorable properties, generally termed drug-like. Here, we demonstrate use of fpocketR to analyze RNA-ligand interactions and novel pockets in small and large RNAs, to assess ensembles of RNA structure models, and to identify pockets in dynamic RNA systems. fpocketR performs best with RNA structures visualized at high (≤3.5 Å) resolution, but also provides useful information with lower resolution structures and computational models. fpocketR is a powerful, freely available tool for discovery and analysis of ligand-binding pockets in RNA molecules.

Circulating miRNAs are associated with successful bone regeneration

Introduction

Bone healing is a well-orchestrated process involving various bone cells and signaling pathways, where disruptions can result in delayed or incomplete healing. MicroRNAs (miRNAs) are small non-coding RNAs capable of influencing various cellular processes, including bone remodeling. Due to their biological relevance and stable presence in biofluids, miRNAs may serve as candidates for diagnosis and prognosis of delayed bone healing. The aim of the study was to investigate changes in miRNAs circulating in the blood during the healing of rat calvaria defects as biomarkers of successful bone regeneration.

Methods

Standardized calvaria defects were created in 36 Wistar rats with a trephine drill and treated with collagen hydroxyapatite (CHA) scaffolds. The treatment groups included CHA scaffolds only, CHA scaffolds containing a plasmid coding for bone morphogenetic protein 2 (BMP2) and miR-590-5p, CHA scaffolds containing mesenchymal stromal cell-derived extracellular vesicles, and empty defects as a control group. After 1, 4 and 8 weeks of healing, the animals were evaluated by microcomputed tomography (microCT), as well as subjected to histological analyses. Blood was sampled from the tail vein prior to surgeries and after 1, 4, and 8 weeks of healing. miRNAs circulating in the plasma were determined using next-generation sequencing.

Results

Variability of bone regeneration within the four groups was unexpectedly high and did not result in significant differences between the groups, as indicated by the microCT and histological analyses of the newly formed bone tissue. However, irrespective of the treatment group and regenerative activity, we identified miRNAs with distinct expression patterns of up- and downregulation at different time points. Furthermore, rats with high and low regenerative activity were characterized by distinct circulating miRNA profiles. miR-133-3p was identified as the top upregulated miRNA and miR-375-3p was identified as the top downregulated miRNA in animals exhibiting strong regeneration over all time points evaluated.

Conclusion

Our study indicates that regardless of the treatment group, success or lack of bone regeneration is associated with a distinct expression pattern of circulating microRNAs. Further research is needed to determine whether their levels in the blood can be used as predictive factors of successful bone regeneration.

Transition to Piscivory Seen Through Brain Transcriptomics in a Juvenile Percid Fish: Complex Interplay of Differential Gene Transcription, Alternative Splicing, and ncRNA Activity

Pikeperch (Sander Lucioperca) belongs to main predatory fish species in freshwater bodies throughout Europe playing the key role by reducing planktivorous fish abundance. Two size classes of the young-of-the-year (YOY) pikeperch are known in Europe and North America. Our long-term fish survey elucidates late-summer size distribution of YOY pikeperch in the Lipno Reservoir (Czechia) and recognizes two distinct subcohorts: smaller pelagic planktivores heavily outnumber larger demersal piscivores. To explore molecular mechanisms accompanying the switch from planktivory to piscivory, we compared brain transcriptomes of both subcohorts and identified 148 differentially transcribed genes. The pathway enrichment analyses identified the piscivorous phase to be associated with genes involved in collagen and extracellular matrix generation with numerous Gene Ontology (GO), while the planktivorous phase was associated with genes for non-muscle-myosins (NMM) with less GO terms. Transcripts further upregulated in planktivores from the periphery of the NMM network were Pmchl, Pomcl, and Pyyb, all involved also in appetite control and producing (an)orexigenic neuropeptides. Noncoding RNAs were upregulated in transcriptomes of planktivores including three transcripts of snoRNA U85. Thirty genes mostly functionally unrelated to those differentially transcribed were alternatively spliced between the subcohorts. Our results indicate planktivores as potentially driven by voracity to initiate the switch to piscivory, while piscivores undergo a dynamic brain development. We propose a spatiotemporal spreading of juvenile development over a longer period and larger spatial scales through developmental plasticity as an adaptation to exploiting all types of resources and decreasing the intraspecific competition.

Profiling the cell-specific small non-coding RNA transcriptome of the human placenta

The human placenta is the composite of multiple cell types, each which contributes uniquely to placental function. Small non-coding RNAs (sncRNAs) are regulators of gene expression and can be cell-specific. The sncRNA transcriptome of individual placental cell types has not yet been investigated due to difficulties in their procurement and isolation. Using a custom sequencing method, we explored the expression of seven sncRNA species (miRNA, piRNA, rRNA, scaRNA, snRNA, snoRNA, tRNA) from whole chorionic villi and four major sample-matched FACS-sorted cell type (cytotrophoblast, stromal, endothelial, Hofbauer) samples from 9 first trimester and 17 term placentas. After normalization for technical variables, samples clustered primarily by cell type lineage. No sncRNAs were uniquely expressed by cell type, however, mean expression differed by cell type for 115 sncRNAs. Known placentally-expressed sncRNAs showed differing expression by cell type and trimester. Expression of few sncRNAs varied by sex. Lastly, sample-matched sncRNA expression and DNA methylation correlation was not significant, although high correlation (> R2 ± 0.6) was observed for some sncRNA-CpG pairs. This study represents the first exploration of the sncRNA transcriptome of bulk placental villi and placental cell types, informing about the expression and regulatory patterns underlying human placental development.

Non-Coding RNAs in Cancer: Structure, Function, and Clinical Application

We are on the brink of a paradigm shift in both theoretical and clinical oncology. Genomic and transcriptomic profiling, alongside personalized approaches that account for individual patient variability, are increasingly shaping discourse. Discussions on the future of personalized cancer medicine are mainly dominated by the potential of non-coding RNAs (ncRNAs), which play a prominent role in cancer progression and metastasis formation by regulating the expression of oncogenic or tumor suppressor proteins at transcriptional and post-transcriptional levels; furthermore, their cell-free counterparts might be involved in intercellular communication. Non-coding RNAs are considered to be promising biomarker candidates for early diagnosis of cancer as well as potential therapeutic agents. This review aims to provide clarity amidst the vast body of literature by focusing on diverse species of ncRNAs, exploring the structure, origin, function, and potential clinical applications of miRNAs, siRNAs, lncRNAs, circRNAs, snRNAs, snoRNAs, eRNAs, paRNAs, YRNAs, vtRNAs, and piRNAs. We discuss molecular methods used for their detection or functional studies both in vitro and in vivo. We also address the challenges that must be overcome to enter a new era of cancer diagnosis and therapy that will reshape the future of oncology.

