ArticlePDF AvailableLiterature Review

Multimodal Long Noncoding RNA Interaction Networks: Control Panels for Cell Fate Specification

December 2019
Genetics 213(4):1093-1110

December 2019
213(4):1093-1110

DOI:10.1534/genetics.119.302661

Authors:

Sarah Miller

Johns Hopkins Medicine

Show all 5 authorsHide

Lineage specification in early development is the basis for the exquisitely precise body plan of multicellular organisms. It is therefore critical to understand cell fate decisions in early development. Moreover, for regenerative medicine, the accurate specification of cell types to replace damaged/diseased tissue is strongly dependent on identifying determinants of cell identity. Long noncoding RNAs (lncRNAs) have been shown to regulate cellular plasticity, including pluripotency establishment and maintenance, differentiation and development, yet broad phenotypic analysis and the mechanistic basis of their function remains lacking. As components of molecular condensates, lncRNAs interact with almost all classes of cellular biomolecules, including proteins, DNA, mRNAs, and microRNAs. With functions ranging from controlling alternative splicing of mRNAs, to providing scaffolding upon which chromatin modifiers are assembled, it is clear that at least a subset of lncRNAs are far from the transcriptional noise they were once deemed. This review highlights the diversity of lncRNA interactions in the context of cell fate specification, and provides examples of each type of interaction in relevant developmental contexts. Also highlighted are experimental and computational approaches to study lncRNAs.

General principles illustrating lncRNA subtypes and genomic origin. LncRNAs may originate from various regions in the genome, including proximal, distal, and overlapping, with respect to protein coding genes. Sense and antisense lncRNAs may, or may not, fully overlap with protein coding genes. Divergent and intergenic lncRNAs are arbitrarily distinguished based on distance from the nearest protein coding gene.

…

By engaging in diverse interaction patterns, a single lncRNA can impact multiple cellular processes. LncRNAs harness different mechanisms and access multiple networks of interacting partners in a context-specific manner. (A)TUG1 can (1) regulate neighboring genes in cis, (2) function in trans to regulate target genes, (3) be translated into a micropeptide that regulates mitochondrial membrane potential, and (4) interact with proteins such as Lin28A to regulate various cell fate decisions. (B) Cyrano's multifaceted functions are illustrated by (1) its ability to inhibit miR-7-mediated repression of Itga9 and Nanog, (2) interact with proteins to support maintenance of the self-renewing pluripotent state, and (3) function in a multi-RNA regulatory network to impact Cdr1as expression, and, ultimately, neuronal activity and neuropsychiatric behavior in mice.

…

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|REVIEW

Multimodal Long Noncoding RNA Interaction

Networks: Control Panels for Cell Fate Speciﬁcation

Keriayn N. Smith,*

,1,2

Sarah C. Miller,*

,1,3

Gabriele Varani,

†

J. Mauro Calabrese,

‡

and Terry Magnuson*

*Department of Genetics and ‡Department of Pharmacology, University of North Carolina, Chapel Hill, North Carolina 27599, and

†Department of Chemistry, University of Washington, Seattle, Washington 98195

ORCID IDs: 0000-0002-4351-2765 (K.N.S.); 0000-0003-3470-8798 (S.C.M.); 0000-0001-6642-7144 (G.V.); 0000-0002-1213-2540 (J.M.C.);

0000-0002-0792-835X (T.M.)

ABSTRACT Lineage speciﬁcation in early development is the basis for the exquisitely precise body plan of multicellular organisms. It is

therefore critical to understand cell fate decisions in early development. Moreover, for regenerative medicine, the accurate speciﬁcation

of cell types to replace damaged/diseased tissue is strongly dependent on identifying determinants of cell identity. Long noncoding

RNAs (lncRNAs) have been shown to regulate cellular plasticity, including pluripotency establishment and maintenance, differentiation

and development, yet broad phenotypic analysis and the mechanistic basis of their function remains lacking. As components of

molecular condensates, lncRNAs interact with almost all classes of cellular biomolecules, including proteins, DNA, mRNAs, and

microRNAs. With functions ranging from controlling alternative splicing of mRNAs, to providing scaffolding upon which chromatin

modiﬁers are assembled, it is clear that at least a subset of lncRNAs are far from the transcriptional noise they were once deemed. This

review highlights the diversity of lncRNA interactions in the context of cell fate speciﬁcation, and provides examples of each type of

interaction in relevant developmental contexts. Also highlighted are experimental and computational approaches to study lncRNAs.

