Readon: a novel algorithm to identify read-through transcripts with long-read sequencing data.

MOTIVATION: There are many clustered transcriptionally active regions in the human genome, in which the transcription complex cannot immediately terminate transcription at the upstream gene termination site, but instead continues to transcribe intergenic regions and downstream genes, resulting in read-through transcripts. Several studies have demonstrated the regulatory roles of read-through transcripts in tumorigenesis and development. However, limited by the read length of next-generation sequencing, discovery of read-through transcripts has been slow. For long but also erroneous third-generation sequencing data, this study developed a novel minimizer sketch algorithm to accurately and quickly identify read-through transcripts. RESULTS: Readon initially splits the reference sequence into distinct active regions. It employs a sliding window approach within each region, calculates minimizers, and constructs the specialized structured arrays for query indexing. Following initial alignment anchor screening of candidate read-through transcripts, further confirmation steps are executed. Comparative assessments against existing software reveal Readon's superior performance on both simulated and validated real data. Additionally, two downstream tools are provided: one for predicting whether a read-through transcript is likely to undergo nonsense-mediated decay or encodes a protein, and another for visualizing splicing patterns. AVAILABILITY AND IMPLEMENTATION: Readon is freely available on GitHub (https://github.com/Bulabula45/Readon).

Long noncoding RNA LRG modulates Drosophila locomotion by sequestering Synaptotagmin 1 protein.

Apparently, the genomes of many organisms are pervasively transcribed, and long noncoding RNAs (lncRNAs) make up the majority of cellular transcripts. LncRNAs have been reported to play important roles in many biological processes; however, their effects on locomotion are poorly understood. Here, we presented a novel lncRNA, Locomotion Regulatory Gene (LRG), which participates in locomotion by sequestering Synaptotagmin 1 (SYT1). LRG deficiency resulted in higher locomotion speed which could be rescued by pan-neuronal overexpression but not by limited ellipsoid body, motoneuron or muscle-expression of LRG. At the molecular level, the synaptic vesicles (SVs) release and movement-related SYT1 protein was recognized as LRG-interacting protein candidate. Furthermore, LRG had no effects on SYT1 expression. Genetically, the behavioral defects in LRG mutant could be rescued by pan-neuronal knock-down of Syt1. Taken together, all the results suggested LRG exerts regulatory effects on locomotion via sequestering SYT1 thereby blocking its function without affecting its expression. Our work displays a new function of lncRNA and provides insights for revealing the pathogenesis of neurological diseases with motor disorders.

Exploring noncoding variants in genetic diseases: from detection to functional insights.

Previous studies on genetic diseases predominantly focused on protein-coding variations, overlooking the vast noncoding regions in the human genome. The development of high-throughput sequencing technologies and functional genomics tools has enabled the systematic identification of functional noncoding variants. These variants can impact gene expression, regulation, and chromatin conformation, thereby contributing to disease pathogenesis. Understanding the mechanisms that underlie the impact of noncoding variants on genetic diseases is indispensable for the development of precisely targeted therapies and the implementation of personalized medicine strategies. The intricacies of noncoding regions introduce a multitude of challenges and research opportunities. In this review, we introduce a spectrum of noncoding variants involved in genetic diseases, along with research strategies and advanced technologies for their precise identification and in-depth understanding of the complexity of the noncoding genome. We will delve into the research challenges and propose potential solutions for unraveling the genetic basis of rare and complex diseases.

LncRNA INHEG promotes glioma stem cell maintenance and tumorigenicity through regulating rRNA 2'-O-methylation.

Glioblastoma (GBM) ranks among the most lethal of human cancers, containing glioma stem cells (GSCs) that display therapeutic resistance. Here, we report that the lncRNA INHEG is highly expressed in GSCs compared to differentiated glioma cells (DGCs) and promotes GSC self-renewal and tumorigenicity through control of rRNA 2'-O-methylation. INHEG induces the interaction between SUMO2 E3 ligase TAF15 and NOP58, a core component of snoRNP that guides rRNA methylation, to regulate NOP58 sumoylation and accelerate the C/D box snoRNP assembly. INHEG activation enhances rRNA 2'-O-methylation, thereby increasing the expression of oncogenic proteins including EGFR, IGF1R, CDK6 and PDGFRB in glioma cells. Taken together, this study identifies a lncRNA that connects snoRNP-guided rRNA 2'-O-methylation to upregulated protein translation in GSCs, supporting an axis for potential therapeutic targeting of gliomas.