Comparative Genomics Reveals LINE-1 Recombination with Diverse RNAs

Long interspersed element-1 (LINE-1, L1) retrotransposons are the most abundant protein-coding transposable elements (TE) in mammalian genomes, and have shaped genome content over 170 million years of evolution. LINE-1 is self-propagating and mobilizes other sequences, including Alu elements. Occasionally, LINE-1 forms chimeric insertions with non-coding RNAs and mRNAs. U6 spliceosomal small nuclear RNA/LINE-1 chimeras are best known, though there are no comprehensive catalogs of LINE-1 chimeras. To address this, we developed TiMEstamp, a computational pipeline that leverages multiple sequence alignments (MSA) to estimate the age of LINE-1 insertions and identify candidate chimeric insertions where an adjacent sequence arrives contemporaneously. Candidates were refined by detecting hallmark features of L1 retrotransposition, such as target site duplication (TSD). Applying this pipeline to the human genome, we recovered all known species of LINE-1 chimeras and discovered new chimeric insertions involving small RNAs, Alu elements, and mRNA fragments. Some insertions are compatible with known mechanisms, such as RNA ligation. Other structures nominate novel mechanisms, such as trans-splicing. We also see evidence that LINE-1 loci with defunct promoters can acquire regulatory elements from nearby genes to restore retrotransposition activity. These discoveries highlight the recombinatory potential of LINE-1 RNA with implications for genome evolution and TE domestication.

RNA-Puzzles Round V: blind predictions of 23 RNA structures

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson-Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest.

miRNATissueAtlas 2025: an update to the uniformly processed and annotated human and mouse non-coding RNA tissue atlas

MiRNAs represent a non-coding RNA class that regulate gene expression and pathways. While miRNAs are evolutionary conserved most data stems from Homo sapiens and Mus musculus. As miRNA expression is highly tissue specific, we developed miRNATissueAtlas to comprehensively explore this landscape in H. sapiens. We expanded the H. sapiens tissue repertoire and included M. musculus. In past years, the number of public miRNA expression datasets has grown substantially. Our previous releases of the miRNATissueAtlas represent a great framework for a uniformly pre-processed and label-harmonized resource containing information on these datasets. We incorporate the respective data in the newest release, miRNATissueAtlas 2025, which contains expressions from 9 classes of ncRNA from 799 billion reads across 61 593 samples for H. sapiens and M. musculus. The number of organs and tissues has increased from 28 and 54 to 74 and 373, respectively. This number includes physiological tissues, cell lines and extracellular vesicles. New tissue specificity index calculations build atop the knowledge of previous iterations. Calculations from cell lines enable comparison with physiological tissues, providing a valuable resource for translational research. Finally, between H. sapiens and M. musculus, 35 organs overlap, allowing cross-species comparisons. The updated miRNATissueAtlas 2025 is available at https://www.ccb.uni-saarland.de/tissueatlas2025.

A Glimpse of Noncoding RNAs: Secondary Structure, Emerging Trends, and Potential Applications in Human Diseases

An appealing strategy for the treatment of several diseases is the therapeutic targeting of noncoding RNAs (ncRNAs), such as microRNAs (miRNAs) and long noncoding RNAs (lncRNAs). Many antisense oligonucleotides and small interfering RNAs have been tested in clinical studies over the past 10 years, and several of these have received FDA approval. However, trial results have thus far been mixed, with some studies reporting strong effects and others showing low effectiveness or side effects, including toxicity. Clinical trials for alternative entities like antimiRNAs are underway, and interest in lncRNA-based therapies is constantly growing. From this perspective, we discuss the basic overview of ncRNAs, their significant role as therapeutic biomarkers against different diseases, and the role of secondary structure in noncoding RNAs.

Profiling of snoRNAs in Exosomes Secreted from Cells Infected with Influenza A Virus

Small nucleolar RNAs (snoRNAs) are non-coding RNAs (ncRNAs) that regulate many cellular processes. Changes in the profiles of cellular ncRNAs and those secreted in exosomes are observed during viral infection. In our study, we analysed differences in expression profiles of snoRNAs isolated from exosomes of influenza (IAV)-infected and non-infected MDCK cells using high-throughput sequencing. The analysis revealed 133 significantly differentially regulated snoRNAs (131 upregulated and 2 downregulated), including 93 SNORD, 38 SNORA, and 2 SCARNA. The most upregulated was SNORD58 (log2FoldChange = 9.61), while the only downregulated snoRNAs were SNORD3 (log2FC = -2.98) and SNORA74 (log2FC = -2.67). Several snoRNAs previously described as involved in viral infections were upregulated, including SNORD27, SNORD28, SNORD29, SNORD58, and SNORD44. In total, 533 interactors of dysregulated snoRNAs were identified using the RNAinter database with an assigned confidence score ≥ 0.25. The main groups of predicted interactors were transcription factors (TFs, 169 interactors) and RNA-binding proteins (RBPs, 130 interactors). Among the most important were pioneer TFs such as POU5F1, SOX2, CEBPB, and MYC, while in the RBP category, notable interactors included Polr2a, TNRC6A, IGF2BP3, and FMRP. Our results suggest that snoRNAs are involved in pro-viral activity, although follow-up studies including experimental validation would be beneficial.

BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation

Artificial intelligence (AI) is revolutionizing scientific discovery because of its super capability, following the neural scaling laws, to integrate and analyze large-scale datasets to mine knowledge. Foundation models, large language models (LLMs) and large vision models (LVMs), are among the most important foundations paving the way for general AI by pre-training on massive domain-specific datasets. Different from the well annotated, formatted and integrated large textual and image datasets for LLMs and LVMs, biomedical knowledge and datasets are fragmented with data scattered across publications and inconsistent databases that often use diverse nomenclature systems in the field of AI for Precision Health and Medicine (AI4PHM). These discrepancies, spanning different levels of biomedical organization from genes to clinical traits, present major challenges for data integration and alignment. To facilitate foundation AI model development and applications in AI4PHM, herein, we developed BioMedGraphica, an all-in-one platform and unified text-attributed knowledge graph (TAKG), consists of 3,131,788 entities and 56,817,063 relations, which are obtained from 11 distinct entity types and harmonizes 29 relations/edge types using data from 43 biomedical databases. All entities and relations are labeled a unique ID and associated with textual descriptions (textual features). Since covers most of research entities in AI4PHM, BioMedGraphica supports the zero-shot or few-shot knowledge discoveries via new relation prediction on the graph. Via a graphical user interface (GUI), researchers can access the knowledge graph with prior knowledge of target functional annotations, drugs, phenotypes and diseases (drug-protein-disease-phenotype), in the graph AI ready format. It also supports the generation of knowledge-multi-omic signaling graphs to facilitate the development and applications of novel AI models, like LLMs, graph AI, for AI4PHM science discovery, like discovering novel disease pathogenesis, signaling pathways, therapeutic targets, drugs and synergistic cocktails.