KEYWORDS long noncoding RNAs; miRNAs; competing endogenous RNAs; k-mers; cell fate speciﬁcation

LINEAGE speciﬁcation decisions in early development pro-

vide a blueprint of the body plan in multicellular organ-

isms. Model systems such as embryonic stem (ES) cells are

often employed in the study of early cell fate decisions. Un-

derstanding cell fate is also critical for regenerative medicine,

as cell-based approaches pose signiﬁcant therapeutic promise.

Toward this end, induced pluripotent stem (iPS) cells, which

display characteristics of ES cells, and can be patient-derived,

have the potential to be differentiated into a myriad of

different cell types.

Understanding determinants of cell fate is critical both for

understanding early development, and to guide lineage com-

mitment of pluripotent stem cells to enable the replacement

of diseased cell types in patients. While central transcrip-

tional regulators of pluripotency including OCT4, SOX2,

and NANOG, which maintain the pluripotent state, and spec-

iﬁcation factors such as SOX1, MEOX1, and SOX17 (Kan et al.

2004; Shimoda et al. 2007; Wang et al. 2013) are relatively

well understood, many key cell-fate determinants remain

functionally undeﬁned. Importantly, recent developments

in transcriptomics have demonstrated that, although the ma-

jority of the mammalian genome is transcribed, protein coding

sequences amount to ,2% of transcribed genomic sequence

(Dinger et al. 2008; Alexander et al. 2010; Harrow et al. 2012),

with the number of noncoding RNA (ncRNA) genes equaling,

or possibly even outnumbering, protein-coding genes based on

estimates from GENCODE and FANTOM (Hon et al. 2017;

Frankish et al. 2019). Already, certain noncoding transcripts,

including microRNAs (miRNAs) and long noncoding RNAs

(lncRNAs; designated as transcripts .200 nt) have been im-

plicated in cell fate decisions for unspecialized cells, including

pluripotent stem cells; however, the vast majority of ncRNAs

remain understudied.

Using pluripotent cells and their derivatives for illustra-

tions, this review centers on lncRNAs, with a focus on the

doi: https://doi.org/10.1534/genetics.119.302661

Manuscript received September 11, 2019; accepted for publication October 3, 2019;

published Early Online October 16, 2019.

These authors contributed equally to this work.

Corresponding author: Department of Genetics, University of North Carolina, Room

5028 Genetic Medicine Bldg. CB#7264, 120 Mason Farm Road, Chapel Hill, NC

27599. E-mail: kns@email.unc.edu

Present address: Johns Hopkins University School of Medicine, Baltimore, MD

21205.

Genetics, Vol. 213, 1093–1110 December 2019 1093

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multimodal interactions through which they regulate cell fate

speciﬁcation. These interactions identify lncRNAs as impor-

tant factors in early developmental processes and suggest that

they should be considered in the design of regenerative

medicine strategies.

LncRNAs: Interactions as Functional Determinants

The presence and number of lncRNAs appear to correlate

with organismal complexity, and their expression patterns

show subcellular, cellular, and tissue speciﬁcity, which sug-

gests context-dependent roles, particularly in the determina-

tion of cell fate (Mattick 2001). Different classes of lncRNAs

are deﬁned based on transcription direction and location (for

example: sense, bidirectional, antisense, intronic, intergenic;

Figure 1) relative to other genes (Mattick and Rinn 2015).

While this location classiﬁcation might be suggestive of

mechanism, lncRNA genomic location does not always

strictly dictate function (Mattick and Rinn 2015; Quinn and

Chang 2016). On the other hand, intermolecular interactions

with other RNAs, proteins and chromatin have revealed

emerging functional themes, and demonstrated the far-

reaching regulatory potential of lncRNAs. Here, we examine

the implication of these interactions in cell fate determina-

tion and early developmental processes.