[Prospects for the Application of Healthcare Big Data Combined With Large Language Models].

The application of big data technology combined with large language models is expected to make an enormous impact in the field of medicine. Herein, the prospects for the application of healthcare big data combined with large language models were discussed in several aspects, including first in assisting doctors in making diagnosis and differential diagnosis and, then, in the field of evidence-based medicine. In addition, healthcare big data combined with large language models could also be applied in assisting doctors to conduct clinical and medical research. Through combining healthcare big data with large language models, medical diagnosis and treatment with improved precision, efficiency, and intelligence will be realized and greater contributions will be made to the field of human health.

A new avialan theropod from an emerging Jurassic terrestrial fauna.

Birds are descended from non-avialan theropod dinosaurs of the Late Jurassic period, but the earliest phase of this evolutionary process remains unclear owing to the exceedingly sparse and spatio-temporally restricted fossil record1-5. Information about the early-diverging species along the avialan line is crucial to understand the evolution of the characteristic bird bauplan, and to reconcile phylogenetic controversies over the origin of birds3,4. Here we describe one of the stratigraphically youngest and geographically southernmost Jurassic avialans, Fujianvenator prodigiosus gen. et sp. nov., from the Tithonian age of China. This specimen exhibits an unusual set of morphological features that are shared with other stem avialans, troodontids and dromaeosaurids, showing the effects of evolutionary mosaicism in deep avialan phylogeny. F. prodigiosus is distinct from all other Mesozoic avialan and non-avialan theropods in having a particularly elongated hindlimb, suggestive of a terrestrial or wading lifestyle-in contrast with other early avialans, which exhibit morphological adaptations to arboreal or aerial environments. During our fieldwork in Zhenghe where F. prodigiosus was found, we discovered a diverse assemblage of vertebrates dominated by aquatic and semi-aquatic species, including teleosts, testudines and choristoderes. Using in situ radioisotopic dating and stratigraphic surveys, we were able to date the fossil-containing horizons in this locality-which we name the Zhenghe Fauna-to 148-150 million years ago. The diversity of the Zhenghe Fauna and its precise chronological framework will provide key insights into terrestrial ecosystems of the Late Jurassic.

Corrigendum to "TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology" [Comput Struct Biotechnol J 20 (2022) 5680-5689].

[This corrects the article DOI: 10.1016/j.scr.2023.103115.].

Cost-Effective Engineered Cementitious Composites with Hybrid PVA and Basalt/PP Fiber: A Study on Compressive, Tensile and Impact Performance.

Currently, oil-coated PVA fibers are the most commonly used material in ECC research. However, the high price limits the application of PVA-ECC in practical engineering. In order to reduce the cost, one of the methods is to partially replace the PVA fibers in ECC. In order to demonstrate the feasibility of PVA/BF-ECC and PVA/PP-ECC, polyvinyl alcohol fibers (PVA), basalt fibers (BFs) and polypropylene fibers (PP) were added at 0.5%, 1.0% and 1.5% by volume of PVA in addition to 1% by volume of PVA. Subsequently, tensile, compression and drop-weight impact tests were conducted on single or hybrid fiber concrete. The results showed that the post-peak compression toughness, tensile strength, and initial cracking impact strength of PVA/BF-ECC and PVA/PP-ECC increased significantly with the increase in the volume ratio of BF and PP fibers, while the performance of PVA-ECC materials with the same fiber volume ratio decreased slightly. Therefore, the cost can be reduced by designing hybrid PVA/BF-ECC materials that meet the performance requirements. The experimental evidence presented in this study demonstrates the feasibility and reasonable prospect of the new hybrid PVA/BF-ECC.