Identification and developmental profiling of microRNAs in the acoel worm Hofstenia miamia

The acoel worm Hofstenia miamia (H. miamia) has recently emerged as a model organism for studying whole-body regeneration and embryonic development. Previous studies suggest that post-transcriptional mechanisms likely play important roles in whole-body regeneration. Here, we establish a resource for studying H. miamia microRNA-mediated gene regulation, a major aspect of post-transcriptional control in animals. Using small RNA-sequencing samples spanning key developmental stages, we annotated H. miamia microRNAs. Our analysis uncovered a total of 1,050 microRNA loci, including 479 high-confidence loci based on structural and read abundance criteria. Comparison of microRNA seed sequences with those in other bilaterian species revealed that H. miamia encodes the majority of known conserved bilaterian microRNA families and that several microRNA families previously reported only in protostomes or deuterostomes likely have ancient bilaterian origins. We profiled the expression dynamics of the H. miamia miRNAs across embryonic and post-embryonic development. We observed that the let-7 and mir-125 microRNAs are unconventionally enriched at early embryonic stages. To generate hypotheses for miRNA function, we annotated the 3' UTRs of H. miamia protein-coding genes and performed miRNA target site predictions. Focusing on genes that are known to function in the wound response, posterior patterning, and neural differentiation in H. miamia , we found that these processes may be under substantial miRNA regulation. Notably, we found that miRNAs in MIR-7 and MIR-9 families which have target sites in the posterior genes fz-1 , wnt-3 , and sp5 are indeed expressed in the anterior of the animal, consistent with a repressive effect on their corresponding target genes. Our annotation offers candidate miRNAs for further functional investigation, providing a resource for future studies of post-transcriptional control during development and regeneration.

Transcriptome-wide analysis uncovers regulatory elements of the antennal transcriptome repertoire of bumblebee at different life stages

Bumblebees are crucial pollinators, providing essential ecosystem services and global food production. The success of pollination services relies on the interaction between sensory organs and the environment. The antenna functions as a versatile multi-sensory organ, pivotal in mediating chemosensory/olfactory information, and governs adaptive responses to environmental changes. Despite an increasing number of RNA-sequencing studies on insect antenna, comprehensive antennal transcriptome studies at the different life stages were not elucidated systematically. Here, we quantified the expression profile and dynamics of coding/microRNA genes of larval head and antennal tissues from early- and late-stage pupa to the adult of Bombus terrestris as suitable model organism among pollinators. We further performed Pearson correlation analyses on the gene expression profiles of the antennal transcriptome from larval head tissue to adult stages, exploring both positive and negative expression trends. The positively correlated coding genes were primarily enriched in sensory perception of chemical stimuli, ion transport, transmembrane transport processes and olfactory receptor activity. Negatively correlated genes were mainly enriched in organic substance biosynthesis and regulatory mechanisms underlying larval body patterning and the formation of juvenile antennal structures. As post-transcriptional regulators, miR-1000-5p, miR-13b-3p, miR-263-5p and miR-252-5p showed positive correlations, whereas miR-315-5p, miR-92b-3p, miR-137-3p, miR-11-3p and miR-10-3p exhibited negative correlations in antennal tissue. Notably, based on the inverse expression relationship, positively and negatively correlated microRNA (miRNA)-mRNA target pairs revealed that differentially expressed miRNAs predictively targeted genes involved in antennal development, shaping antennal structures and regulating antenna-specific functions. Our data serve as a foundation for understanding stage-specific antennal transcriptomes and large-scale comparative analysis of transcriptomes in different insects.

Peptidomics and Machine Learning-based Evaluation of Noncoding RNA-Derived Micropeptides in Breast Cancer: Expression Patterns and Functional/Therapeutic Insights

Breast cancer is a highly heterogeneous disease characterized by different subtypes arising from molecular alterations that give the disease different phenotypes, clinical behaviors, and prognostic. The noncoding RNA (ncRNA)-derived micropeptides (MPs) represent a novel layer of complexity in cancer study once they can be biologically active and can present potential as biomarkers and also in therapeutics. However, few large-scale studies address the expression of these peptides at the peptidomics level or evaluate their functions and potential in peptide-based therapeutics for breast cancer. In this study, we propose deepening the landscape of ncRNA-derived MPs in breast cancer subtypes and advance the comprehension of the relevance of these molecules to the disease. First, we constructed a 16,349 unique putative MP sequence data set by integrating 2 previously published lists of predicted ncRNA-derived MPs. We evaluated its expression on high-throughput mass spectrometry data of breast tumor samples from different subtypes. Next, we applied several machine and deep learning tools, such as AntiCP 2.0, MULocDeep, PEPstrMOD, Peptipedia, and PreAIP, to predict its functions, cellular localization, tertiary structure, physicochemical features, and other properties related to therapeutics. We identified 58 peptides expressed on breast tissue, including 27 differentially expressed MPs in tumor compared with nontumor samples and MPs exhibiting tumor or subtype specificity. These peptides presented physicochemical features compatible with the canonical proteome and were predicted to influence the tumor immune environment and participate in cell communication, metabolism, and signaling processes. In addition, some MPs presented potential as anticancer, antiinflammatory, and antiangiogenic molecules. Our data demonstrate that MPs derived from ncRNAs have expression patterns associated with specific breast cancer subtypes and tumor specificity, thus highlighting their potential as biomarkers for molecular classification. We also reinforce the relevance of MPs as biologically active molecules that play a role in breast tumorigenesis, besides their potential in peptide-based therapeutics.

Non-coding RNA notations, regulations and interactive resources

An increasing number of non-coding RNAs (ncRNAs) are found to have roles in gene expression and cellular regulations. However, there are still a large number of ncRNAs whose functions remain to be studied. Despite decades of research, the field continues to evolve, with each newly identified ncRNA undergoing processes such as biogenesis, identification, and functional annotation. Bioinformatics methodologies, alongside traditional biochemical experimental methods, have played an important role in advancing ncRNA research across various stages. Presently, over 50 types of ncRNAs have been characterized, each exhibiting diverse functions. However, there remains a need for standardization and integration of these ncRNAs within a unified framework. In response to this gap, this review traces the historical trajectory of ncRNA research and proposes a unified notation system. Additionally, we comprehensively elucidate the ncRNA interactome, detailing its associations with DNAs, RNAs, proteins, complexes, and chromatin. A web portal named ncRNA Hub ( https://bis.zju.edu.cn/nchub/ ) is also constructed to provide detailed notations of ncRNAs and share a collection of bioinformatics resources. This review aims to provide a broader perspective and standardized paradigm for advancing ncRNA research.