Much of the function of lncRNAs depend on their ability

to base pair to other RNAs or DNA through conventional

or Hoogsteen base pairing, to form complex intramolecular

and intermolecular secondary and higher order structures

(Mercer and Mattick 2013). The structures formed by

lncRNAs regulate and direct interaction with RNA-binding

proteins (RBPs) to regulate, negatively or positively, their

cellular targets. These proteins are central to lncRNA mech-

anisms of action and regulation of their function (Rinn and

Ule 2014).

Localization of a lncRNA transcript can be suggestive of

its functional role and contribution to gene regulation. While

cytoplasmic lncRNAs tend to function post-transcriptionally,

many nuclear lncRNAs regulate gene expression at the tran-

scriptional level (Rinn and Chang 2012; Mercer and Mattick

2013). Within the nucleus, expression of a gene requires

chromatin decompaction, particularly in heterochromatic re-

gions. The compaction state is determined by chemical mod-

iﬁcations of nucleosomal histone proteins, controlled by

histone-modifying enzymes. LncRNAs have been shown to

interact with chromatin modiﬁers (readers, writers, erasers)

and remodelers to facilitate changes in the chromatin’s bio-

chemical and accessibility landscape at speciﬁc gene loci

through both cis- and trans-acting mechanisms (Figure 2)

(Rinn and Chang 2012).

Additionally, lncRNAs have also been shown to have a

number of post-transcriptional and cytoplasmic functions in

many developmental processes. These include functioning

in mRNA stability and translation regulation through pro-

tein, miRNA, and mRNA interactions (Figure 2) (Batista and

Chang 2013; Yoon et al. 2013; Quinn and Chang 2016).

These interactions provide a basis for lncRNA functional clas-

siﬁcation, but can also be targeted to direct cell fate.

Interactions with proteins: chromatin regulation

The plasticity of pluripotent stem cells is related to the high

ratio of euchromatin to heterochromatin (Gaspar-Maia et al.

2011), making more chromatin accessible to transcription

factors, RNA polymerase, and other proteins necessary for

transcription. Pluripotent stem cells also have a high propor-

tion of poised chromatin (Fisher and Fisher 2011), which

facilitates the rapid gene derepression required for lineage

commitment. Extensive binding of lncRNAs to epigenetic reg-

ulators that control chromatin accessibility deﬁnes one cate-

gory of lncRNA function (Mercer and Mattick 2013). Studies

using ES cells and other cell types have shown that 30% of

intergenic lncRNAs were bound by at least one epigenetic

regulator (Khalil et al. 2009), indicating widespread impact

of lncRNAs on cell identity at the transcriptional level.

LncRNAs can act in cis by binding to neighboring genes and

facilitating recruitment of chromatin modiﬁer/remodelers

to the target locus (Bassett et al. 2014). The act of lncRNA

transcription can also have a cis-regulatory function in gene

expression, and inﬂuence genome organization (Bassett et al.

2014; Engreitz et al. 2016; Melé and Rinn 2016). LncRNAs

can function in trans as well, either by serving as a recruit-

ment or scaffolding factor on which chromatin modifying

proteins assemble, or by modulating the stability of the chro-

matin regulatory protein complex (Rinn and Chang 2012;

Bassett et al. 2014).

Cis-regulatory lncRNAs control expression of neighboring

genes. In the context of cell fate speciﬁcation, these lncRNA

genes are often located adjacent to key developmental regu-

lators that determine cell fate and organismal development

(Bassett et al. 2014; Engreitz et al. 2016; Melé and Rinn

2016). This mechanism is commonly used for antisense and

divergent lncRNAs that are typically ,5 kb from, and tran-

scribed in the opposite direction relative to, a transcribed

gene. For example, Evx1as and its neighboring protein-

coding gene, Evx1, demonstrate highly correlated expression

in murine ES cells (Luo et al. 2016). Depletion of Evx1as

indicated unidirectional regulation of the protein-coding

neighbor by the lncRNA where Evx1as bound to its own pro-

moter and facilitated binding of Mediator to activate tran-

scription at the locus (Luo et al. 2016). This example

highlights how lncRNAs can act in cis, tethered to their pro-

moter, to modify gene expression near their transcription site.

Another illustration of cis inﬂuence of a lncRNA is exem-

pliﬁed by Chaserr’s regulation of Chd2—a chromatin remod-

eler with roles in cell differentiation in mice (Rom et al.