AlphaFold2 and its applications in the fields of biology and medicine.

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis.

Introduction: Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important, yet challenging to study due to disease lesion locations and other complexities. Methods: Utilizing the powerful method of spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular hemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications. Results: Our data identified three main 3D global pseudospace-time trajectory bundles that represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex affected by experimental IVH stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH stimulation. Discussion: These results are informative for understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injury, strategizing more effective clinical management regimens, and developing novel bioinformatics strategies for the study of other CNS diseases. The algorithm strategies used in this study are searchable via a web service (www.combio-lezhang.online/3dstivh/home).

Long non-coding RNAs: definitions, functions, challenges and recommendations.

Genes specifying long non-coding RNAs (lncRNAs) occupy a large fraction of the genomes of complex organisms. The term 'lncRNAs' encompasses RNA polymerase I (Pol I), Pol II and Pol III transcribed RNAs, and RNAs from processed introns. The various functions of lncRNAs and their many isoforms and interleaved relationships with other genes make lncRNA classification and annotation difficult. Most lncRNAs evolve more rapidly than protein-coding sequences, are cell type specific and regulate many aspects of cell differentiation and development and other physiological processes. Many lncRNAs associate with chromatin-modifying complexes, are transcribed from enhancers and nucleate phase separation of nuclear condensates and domains, indicating an intimate link between lncRNA expression and the spatial control of gene expression during development. lncRNAs also have important roles in the cytoplasm and beyond, including in the regulation of translation, metabolism and signalling. lncRNAs often have a modular structure and are rich in repeats, which are increasingly being shown to be relevant to their function. In this Consensus Statement, we address the definition and nomenclature of lncRNAs and their conservation, expression, phenotypic visibility, structure and functions. We also discuss research challenges and provide recommendations to advance the understanding of the roles of lncRNAs in development, cell biology and disease.

NPInter v5.0: ncRNA interaction database in a new era.

Noncoding RNAs (ncRNAs) play key regulatory roles in biological processes by interacting with other biomolecules. With the development of high-throughput sequencing and experimental technologies, extensive ncRNA interactions have been accumulated. Therefore, we updated the NPInter database to a fifth version to document these interactions. ncRNA interaction entries were doubled from 1 100 618 to 2 596 695 by manual literature mining and high-throughput data processing. We integrated global RNA-DNA interactions from iMARGI, ChAR-seq and GRID-seq, greatly expanding the number of RNA-DNA interactions (from 888 915 to 8 329 382). In addition, we collected different types of RNA interaction between SARS-CoV-2 virus and its host from recently published studies. Long noncoding RNA (lncRNA) expression specificity in different cell types from tumor single cell RNA-seq (scRNA-seq) data were also integrated to provide a cell-type level view of interactions. A new module named RBP was built to display the interactions of RNA-binding proteins with annotations of localization, binding domains and functions. In conclusion, NPInter v5.0 (http://bigdata.ibp.ac.cn/npinter5/) provides informative and valuable ncRNA interactions for biological researchers.

TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology.

Recent advances in RNA engineering have enabled the development of RNA-based therapeutics for a broad spectrum of applications. Developing RNA therapeutics start with targeted RNA screening and move to the drug design and optimization. However, existing target screening tools ignore noncoding RNAs and their disease-relevant regulatory relationships. And designing therapeutic RNAs encounters high computational complexity of multi-objective optimization to overcome the immunogenicity, instability and inefficient translational production. To unlock the therapeutic potential of noncoding RNAs and enable one-stop screening and design of therapeutic RNAs, we have built the platform TREAT. It incorporates 43,087,953 regulatory relationships between coding and noncoding genes from 81 biological networks under different physiological conditions. TREAT introduces graph representation learning with Random Walk Diffusions to perform disease-relevant target screening, in addition to the commonly utilized Topological Degree and PageRank algorithms. Design and optimization of large RNAs or interfering RNAs are both available. To reduce the computational complexity of multi-objective optimization for large RNA, we stratified the features into local and global features. The local features are evaluated on the fixed-length or dynamic-length local bins, whereas the latter are inspired by AI language models of protein sequence. Then the global assessment is performed on refined candidates, thus reducing the enormous search space. Overall, TREAT is a one-stop platform for the screening and designing of therapeutic RNAs, with particular attention to noncoding RNAs and cutting-edge AI technology embedded, leading the progress of innovative therapeutics for challenging diseases. TREAT is freely accessible at https://rna.org.cn/treat.

