Very long intergenic non-coding (vlinc) RNAs directly regulate multiple genes in cis and trans

Abstract

Background The majority of the human genome is transcribed in the form of long non-coding (lnc) RNAs. While these transcripts have attracted considerable interest, their molecular mechanisms of function and biological significance remain controversial. One of the main reasons behind this lies in the significant challenges posed by lncRNAs requiring the development of novel methods and concepts to unravel their functionality. Existing methods often lack cross-validation and independent confirmation by different methodologies and therefore leave significant ambiguity as to the authenticity of the outcomes. Nonetheless, despite all the caveats, it appears that lncRNAs may function, at least in part, by regulating other genes via chromatin interactions. Therefore, the function of a lncRNA could be inferred from the function of genes it regulates. In this work, we present a genome-wide functional annotation strategy for lncRNAs based on identification of their regulatory networks via the integration of three distinct types of approaches: co-expression analysis, mapping of lncRNA-chromatin interactions, and assaying molecular effects of lncRNA knockdowns obtained using an inducible and highly specific CRISPR/Cas13 system. Results We applied the strategy to annotate 407 very long intergenic non-coding (vlinc) RNAs belonging to a novel widespread subclass of lncRNAs. We show that vlincRNAs indeed appear to regulate multiple genes encoding proteins predominantly involved in RNA- and development-related functions, cell cycle, and cellular adhesion via a mechanism involving proximity between vlincRNAs and their targets in the nucleus. A typical vlincRNAs can be both a positive and negative regulator and regulate multiple genes both in trans and cis. Finally, we show vlincRNAs and their regulatory networks potentially represent novel components of DNA damage response and are functionally important for the ability of cancer cells to survive genotoxic stress. Conclusions This study provides strong evidence for the regulatory role of the vlincRNA class of lncRNAs and a potentially important role played by these transcripts in the hidden layer of RNA-based regulation in complex biological systems.

Chromatin lncRNA Platr10 controls stem cell pluripotency by coordinating an intrachromosomal regulatory network

“…Differential binding analysis was performed with the DiffBind package using parameters of fold-change difference >2 and p value < 0.05, with false discovery rate (FDR) <0.1. The adjusted RAT-Seq data were used for mapping the lncRNA target gene interaction network [ 44 , 45 ].…”

Methods to Analyze the Non-Coding RNA Interactome—Recent Advances and Challenges

“…Therefore, the RNA-centric interactome mapping methods employ a variety of steps in terms of both the design of the assays and controls to ensure the specificity of the detected interactions. Such approaches typically start with careful selection of the probes to avoid sequences that are repetitive in the genome ( Chu et al, 2011 ; Cao et al, 2021 ; Chu et al, 2021 ). Furthermore, in some studies, the oligonucleotides targeting the same RNA are split into two non-overlapping pools and the RNA-interactome mapping is performed independently using each pool, and subsequently, only interactions detected using both pools are kept ( Chu et al, 2011 ; Cao et al, 2021 ).…”

“…Considering the complexities of the methodologies and potential for detection of non-specific interactions, in addition to the experimental design, a number of common controls are often incorporated into the RNA interactome mapping studies. As expected, the RNA-centric methods ( Table 1 ) often include experiments to control for oligonucleotide specificity, for example, performed with oligonucleotides in the sense polarity of the targeting RNAs (and thus not expected to bind to these transcripts) and/or scrambled sequences to estimate contribution of the genomic DNA and non-specific binding to the resulting signal [for example, ( Simon et al, 2011 ; Engreitz et al, 2013 ; West et al, 2014 ; Chu et al, 2021 ; Yap et al, 2021 )]; or performed in the absence of the targeting oligonucleotides to estimate the contribution of experimental noise ( Cao et al, 2021 ; Yap et al, 2021 ). Similarly, the in vivo RNA-interactome mapping techniques that rely on CRISPR/dCas13 ( Table 1 ) employ non-specific gRNA or empty vectors that do not express gRNAs as controls ( Yi et al, 2020 ; Lin et al, 2021 ).…”

“…For example, the study by Morf et al (2019) that developed Proximity RNA-seq has used a conversion coefficient of 0.01 nm per bp of DNA in chromatin fibers to estimate the resulting size distribution of particles from that of the fragmented DNA ( Morf et al, 2019 ). To our knowledge, only a study from our group by Cao et al (2021) that used the RAT technique (see below) directly measured sizes of fragmented crosslinked chromatin particles using flow sorting ( Cao et al, 2021 ). Interestingly, we found that the size of the chromatin particles reached 300–500 nm, even though the fragmented DNA was below 500 bp ( Cao et al, 2021 ).…”

“…To our knowledge, only a study from our group by Cao et al (2021) that used the RAT technique (see below) directly measured sizes of fragmented crosslinked chromatin particles using flow sorting ( Cao et al, 2021 ). Interestingly, we found that the size of the chromatin particles reached 300–500 nm, even though the fragmented DNA was below 500 bp ( Cao et al, 2021 ). These results suggest that methods based on fragmented crosslinked chromatin can measure very distal interactions, presumable more distal than the methods that use proximity ligation ( Cao et al, 2021 ).…”