2019). Chaserr’s transcript is produced upstream of the tran-

scription start site of Chd2, where it collaborates with CHD2

protein to repress Chd2’s expression in a negative feedback

loop to maintain cellular levels of CHD2 (Rom et al. 2019).

yylncT—a member of the divergent subclass of lncRNAs

known as yin yang (yy) lncRNAs—supports expression of its

gene neighbor, Brachyury (T) by localizing to its locus during

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mesoderm commitment in human ES cells (Frank et al.

2019). yylncRNAs are primarily encoded from genomic loci

of key cell-fate regulators, thus mirroring their developmen-

tal expression patterns, and, as a class, they illustrate a broad

mechanism through which lncRNAs safeguard cell-fate deci-

sions (Frank et al. 2019).

A handful of cis-acting lncRNAs are also known to repress

gene expression over long genomic distances. In the most

extreme example, the lncRNA Xist silences gene expression

over the entire 165 million base pair X chromosome early

during the development of female mammals, as part of the

dosage compensation process called X-chromosome Inactiva-

tion (XCI) (recently reviewed by Sahakyan et al. 2018).

Xist induces stable gene silencing through two parallel

pathways. In the ﬁrst, Xist silences actively transcribed genes

through an incompletely deﬁned mechanism that involves

the protein SPEN and the RNA element Repeat A, which is

a tandem repeat located at the 59end of Xist. In parallel, and

subsequent to Repeat-A-mediated silencing, Xist induces the

spread of Polycomb Repressive Complexes (PRCs) over tran-

scriptionally inactive chromatin (Nesterova et al. 2019; ˙

Zylicz

et al. 2019). In a mechanistic sense, this spread of PRCs over

the X is likely a major means by which Xist orchestrates stable

silencing that is inherited through subsequent cell divisions

(Wang et al. 2001; Kalantry et al. 2006; Sahakyan et al.

2018). Other cis-repressive lncRNAs that depend on PRCs

for their silencing functions, such as Kcnq1ot1,Airn,

Morrbid, and Haunt, may utilize similar mechanisms to bring

PRCs to chromatin (Regha et al. 2007; Terranova et al. 2008;

Yin et al. 2015; Kotzin et al. 2016; Schertzer et al. 2019).

Indeed, both Kcnq1ot1 and Airn were recently shown to re-

quire the Xist cofactor HNRNPK to induce the spread of PRCs

in mouse trophoblast stem cells (Schertzer et al. 2019).

In addition, the PRCs, particularly PRC2, have been shown

to interact with many RNAs, and the functional consequence

of this interaction has not always been clear. For example, the

PRC2 component SUZ12 has been shown to interact with a

lncRNA to repress a differentiation-inducing transcriptional

program in human ES cells. Here, the lncRNA tsRMST uses

multiple mechanisms, including coregulation with SUZ12

and NANOG, to block expression of lineage speciﬁcation

genes and impede WNT5A-induced epithelial-mesenchymal

transition (Yu and Kuo 2016). SUZ12 and the central pluri-

potency regulator SOX2 also interacts with lncRNA_ES1 and

lncRNA_ES2 to contribute to pluripotency maintenance (Ng

et al. 2012) through unclear mechanisms.

Other lncRNAs, at least partially through their interaction

with PRC2, have been implicated in processes supporting

lineage commitment. For example, the lncRNA Braveheart

interacts with PRC2, and, perhaps in part due to a conse-

quence of this interaction, Braveheart directs murine plurip-

otent cells to a cardiac fate by moderating a mesoderm and

cardiac-speciﬁc transcription factor network (Klattenhoff

et al. 2013). Nevertheless, through a speciﬁc structured ele-

ment, Braveheart interacts with the CNBP/ZNF9 nucleic acid

binding protein, and at least a portion of Braveheart function

can be ascribed to the CNBP/ZNF9 interaction (Xue et al.

2016). Moreover, and surprisingly, even though depletion

of Braveheart results in myogenic defects through its control

of central cardiomyogenic regulators including Mesp1,

Hand1,Nkx2.5, and Tbx20, and general loss of sarcomere

gene expression, Braveheart null mice were grossly pheno-

typically normal (Han et al. 2018). Conversely, genetic abla-

tion of the lncRNA Fendrr, which has also been shown to

interact with PRC2, results in mouse embryonic lethality at

around E13.75 due to myocardial defects (Grote et al. 2013).