Small and Long Non-Coding RNA Analysis for Human Trophoblast-Derived Extracellular Vesicles and Their Effect on the Transcriptome Profile of Human Neural Progenitor Cells

In mice, the fetal brain is dependent upon the placenta for factors that guide its early development. This linkage between the two organs has given rise to the term, the placenta-brain axis. A similar interrelationship between the two organs may exist in humans. We hypothesize that extracellular vesicles (EVs) released from placental trophoblast (TB) cells transport small RNA and other informational biomolecules from the placenta to the brain where their contents have pleiotropic effects. Here, EVs were isolated from the medium in which human trophoblasts (TBs) had been differentiated in vitro from induced pluripotent stem cells (iPSC) and from cultured iPSC themselves, and their small RNA content analyzed by bulk RNA-seq. EVs derived from human TB cells possess unique profiles of miRs, including hsa-miR-0149-3p, hsa-302a-5p, and many long non-coding RNAs (lncRNAs) relative to EVs isolated from parental iPSC. These miRs and their mRNA targets are enriched in neural tissue. Human neural progenitor cells (NPCs), generated from the same iPSC, were exposed to EVs from either TB or iPSC controls. Both sets of EVs were readily internalized. EVs from TB cells upregulate several transcripts in NPCs associated with forebrain formation and neurogenesis; those from control iPSC upregulated a transcriptional phenotype that resembled glial cells more closely than neurons. These results shed light on the possible workings of the placenta-brain axis. Understanding how the contents of small RNA within TB-derived EVs affect NPCs might yield new insights, possible biomarkers, and potential treatment strategies for neurobehavioral disorders that originate in utero, such as autism spectrum disorders (ASDs).

Deep generative design of RNA aptamers using structural predictions

RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers. We show that our approach can design RNA aptamers that are predicted to be structurally similar, yet sequence dissimilar, to known light-up aptamers that fluoresce in the presence of small molecules. We experimentally validate several generated RNA aptamers to have fluorescent activity, show that these aptamers can be optimized for activity in silico, and find that they exhibit a mechanism of fluorescence similar to that of known light-up aptamers. Our results demonstrate how structural predictions can guide the targeted and resource-efficient design of new RNA sequences.

GenerRNA: A generative pre-trained language model for de novo RNA design

The design of RNA plays a crucial role in developing RNA vaccines, nucleic acid therapeutics, and innovative biotechnological tools. However, existing techniques frequently lack versatility across various tasks and are dependent on pre-defined secondary structure or other prior knowledge. To address these limitations, we introduce GenerRNA, a Transformer-based model inspired by the success of large language models (LLMs) in protein and molecule generation. GenerRNA is pre-trained on large-scale RNA sequences and capable of generating novel RNA sequences with stable secondary structures, while ensuring distinctiveness from existing sequences, thereby expanding our exploration of the RNA space. Moreover, GenerRNA can be fine-tuned on smaller, specialized datasets for specific subtasks, enabling the generation of RNAs with desired functionalities or properties without requiring any prior knowledge input. As a demonstration, we fine-tuned GenerRNA and successfully generated novel RNA sequences exhibiting high affinity for target proteins. Our work is the first application of a generative language model to RNA generation, presenting an innovative approach to RNA design.

A library-based approach allows systematic and rapid evaluation of seed region length and reveals design rules for synthetic bacterial small RNAs

All organisms must respond to environmental changes. In bacteria, small RNAs (sRNAs) are an important aspect of the regulation network underlying the adaptation to such changes. sRNAs base-pair with their target mRNAs, allowing rapid modulation of the proteome. This post-transcriptional regulation is usually facilitated by RNA chaperones, such as Hfq. sRNAs have a potential as synthetic regulators that can be modulated by rational design. In this study, we use a library-based approach and oxacillin susceptibility assays to investigate the importance of the seed region length for synthetic sRNAs based on RybB and SgrS scaffolds in Escherichia coli. In the presence of Hfq we show that 12 nucleotides are sufficient for regulation. Furthermore, we observe a scaffold-specific Hfq-dependency and processing by RNase E. Our results provide information for design considerations of synthetic sRNAs in basic and applied research.

Machine learning in RNA structure prediction: Advances and challenges

RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.

Staphylococcal exoribonuclease YhaM destabilizes ribosomes by targeting the mRNA of a hibernation factor

The hibernation-promoting factor (Hpf) in Staphylococcus aureus binds to 70S ribosomes and induces the formation of the 100S complex (70S dimer), leading to translational avoidance and occlusion of ribosomes from RNase R-mediated degradation. Here, we show that the 3'-5' exoribonuclease YhaM plays a previously unrecognized role in modulating ribosome stability. Unlike RNase R, which directly degrades the 16S rRNA of ribosomes in S. aureus cells lacking Hpf, YhaM destabilizes ribosomes by indirectly degrading the 3'-hpf mRNA that carries an intrinsic terminator. YhaM adopts an active hexameric assembly and robustly cleaves ssRNA in a manganese-dependent manner. In vivo, YhaM appears to be a low-processive enzyme, trimming the hpf mRNA by only 1 nucleotide. Deletion of yhaM delays cell growth. These findings substantiate the physiological significance of this cryptic enzyme and the protective role of Hpf in ribosome integrity, providing a mechanistic understanding of bacterial ribosome turnover.

Revealing differential expression patterns of piRNA in FACS blood cells of SARS-CoV-2 infected patients

Non-coding RNA expression has shown to have cell type-specificity. The regulatory characteristics of these molecules are impacted by changes in their expression levels. We performed next-generation sequencing and examined small RNA-seq data obtained from 6 different types of blood cells separated by fluorescence-activated cell sorting of severe COVID-19 patients and healthy control donors. In addition to examining the behavior of piRNA in the blood cells of severe SARS-CoV-2 infected patients, our aim was to present a distinct piRNA differential expression portrait for each separate cell type. We observed that depending on the type of cell, different sorted control cells (erythrocytes, monocytes, lymphocytes, eosinophils, basophils, and neutrophils) have altering piRNA expression patterns. After analyzing the expression of piRNAs in each set of sorted cells from patients with severe COVID-19, we observed 3 significantly elevated piRNAs - piR-33,123, piR-34,765, piR-43,768 and 9 downregulated piRNAs in erythrocytes. In lymphocytes, all 19 piRNAs were upregulated. Monocytes were presented with a larger amount of statistically significant piRNA, 5 upregulated (piR-49039 piR-31623, piR-37213, piR-44721, piR-44720) and 35 downregulated. It has been previously shown that piR-31,623 has been associated with respiratory syncytial virus infection, and taking in account the major role of piRNA in transposon silencing, we presume that the differential expression patterns which we observed could be a signal of indirect antiviral activity or a specific antiviral cell state. Additionally, in lymphocytes, all 19 piRNAs were upregulated.

