Phase-separated membraneless organelles often contain RNAs that exhibit unusual semi-extractability using the conventional RNA extraction method, and can be efficiently retrieved by needle shearing or heating during RNA extraction. Semi-extractable RNAs are promising resources for understanding RNA-centric phase separation. However, limited assessments have been performed to systematically identify and characterize semi-extractable RNAs. In this study, 1074 semi-extractable RNAs, including ASAP1, DANT2, EXT1, FTX, IGF1R, LIMS1, NEAT1, PHF21A, PVT1, SCMH1, STRG.3024.1, TBL1X, TCF7L2, TVP23C-CDRT4, UBE2E2, ZCCHC7, ZFAND3 and ZSWIM6, which exhibited consistent semi-extractability were identified across five human cell lines. By integrating publicly available datasets, we found that semi-extractable RNAs tend to be distributed in the nuclear compartments but are dissociated from the chromatin. Long and repeat-containing semi-extractable RNAs act as hubs to provide global RNA-RNA interactions. Semi-extractable RNAs were divided into four groups based on their k-mer content. The NEAT1 group preferred to interact with paraspeckle proteins, such as FUS and NONO, implying that RNAs in this group are potential candidates of architectural RNAs that constitute nuclear bodies.

With a large number of annotated non-coding RNAs (ncRNAs), repetitive sequences are found to constitute functional components (termed as repetitive elements) in ncRNAs that perform specific biological functions. Bioinformatics analysis is a powerful tool for improving our understanding of the role of repetitive elements in ncRNAs. This chapter summarizes recent findings that reveal the role of repetitive elements in ncRNAs. Furthermore, relevant bioinformatics approaches are systematically reviewed, which promises to provide valuable resources for studying the functional impact of repetitive elements on ncRNAs.

<jats:title>Abstract</jats:title>
<jats:p>Recent reports have revealed that repeat-derived sequences embedded in introns or long noncoding RNAs (lncRNAs) are targets of RNA-binding proteins (RBPs) and contribute to biological processes such as RNA splicing or transcriptional regulation. These findings suggest that repeat-derived RNAs are important as scaffolds of RBPs and functional elements. However, the overall functional sequences of the repeat-derived RNAs are not fully understood. Here, we show the putative functional repeat-derived RNAs by analyzing the binding patterns of RBPs based on ENCODE eCLIP data. We mapped all eCLIP reads to repeat sequences and observed that 10.75 % and 7.04 % of reads on average were enriched (at least 2-fold over control) in the repeats in K562 and HepG2 cells, respectively. Using these data, we predicted functional RNA elements on the sense and antisense strands of long interspersed element 1 (LINE1) sequences. Furthermore, we found several new sets of RBPs on fragments derived from other transposable element (TE) families. Some of these fragments show specific and stable secondary structures and are found to be inserted into the introns of genes or lncRNAs. These results suggest that the repeat-derived RNA sequences are strong candidates for the functional RNA elements of endogenous noncoding RNAs.</jats:p>

(1) Background: Acquired resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) is an intractable problem for many clinical oncologists. The mechanisms of resistance to EGFR-TKIs are complex. Long non-coding RNAs (lncRNAs) may play an important role in cancer development and metastasis. However, the biological process between lncRNAs and drug resistance to EGFR-mutated lung cancer remains largely unknown. (2) Methods: Osimertinib- and afatinib-resistant EGFR-mutated lung cancer cells were established using a stepwise method. A microarray analysis of non-coding and coding RNAs was performed using parental and resistant EGFR-mutant non-small cell lung cancer (NSCLC) cells and evaluated by bioinformatics analysis through medical-industrial collaboration. (3) Results: Colorectal neoplasia differentially expressed (CRNDE) and DiGeorge syndrome critical region gene 5 (DGCR5) lncRNAs were highly expressed in EGFR-TKI-resistant cells by microarray analysis. RNA-protein binding analysis revealed eukaryotic translation initiation factor 4A3 (eIF4A3) bound in an overlapping manner to CRNDE and DGCR5. The CRNDE downregulates the expression of eIF4A3, mucin 1 (MUC1), and phospho-EGFR. Inhibition of CRNDE activated the eIF4A3/MUC1/EGFR signaling pathway and apoptotic activity, and restored sensitivity to EGFR-TKIs. (4) Conclusions: The results showed that CRNDE is associated with the development of resistance to EGFR-TKIs. CRNDE may be a novel therapeutic target to conquer EGFR-mutant NSCLC.

Ribosome profiling shows potential for studying the function of long noncoding RNAs (lncRNAs). We introduce a bioinformatics pipeline for detecting ribosome-associated lncRNAs (ribo-lncRNAs) from ribosome profiling data. Further, we describe a machine-learning approach for the characterization of ribo-lncRNAs based on their sequence features. Scripts for ribo-lncRNA analysis can be accessed at ( https://ribolnc.hamadalab.com/ ).

BACKGROUND: With the increasing number of annotated long noncoding RNAs (lncRNAs) from the genome, researchers are continually updating their understanding of lncRNAs. Recently, thousands of lncRNAs have been reported to be associated with ribosomes in mammals. However, their biological functions or mechanisms are still unclear. RESULTS: In this study, we tried to investigate the sequence features involved in the ribosomal association of lncRNA. We have extracted ninety-nine sequence features corresponding to different biological mechanisms (i.e., RNA splicing, putative ORF, k-mer frequency, RNA modification, RNA secondary structure, and repeat element). An [Formula: see text]-regularized logistic regression model was applied to screen these features. Finally, we obtained fifteen and nine important features for the ribosomal association of human and mouse lncRNAs, respectively. CONCLUSION: To our knowledge, this is the first study to characterize ribosome-associated lncRNAs and ribosome-free lncRNAs from the perspective of sequence features. These sequence features that were identified in this study may shed light on the biological mechanism of the ribosomal association and provide important clues for functional analysis of lncRNAs.