Here, it has been proposed that Fendrr’sinteraction with both

PRC2 and Trithorax Group/MLL complexes modulates chro-

matin signatures in control of lateral mesoderm differentia-

tion (Grote et al. 2013). PRC2 function has also been

reported to be regulated in trans, in mouse ES cells and hu-

man iPS cells, by the relatively abundant lncRNAs Rian,Mirg,

and Meg3/Gtl2, which are produced from an imprinted clus-

ter (Kaneko et al. 2014). Examples of these lncRNAs with

clear roles for repressing transcription in cis also exist

(Sanli et al. 2018). Additionally, many lncRNAs produced

from the developmentally important Hox gene clusters also

bind PRC2, the most notable of which may be the lncRNA

HOTAIR. The extent to which lncRNA/PRC2 interactions in

the Hox clusters contribute to gene regulation in mammalian

development, whether the regulation occurs in cis or in trans,

and what the mechanisms are, however, remain unclear (Li

et al. 2013, 2016; Tsai et al. 2010; Amândio et al. 2016;

Selleri et al. 2016; Portoso et al. 2017).

Figure 1 General principles illus-

trating lncRNA subtypes and ge-

nomic origin. LncRNAs may

originate from various regions in

the genome, including proximal,

distal, and overlapping, with re-

spect to protein coding genes.

Sense and antisense lncRNAs

may, or may not, fully overlap

with protein coding genes. Diver-

gent and intergenic lncRNAs are

arbitrarily distinguished based on

distance from the nearest protein

coding gene.

Multimodal lncRNA Interactions 1095

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Importantly, both crosslinking immunoprecipitation (CLIP)

studies of PRC2 as well as those that have studied the RNA-

binding properties of PRC2 in vitro have found that PRC2 binds

RNA with little sequence speciﬁcity and nanomolar afﬁnity

(Kaneko et al. 2013, 2014; Davidovich et al. 2015; Wang

et al. 2017). Collectively, these studies suggest that one func-

tion of lncRNAs, and perhaps chromatin-bound RNAs in gen-

eral, is to tether PRC2 to transcriptionally active regions of

chromatin. This tethering may keep PRC2 in a poised state,

in close proximity to future target genes, where it can initiate

stable gene silencing upon receipt of the appropriate cues

(Kaneko et al. 2013, 2014; Davidovich et al. 2015; Wang

et al. 2017). High-afﬁnity and nonspeciﬁc interactions with

RNA may also govern PRC1 function in an analogous fashion

(Bernstein et al. 2006; Bonasio et al. 2014).

LncRNAshave beenshown tointeract with a wide variety of

chromatin modulatory factors. In addition to PRCs, these

include histone methylases (Hendrickson et al. 2016). The

H3K4 methylase MLL family is necessary for activating the

expression of certain genes (Yang et al. 2014). WDR5 is a

Figure 2 Schematic illustration of the different modes of action for lncRNAs. Localization of lncRNAs to the nucleus or cytoplasm can dictate different

mechanisms of action. Based on their ability to bind to DNA and interact with proteins, lncRNAs can guide transcription regulators and epigenetic

modulators (A); act as scaffolds to assemble chromatin regulatory factors (B); titrate away regulators of transcription by acting as decoys (C); regulate

domain- or chromosome-wide chromatin state to regulate transcriptional output (D); act as ceRNAs to capture regulatory factors such as miRNAs away

from target genes (E); contribute to the stabilization of protein complexes, proteins, and mRNAs (F); and inﬂuence alternative splicing (G).

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protein-subunit of MLL, recruiting the complex to target sites

for activation (Yang et al. 2014). WDR5 engages with several

lncRNAs that have been implicated in the self-renewal of ES

cells (Yang et al. 2014). When the RNA-binding site of WDR5

was mutated in mouse ES cells, rendering it unable to bind

lncRNAs, WDR5 was signiﬁcantly less stable (Yang et al.

2014). This loss of stability resulted in a severe decrease in

H3K4me3 marks on the promoters of pluripotency-related

genes, and a loss of the ES cell state in 50% of colonies

(Yang et al. 2014).