Challenges in LncRNA Biology: Views and Opinions

This is a mini-review capturing the views and opinions of selected participants at the 2021 IEEE BIBM 3rd Annual LncRNA Workshop, held in Dubai, UAE. The views and opinions are expressed on five broad themes related to problems in lncRNA, namely, challenges in the computational analysis of lncRNAs, lncRNAs and cancer, lncRNAs in sports, lncRNAs and COVID-19, and lncRNAs in human brain activity.

18S and ITS2 rRNA gene sequence-structure phylogeny of the Phaeophyceae (SAR, Stramenopiles) with special reference to Laminariales

The phylogeny of brown algae (Phaeophyceae) has undergone extensive changes in the recent past due to regular new scientific insights. We used nuclear 18S rDNA with an extensive dataset, aiming to increase the accuracy and robustness of the reconstructed phylogenetic trees using a simultaneous sequence-structure approach. Individual secondary structures were generated for all 18S rDNA sequences. The sequence-structure information was encoded and used for an automated simultaneous sequence-structure alignment. Neighbor-joining and profile neighbor-joining trees were calculated based on 186 phaeophycean sequence-structure pairs. Additionally, sequence-structure neighbor-joining, maximum parsimony and maximum likelihood trees were reconstructed on a representative subset. Using a similar approach, ITS2 rDNA sequence-structure information was used to reconstruct a neighbor-joining tree including 604 sequence-structure pairs of the Laminariales. Our study results are in significant agreement with previous single marker 18S and ITS2 rDNA analyses. Moreover, the 18S results are in wide agreement with recent multi-marker analyses. The bootstrap support was significantly higher for our sequence-structure analysis in comparison to sequence-only analyses in this study and the available literature. This study supports the simultaneous inclusion of sequence-structure data at least for 18S to obtain more accurate and robust phylogenetic trees compared to sequence-only analyses.

Foundation models in molecular biology

Determining correlations between molecules at various levels is an important topic in molecular biology. Large language models have demonstrated a remarkable ability to capture correlations from large amounts of data in the field of natural language processing as well as image generation, and correlations captured from data using large language models can also be applicable to solving a wide range of specific tasks, hence large language models are also referred to as foundation models. The massive amount of data that exists in the field of molecular biology provides an excellent basis for the development of foundation models, and the recent emergence of foundation models in the field of molecular biology has really pushed the entire field forward. We summarize the foundation models developed based on RNA sequence data, DNA sequence data, protein sequence data, single-cell transcriptome data, and spatial transcriptome data respectively, and further discuss the research directions for the development of foundation models in molecular biology.

RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models

MOTIVATION

RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the nonunique structure-sequence mapping, and the flexibility of RNA conformation.

RESULTS

In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/ml4bio/RiboDiffusion.

Expanding and Enriching the LncRNA Gene-Disease Landscape Using the GeneCaRNA Database

The GeneCaRNA human gene database is a member of the GeneCards Suite. It presents ~280,000 human non-coding RNA genes, identified algorithmically from ~690,000 RNAcentral transcripts. This expands by ~tenfold the ncRNA gene count relative to other sources. GeneCaRNA thus contains ~120,000 long non-coding RNAs (LncRNAs, >200 bases long), including ~100,000 novel genes. The latter have sparse functional information, a vast terra incognita for future research. LncRNA genes are uniformly represented on all nuclear chromosomes, with 10 genes on mitochondrial DNA. Data obtained from MalaCards, another GeneCards Suite member, finds 1547 genes associated with 1 to 50 diseases. About 15% of the associations portray experimental evidence, with cancers tending to be multigenic. Preliminary text mining within GeneCaRNA discovers interactions of lncRNA transcripts with target gene products, with 25% being ncRNAs and 75% proteins. GeneCaRNA has a biological pathways section, which at present shows 131 pathways for 38 lncRNA genes, a basis for future expansion. Finally, our GeneHancer database provides regulatory elements for ~110,000 lncRNA genes, offering pointers for co-regulated genes and genetic linkages from enhancers to diseases. We anticipate that the broad vista provided by GeneCaRNA will serve as an essential guide for further lncRNA research in disease decipherment.

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning

It is now known that RNAs play more active roles in cellular pathways beyond simply serving as transcription templates. These biological mechanisms might be mediated by higher RNA stereo conformations, triggering the need to understand RNA secondary structures first. However, experimental protocols for solving RNA structures are unavailable for large-scale investigation due to their high costs and time-consuming nature. Various computational tools were thus developed to predict the RNA secondary structures from sequences. Recently, deep networks have been investigated to help predict RNA structures directly from their sequences. However, existing deep-learning-based tools are more or less suffering from model overfitting due to their complicated problem formulation and defective model training processes, limiting their applications across sequences from different structural families. In this research, we designed a two-stage RNA structure prediction strategy called DEBFold (deep ensemble boosting and folding) based on convolution encoding/decoding and self-attention mechanisms to enhance the existing thermodynamic structure models. Moreover, the model training process followed rigorous steps to achieve an acceptable prediction generalization. On the family-wise reserved test sets and the PDB-derived test set, DEBFold achieves better structure prediction performance over traditional tools and existing deep-learning methods. In summary, we obtained a cutting-edge deep-learning-based structure prediction tool with supreme across-family generalization performance. The DEBFold tool can be accessed at https://cobis.bme.ncku.edu.tw/DEBFold/.

Shared genetic factors and causal association between chronic hepatitis C infection and diffuse large B cell lymphoma

BACKGROUND

Epidemiological research and systematic meta-analyses indicate a higher risk of B-cell lymphomas in patients with chronic hepatitis C virus (HCV) compared to non-infected individuals. However, the genetic links between HCV and these lymphomas remain under-researched.

METHODS

Mendelian randomization analysis was employed to explore the association between chronic hepatitis C (CHC) and B-cell lymphomas as well as chronic lymphocytic leukemia (CLL). Approximate Bayes Factor (ABF) localization analysis was conducted to find shared genetic variants that might connect CHC with B-cell lymphomas and chronic lymphocytic leukemia (CLL). Furthermore, The Variant Effect Predictor (VEP) was utilized to annotate the functional effects of the identified genetic variants.