LncRNA CCTT-mediated RNA-DNA and RNA-protein interactions facilitate the recruitment of CENP-C to centromeric DNA during kinetochore assembly.

Kinetochore assembly on centromeres is central for chromosome segregation, and defects in this process cause mitotic errors and aneuploidy. Besides the well-established protein network, emerging evidence suggests the involvement of regulatory RNA in kinetochore assembly; however, it has remained elusive about the identity of such RNA, let alone its mechanism of action in this critical process. Here, we report CCTT, a previously uncharacterized long non-coding RNA (lncRNA) transcribed from the arm of human chromosome 17, which plays a vital role in kinetochore assembly. We show that CCTT highly localizes to all centromeres via the formation of RNA-DNA triplex and specifically interacts with CENP-C to help engage this blueprint protein in centromeres, and consequently, CCTT loss triggers extensive mitotic errors and aneuploidy. These findings uncover a non-centromere-derived lncRNA that recruits CENP-C to centromeres and shed critical lights on the function of centromeric DNA sequences as anchor points for kinetochore assembly.

Long noncoding RNA MAGI2-AS3 regulates the H2O2 level and cell senescence via HSPA8.

The redox homeostasis system regulates many biological processes, intracellular antioxidant production and redox signaling. However, long noncoding RNAs (lncRNAs) involved in redox regulation have rarely been reported. Herein, we reported that downregulation of MAGI2-AS3 decreased the superoxide level in Human fibroblasts (Fbs), a replicative aging model, as detected by the fluorescent probes dihydroethidium (DHE) and MitoSOX™ Red. RNA pulldown combined with mass spectrometry showed that HSPA8 is a novel interacting protein of MAGI2-AS3, which was further confirmed by photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP). Downregulation of MAGI2-AS3 decreased the hydrogen peroxide (H2O2) content by stabilizing the HSPA8 protein level via inhibiting the protesome degradation of HSPA8. Further evidence showed that MAGI2-AS3 interacted with the C-terminal domain (CTD) of HSPA8. Downregulation of MAGI2-AS3 delayed cell senescence, while this antiaging effect was abolished by HSPA8 knockdown. The underlying molecular mechanism by which MAGI2-AS3 knockdown inhibited cell senescence was mediated via suppression of the ROS/MAP2K6/p38 signaling pathway. Taken together, these findings revealed that downregulation of lncRNA MAGI2-AS3 decreased the H2O2 content and delayed cell senescence by stabilizing the HSPA8 protein level, identifying a potential antiaging application.

Identification and characterization of long non-coding RNA Carip in modulating spatial learning and memory.

CaMKII has long been known to be a key effector for synaptic plasticity. Recent studies have shown that a variety of modulators interact with the subunits of CaMKII to regulate the long-term potentiation (LTP) of hippocampal neurons. However, whether long non-coding RNAs modulate the activity of CaMKII and affect synaptic plasticity is still elusive. Here, we identify a previously uncharacterized long non-coding RNA Carip that functions as a scaffold, specifically interacts with CaMKIIß, and regulates the phosphorylation of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-d-aspartate (NMDA) receptor subunits in the hippocampus. The absence of Carip causes dysfunction of synaptic transmission and attenuates LTP in hippocampal CA3-CA1 synapses, which further leads to impairment of spatial learning and memory. In summary, our findings demonstrate that Carip modulates long-term synaptic plasticity by changing AMPA receptor and NMDA receptor activities, thereby affecting spatial learning and memory in mice.