Interactions between lncRNAs and chromatin readers are

exempliﬁed by the interaction between DIGIT and BRD3 in

the regulation of endoderm differentiation (Daneshvar et al.

2016, 2019). Here, DIGIT supports BRD3 recruitment to

H3K18ac at regions in the genome enriched during endo-

derm differentiation in human ES cells (Daneshvar et al.

2019).

In addition to chromatin remodelers and modiﬁers, anal-

ysis of lncRNA interactomes has also identiﬁed transcription

factors as key players in lncRNA function. Panct1—a nuclear-

functioning sense lncRNA transcribed from an intron of the

gene coding for its protein interacting partner, TOBF1—

exempliﬁes this type of trans interaction in mouse ES cells

(Chakraborty et al. 2017). Panct1 was shown to facilitate the

binding of TOBF1 to pluripotency marker promoters by way

of sequence-directed binding to Oct-Sox motifs, thus recruit-

ing transcription factors such as Oct4 to promote target ex-

pression (Chakraborty et al. 2017). This interaction illustrates

the role of a lncRNA in efﬁcient binding of pluripotency-

associated transcription factors to their target sites without

direct interaction (Chakraborty et al. 2017).

The aforementioned examples demonstrate the breadth of

lncRNA–protein interactions that inﬂuence transcription in

developmental processes, and on which cell speciﬁcation is

at least partly dependent. Many of the described lncRNA

interactions involve key transcription factors and epigenetic

regulators that affect developmental progression. Modula-

tion of speciﬁc lncRNAs could therefore be a viable avenue

for speciﬁc regulation of target expression in developmental

contexts.

Interactions with proteins: stability and sequestration

LncRNAs enhance or repress protein function through a va-

riety of mechanisms including sequestration, binding support,

and degradation, as in the aforementioned case of WDR5 and

its reliance on lncRNAs for stability (Yang et al. 2014). Con-

versely, in other contexts, lncRNAs have been shown to be

dependent on their protein partners for stability to carry out

their functions, and regulate their half-life. This is the case for

lncR492—a noncoding transcript that inhibits neural differ-

entiation in mouse ES cells (Winzi et al. 2018). Knockdown of

lncR492 resulted in increased expression of neural markers

such as Pax6 and Nestin during differentiation. Proteomic

analysis indicated that lncR492 directly interacts with HuR—

a mRNA binding protein withfunctions inthe (de)stabilization

of mRNA transcripts. Overexpression and knockdown of HuR

moderated the expression of lncR492, increasing and reducing

the prevalence of the lncRNA, respectively—a pattern that

suggests HuR supports the stability of the lncR492 transcript.

Finally, both lncR492 and HuR positively inﬂuence WNT sig-

naling, which has a known inhibitory effect on neural differ-

entiation (Haegele et al. 2003), outlining the axis by which

lncR492 and HUR function (Winzi et al. 2018).

LncRNAs can impede protein function through binding and

sequestration. In the context of pluripotency, the chromatin

mark H3K56 acetylation activates core pluripotency-related

genes and is required for the maintenance of the undifferen-

tiated ES cell state. SIRT6 is a chromatin-binding protein that

removes this chromatin modiﬁcation and functionally re-

presses pluripotency-related genes to promote exit from the

stem cell state (Etchegaray et al. 2015). LncPRESS1 functions

as a molecular decoy for SIRT6, sequestering the protein,

which binds to the 39-end of the lncRNA in human ES cells.

This interaction in the nucleus prevents SIRT6 from binding

promoters of pluripotency-related genes and repressing tran-

scription via deacetylation (Jain et al. 2016). Another regu-

latory interaction in this network is illustrated by P53, which

antagonizes lncPRESS1, freeing SIRT6 to further repress plu-

ripotency markers in human ES cells (Jain et al. 2016).

Emerging data indicate that regulatory lncRNA–protein

interactions occur in specialized microenvironments that dis-

play characteristics of phase-separated particles (Hnisz et al.

2017; Daneshvar et al. 2019). These liquid-like condensates

are able to exchange molecules dynamically with their sur-

roundings (Bergeron-Sandoval et al. 2016; Boeynaems et al.