RESULTS

Mendelian randomization revealed a significant association between CHC and increased diffuse large B cell lymphoma (DLBCL) risk (OR: 1.34; 95% CI: 1.01-1.78; P = 0.0397). Subsequent colocalization analysis pinpointed two noteworthy variants, rs17208853 (chr6:32408583) and rs482759 (chr6:32227240) between these two traits. The annotation of these variants through the VEP revealed their respective associations with the butyrophilin-like protein 2 (BTNL2) and notch receptor 4 (NOTCH4) genes, along with the long non-coding RNA (lncRNA) TSBP1-AS1.

CONCLUSION

This research provides a refined genetic understanding of the CHC-DLBCL connection, opening avenues for targeted therapeutic research and intervention.

Highly Reactive Group I Introns Ubiquitous in Pathogenic Fungi

Systemic fungal infections are a growing public health threat, and yet viable antifungal drug targets are limited as fungi share a similar proteome with humans. However, features of RNA metabolism and the noncoding transcriptomes in fungi are distinctive. For example, fungi harbor highly structured RNA elements that humans lack, such as self-splicing introns within key housekeeping genes in the mitochondria. However, the location and function of these mitochondrial riboregulatory elements has largely eluded characterization. Here we used an RNA-structure-based bioinformatics pipeline to identify the group I introns interrupting key mitochondrial genes in medically relevant fungi, revealing their fixation within a handful of genetic hotspots and their ubiquitous presence across divergent phylogenies of fungi, including all highest priority pathogens such as Candida albicans, Candida auris, Aspergillus fumigatus and Cryptococcus neoformans. We then biochemically characterized two representative introns from C. albicans and C. auris, demonstrating their exceptionally efficient splicing catalysis relative to previously-characterized group I introns. Indeed, the C. albicans mitochondrial intron displays extremely rapid catalytic turnover, even at ambient temperatures and physiological magnesium ion concentrations. Our results unmask a significant new set of players in the RNA metabolism of pathogenic fungi, suggesting a promising new type of antifungal drug target.

Analysis of the global transcriptome and miRNAome associated with seed dormancy during seed maturation in rice (Oryza sativa L. cv. Nipponbare)

BACKGROUND

Seed dormancy is a biological mechanism that prevents germination until favorable conditions for the subsequent generation of plants are encountered. Therefore, this mechanism must be effectively established during seed maturation. Studies investigating the transcriptome and miRNAome of rice embryos and endosperms at various maturation stages to evaluate seed dormancy are limited. This study aimed to compare the transcriptome and miRNAome of rice seeds during seed maturation.

RESULTS

Oryza sativa L. cv. Nipponbare seeds were sampled for embryos and endosperms at three maturation stages: 30, 45, and 60 days after heading (DAH). The pre-harvest sprouting (PHS) assay was conducted to assess the level of dormancy in the seeds at each maturation stage. At 60 DAH, the PHS rate was significantly increased compared to those at 30 and 45 DAH, indicating that the dormancy is broken during the later maturation stage (45 DAH to 60 DAH). However, the largest number of differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs) were identified between 30 and 60 DAH in the embryo and endosperm, implying that the gradual changes in genes and miRNAs from 30 to 60 DAH may play a significant role in breaking seed dormancy. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses confirmed that DEGs related to plant hormones were most abundant in the embryo during 45 DAH to 60 DAH and 30 DAH to 60 DAH transitions. Alternatively, most of the DEGs in the endosperm were related to energy and abiotic stress. MapMan analysis and quantitative real-time polymerase chain reaction identified four newly profiled auxin-related genes (OsSAUR6/12/23/25) and one ethylene-related gene (OsERF087), which may be involved in seed dormancy during maturation. Additionally, miRNA target prediction (psRNATarget) and degradome dataset (TarDB) indicated a potential association between osa-miR531b and ethylene biosynthesis gene (OsACO4), along with osa-miR390-5p and the abscisic acid (ABA) exporter-related gene (OsMATE19) as factors involved in seed dormancy.

CONCLUSIONS

Analysis of the transcriptome and miRNAome of rice embryos and endosperms during seed maturation provided new insights into seed dormancy, particularly its relationship with plant hormones such as ABA, auxin, and ethylene.

Cryo-EM structure of the inactive ribosome complex accumulated in chick embryo cells in cold-stress conditions

Here, we present the high-resolution structure of the Gallus gallus 80S ribosome obtained from cold-treated chicken embryos. The translationally inactive ribosome complex contains elongation factor eEF2 with GDP, SERPINE1 mRNA binding protein 1 (SERBP1) and deacylated tRNA in the P/E position, showing common features with complexes already described in mammals. Modeling of most expansion segments of G. gallus 28S ribosomal RNA allowed us to identify specific features in their structural organization and to describe areas where a marked difference between mammalian and avian ribosomes could shed light on the evolution of the expansion segments. This study provides the first structure of an avian ribosome, establishing a model for future structural and functional studies on the translational machinery in Aves.

sRNAflow: A Tool for the Analysis of Small RNA-Seq Data

The analysis of small RNA sequencing data across a range of biofluids is a significant research area, given the diversity of RNA types that hold potential diagnostic, prognostic, and predictive value. The intricate task of segregating the complex mixture of small RNAs from both human and other species, including bacteria, fungi, and viruses, poses one of the most formidable challenges in the analysis of small RNA sequencing data, currently lacking satisfactory solutions. This study introduces sRNAflow, a user-friendly bioinformatic tool with a web interface designed for the analysis of small RNAs obtained from biological fluids. Tailored to the unique requirements of such samples, the proposed pipeline addresses various challenges, including filtering potential RNAs from reagents and environment, classifying small RNA types, managing small RNA annotation overlap, conducting differential expression assays, analysing isomiRs, and presenting an approach to identify the sources of small RNAs within samples. sRNAflow also encompasses an alternative alignment-free analysis of RNA-seq data, featuring clustering and initial RNA source identification using BLAST. This comprehensive approach facilitates meaningful comparisons of results between different analytical methods.

Multiple sequence alignment-based RNA language model and its application to structural inference

Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.

Seminal extracellular vesicle sncRNA sequencing reveals altered miRNA/isomiR profiles as sperm retrieval biomarkers for azoospermia

BACKGROUND

Non-invasive molecular biomarkers for classifying azoospermia by origin into either obstructive or non-obstructive/secretory azoospermia, as well as for inferring the spermatogenic reserve of the testis of non-obstructive/secretory azoospermia patients, are of great interest for testicular sperm retrieval outcome prediction for assisted reproduction. Prior analyses of semen small non-coding RNA expression in azoospermia have focused on microRNAs, but there has been a lack of attention on other regulatory small RNA species. In this regard, studying more in-depth expression changes of small non-coding RNA subtypes in small extracellular vesicles from semen of azoospermic individuals could be useful to select additional non-invasive biomarkers with diagnostic/prognostic purposes.

MATERIAL AND METHODS

A high-throughput small RNA profiling analysis to determine the expression pattern of seminal small extracellular vesicle microRNAs (analyzed at the isomiR level), PIWI-interacting RNAs, and transfer RNA-derived small RNAs in normozoospermic (n = 4) and azoospermic (obstructive azoospermia because of pathological occurring obstruction in the genital tract, n = 4; secretory azoospermic individuals with positive testicular sperm extraction value, n = 5; secretory azoospermic individuals with negative testicular sperm extraction value, n = 4) individuals was carried out. Reverse transcriptase-quantitative real-time polymerase chain reaction validation analysis of selected microRNAs was additionally performed in a larger number of individuals.

RESULTS AND DISCUSSION

Clinically relevant quantitative changes in the small non-coding RNA levels contained in semen small extracellular vesicles can be used as biomarkers for the origin of azoospermia and for predicting the presence of residual spermatogenesis. In this regard, canonical isoform microRNAs (n = 185) but also other isomiR variants (n = 238) stand out in terms of numbers and fold-change differences in expression, underlining the need to consider isomiRs when investigating microRNA-based regulation. Conversely, although transfer RNA-derived small RNAs are shown in our study to represent a high proportion of small non-coding RNA sequences in seminal small extracellular vesicle samples, they are not able to discriminate the origin of azoospermia. PIWI-interacting RNA cluster profiles and individual PIWI-interacting RNAs with significant differential expression were also not able to discriminate. Our study demonstrated that expression values of individual and/or combined canonical isoform microRNAs (miR-10a-5p, miR-146a-5p, miR-31-5p, miR-181b-5p; area under the receiver operating characteristic curve >0.8) in small extracellular vesicles provide considerable clinical value in identifying samples with a high likelihood of sperm retrieval while discriminating azoospermia by origin. Although no individual microRNA showed sufficient discriminating power on its own to identify severe spermatogenic disorders with focal spermatogenesis, multivariate microRNA models in semen small extracellular vesicles have the potential to identify those individuals with residual spermatogenesis. Availability and adoption of such non-invasive molecular biomarkers would represent a great improvement in reproductive treatment decision protocols for azoospermia in clinical practice.

Analysis of extracellular vesicle microRNA profiles reveals distinct blood and lymphatic endothelial cell origins

Extracellular vesicles (EVs) are crucial mediators of cell-to-cell communication in physiological and pathological conditions. Specifically, EVs released from the vasculature into blood were found to be quantitatively and qualitatively different in diseases compared to healthy states. However, our understanding of EVs derived from the lymphatic system is still scarce. In this study, we compared the mRNA and microRNA (miRNA) expression in blood vascular (BEC) and lymphatic (LEC) endothelial cells. After characterization of the EVs by fluorescence-triggered flow cytometry, nanoparticle tracking analysis and cryo-transmission electron microscopy (cryo-TEM) we utilized small RNA-sequencing to characterize miRNA signatures in the EVs and identify cell-type specific miRNAs in BEC and LEC. We found miRNAs specifically enriched in BEC and LEC on the cellular as well as the extracellular vesicle level. Our data provide a solid basis for further functional in vitro and in vivo studies addressing the role of EVs in the blood and lymphatic vasculature.

Hepatitis B Virus and microRNAs: A Bioinformatics Approach

In recent decades, microRNAs (miRNAs) have emerged as key regulators of gene expression, and the identification of viral miRNAs (v-miRNAs) within some viruses, including hepatitis B virus (HBV), has attracted significant attention. HBV infections often progress to chronic states (CHB) and may induce fibrosis/cirrhosis and hepatocellular carcinoma (HCC). The presence of HBV can dysregulate host miRNA expression, influencing several biological pathways, such as apoptosis, innate and immune response, viral replication, and pathogenesis. Consequently, miRNAs are considered a promising biomarker for diagnostic, prognostic, and treatment response. The dynamics of miRNAs during HBV infection are multifaceted, influenced by host variability and miRNA interactions. Given the ability of miRNAs to target multiple messenger RNA (mRNA), understanding the viral-host (human) interplay is complex but essential to develop novel clinical applications. Therefore, bioinformatics can help to analyze, identify, and interpret a vast amount of miRNA data. This review explores the bioinformatics tools available for viral and host miRNA research. Moreover, we introduce a brief overview focusing on the role of miRNAs during HBV infection. In this way, this review aims to help the selection of the most appropriate bioinformatics tools based on requirements and research goals.

Evidence for widespread existence of functional novel and non-canonical human transcripts

BACKGROUND

Fraction of functional sequence in the human genome remains a key unresolved question in Biology and the subject of vigorous debate. While a plethora of studies have connected a significant fraction of human DNA to various biochemical processes, the classical definition of function requires evidence of effects on cellular or organismal fitness that such studies do not provide. Although multiple high-throughput reverse genetics screens have been developed to address this issue, they are limited to annotated genomic elements and suffer from non-specific effects, arguing for a strong need to develop additional functional genomics approaches.

RESULTS

In this work, we established a high-throughput lentivirus-based insertional mutagenesis strategy as a forward genetics screen tool in aneuploid cells. Application of this approach to human cell lines in multiple phenotypic screens suggested the presence of many yet uncharacterized functional elements in the human genome, represented at least in part by novel exons of known and novel genes. The novel transcripts containing these exons can be massively, up to thousands-fold, induced by specific stresses, and at least some can represent bi-cistronic protein-coding mRNAs.

CONCLUSIONS

Altogether, these results argue that many unannotated and non-canonical human transcripts, including those that appear as aberrant splice products, have biological relevance under specific biological conditions.

sRNAfrag: a pipeline and suite of tools to analyze fragmentation in small RNA sequencing data

Fragments derived from small RNAs such as small nucleolar RNAs are biologically relevant but remain poorly understood. To address this gap, we developed sRNAfrag, a modular and interoperable tool designed to standardize the quantification and analysis of small RNA fragmentation across various biotypes. The tool outputs a set of tables forming a relational database, allowing for an in-depth exploration of biologically complex events such as multi-mapping and RNA fragment stability across different cell types. In a benchmark test, sRNAfrag was able to identify established loci of mature microRNAs solely based on sequencing data. Furthermore, the 5' seed sequence could be rediscovered by utilizing a visualization approach primarily applied in multi-sequence-alignments. Utilizing the relational database outputs, we detected 1411 snoRNA fragment conservation events between two out of four eukaryotic species, providing an opportunity to explore motifs through evolutionary time and conserved fragmentation patterns. Additionally, the tool's interoperability with other bioinformatics tools like ViennaRNA amplifies its utility for customized analyses. We also introduce a novel loci-level variance-score which provides insights into the noise around peaks and demonstrates biological relevance by distinctly separating breast cancer and neuroblastoma cell lines after dimension reduction when applied to small nucleolar RNAs. Overall, sRNAfrag serves as a versatile foundation for advancing our understanding of small RNA fragments and offers a functional foundation to further small RNA research. Availability: https://github.com/kenminsoo/sRNAfrag.

A microRNA profile of the saliva in the argasid ticks Ornithodoros erraticus and Ornithodoros moubata and prediction of specific target genes

Ornithodoros erraticus and Ornithodoros moubata ticks are the main vectors of the agents of human relapsing fever (TBRF) and African swine fever (ASF) in the Mediterranean Basin and Africa, respectively. Tick ​​saliva is crucial for complete tick feeding and pathogen transmission, as it contains numerous molecules such as proteins, lipids, and non-coding RNAs (ncRNA) including microRNAs (miRNA). MiRNAs are small ncRNAs capable of regulating the expression of their target messenger RNA (mRNA) leading to degradation or inhibition of its translation into protein. Research on miRNAs from ixodid ticks has revealed that miRNAs are involved in the regulation of different physiological processes of ticks, as well as in the modulation of host gene expression, immune response to tick bite and pathogen transmission. Regarding argasid ticks, there is not information about their miRNAs or their potential involvement in tick physiology and/or in the regulation of the tick-host-pathogen interactions. The aim of this work was to profile the miRNAs expressed in the saliva of O. erraticus and O. moubata, and the in silico prediction and functional analysis of their target genes in the swine host. As a whole, up to 72 conserved miRNAs families were identified in both species: 35 of them were shared and 23 and 14 families were unique to O. erraticus and O. moubata, respectively. The most abundant miRNAs families were mir-1, mir-10 and let-7 in O. erraticus and let-7, mir-252, mir-10 in O. moubata. Four miRNAs sequences of each species were validated by RT-qPCR confirming their presence in the saliva. Target gene prediction in the host (Sus scrofa) and functional analysis showed that the selected miRNAs are mainly involved in processes related to signal transduction, regulation of mRNA transcription and gene expression, synapse regulation, immune response, angiogenesis and vascular development. These results suggest that miRNAs could play an important role at the tick-host interface, providing new insights into this complex relationship that may contribute to a more precise selection of tick molecules for the development of therapeutic and immune strategies to control tick infestations and tick-borne pathogens.

Start codon-associated ribosomal frameshifting mediates nutrient stress adaptation

A translating ribosome is typically thought to follow the reading frame defined by the selected start codon. Using super-resolution ribosome profiling, here we report pervasive out-of-frame translation immediately from the start codon. Start codon-associated ribosomal frameshifting (SCARF) stems from the slippage of ribosomes during the transition from initiation to elongation. Using a massively paralleled reporter assay, we uncovered sequence elements acting as SCARF enhancers or repressors, implying that start codon recognition is coupled with reading frame fidelity. This finding explains thousands of mass spectrometry spectra that are unannotated in the human proteome. Mechanistically, we find that the eukaryotic initiation factor 5B (eIF5B) maintains the reading frame fidelity by stabilizing initiating ribosomes. Intriguingly, amino acid starvation induces SCARF by proteasomal degradation of eIF5B. The stress-induced SCARF protects cells from starvation by enabling amino acid recycling and selective mRNA translation. Our findings illustrate a beneficial effect of translational 'noise' in nutrient stress adaptation.

Identification of immune-related lncRNA in sepsis by construction of ceRNA network and integrating bioinformatic analysis

BACKGROUND

Sepsis is a high mortality disease which seriously threatens human life and health, for which the pathogenetic mechanism still unclear. There is increasing evidence showed that immune and inflammation responses are key players in the development of sepsis pathology. LncRNAs, which act as ceRNAs, have critical roles in various diseases. However, the regulatory roles of ceRNA in the immunopathogenesis of sepsis have not yet been elucidated.

RESULTS

In this study, we aimed to identify immune biomarkers associated with sepsis. We first generated a global immune-associated ceRNA (IMCE) network based on data describing interactions pairs of gene-miRNA and miRNA-lncRNA. Afterward, we excavated a dysregulated sepsis immune-associated ceRNA (SPIMC) network from the global IMCE network by means of a multi-step computational approach. Functional enrichment indicated that lncRNAs in SPIMC network have pivotal roles in the immune mechanism underlying sepsis. Subsequently, we identified module and hub genes (CD4 and STAT4) via construction of a sepsis immune-related PPI network. Then, we identified hub genes based on the modular structure of PPI network and generated a ceRNA subnetwork to analyze key lncRNAs associated with sepsis. Finally, 6 lncRNAs (LINC00265, LINC00893, NDUFA6-AS1, NOP14-AS1, PRKCQ-AS1 and ZNF674-AS1) that identified as immune biomarkers of sepsis. Moreover, the CIBERSORT algorithm and the infiltration of circulating immune cells types were performed to identify the inflammatory state of sepsis. Correlation analyses between immune cells and sepsis immune biomarkers showed that the LINC00265 was strongly positive correlated with the macrophages M2 (r = 0.77).

CONCLUSION

Collectively, these results may suggest that these lncRNAs (LINC00265, LINC00893, NDUFA6-AS1, NOP14-AS1, PRKCQ-AS1 and ZNF674-AS1) played important roles in the immune pathogenesis of sepsis and provide potential therapeutic targets for further researches on immune therapy treatment in patients with sepsis.

sRNAfrag: A pipeline and suite of tools to analyze fragmentation in small RNA sequencing data

Fragments derived from small RNAs such as small nucleolar RNAs hold biological relevance. However, they remain poorly understood, calling for more comprehensive methods for analysis. We developed sRNAfrag, a standardized workflow and set of scripts to quantify and analyze sRNA fragmentation of any biotype. In a benchmark, it is able to detect loci of mature microRNAs fragmented from precursors and, utilizing multi-mapping events, the conserved 5' seed sequence of miRNAs which we believe may extraoplate to other small RNA fragments. The tool detected 1411 snoRNA fragment conservation events between 2/4 eukaryotic species, providing the opportunity to explore motifs and fragmentation patterns not only within species, but between. Availability: https://github.com/kenminsoo/sRNAfrag.