2018; Lu et al. 2018). The preceding interactions demon-

strate the role lncRNA-protein interactions play in integral

processes throughout differentiation and development. Such

interactions can be investigated as points of manipulation for

control of differentiation processes, especially when they lo-

calize to distinct microdroplets, and when the mechanisms

for the interactions have been clearly deﬁned.

Interactions with RNA

Similar to the prevalence of lncRNA–protein interactions,

lncRNA–RNA interactions are proliﬁc and have widespread

effects on cell identity. Interestingly, lncRNAs interplay with

multiple other RNA types, from mRNA to other ncRNA, in-

cluding miRNAs and circular RNAs. Through different mech-

anisms, these lncRNA-RNA interactions can affect lncRNA

function through repressing or supporting downstream

targets.

Canonical functions of mRNAs are determined by their

availability and potential to be translated. In addition to long-

characterized protein factors, the half-life of a mRNA is de-

termined by various co- and post-transcriptional regulatory

factors, including lncRNAs. LncRNA interaction with mRNAs

or mRNA-regulatory factors can stabilize or facilitate the

degradation of the mRNA molecules, increasing or decreasing

translational output (Faghihi et al. 2008, 2010; Gong and

Maquat 2011). For example, Sirt1-AS interacts with Sirt1

mRNA to promote its stability, thereby inhibiting myogenic

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differentiation in favor of myoblast proliferation in mice

(Wang et al. 2016).

LncRNAs have also been shown to alter the translation

output of mRNAs by affecting alternative splicing. A prime

example of this mechanism of action is the interaction of

Zeb2-NAT—an antisense lncRNA—with its sense transcript,

protein-coding Zeb2.Zeb2-NAT was demonstrated to bind

Zeb2’s59UTR, which contains an intron where the internal

ribosome entry site for the ZEB2 protein resides (Beltran et al.

2008). Without protection of the ﬁrst intron by lncRNA bind-

ing, ZEB2 protein levels are signiﬁcantly diminished. In mice,

Zeb2-NAT and ZEB2 prevent ﬁbroblasts from being effectively

reprogrammed to the pluripotent state, possibly by support-

ing senescence due to E-cadherin downregulation (Beltran

et al. 2008; Bernardes de Jesus et al. 2018). Conversely, un-

der conditions of decreased Zeb2-NAT expression, the mouse

ﬁbroblasts readily transitioned to ES cell-like cells in media

conditions that support the pluripotent state (Bernardes de

Jesus et al. 2018). Further, mouse ES cells in Zeb2-NAT knock-

down conditions were able to maintain the pluripotent state

in differentiation-inducing contexts (Bernardes de Jesus et al.

2018).

Perhaps even more impactful, based on their numerous

targets, is the inﬂuence of lncRNAs on miRNAs. These small

ncRNAs (22 nt long) are key post-transcriptional regula-

tory factors that inﬂuence target transcript translational re-

pression and/or degradation (Heinrich and Dimmeler 2012).

miRNA function has been implicated in the establishment

and maintenance of ES cell pluripotency and differentiation

(Heinrich and Dimmeler 2012). Generally, miRNAs and as-

sociated proteins assemble to form the RNA-induced silenc-

ing complex (RISC), in which the miRNA serves as a guide to

target speciﬁc mRNAs, which are degraded in proportion to

the degree of complementarity with the miRNA (Gregory

et al. 2005). LncRNAs can affect the efﬁciency of these

processes as well, since they can dictate the abundance

of individual miRNAs by supporting miRNA stability or by

causing their degradation, for example through template-

mediated degradation (Fuchs Wightman et al. 2018). Addi-

tionally, lncRNAs can alter miRNA function by behaving as a

sponge or competing endogenous RNA (ceRNA) that seques-

ters the miRNA, thus preventing degradation of the miRNA

target genes to support or promote exit from the stem cell

state (Liu et al. 2014, see Table 1).

Linc-RoR (Regulator of Reprogramming) exempliﬁes the

ceRNA mechanism in the context of reprogramming and the

maintenance of pluripotency. Deviation from precise linc-RoR

levels results in the differentiation of human ES cells to me-

soderm and/or endoderm if its levels are depleted, or the

inability of cells to properly differentiate if linc-RoR levels

are elevated. Linc-ROR was shown to be a ceRNA for miR-

145-5p, suppressing the miRNA’s negative regulation of stem

cell regulatory factors such as OCT4 and SOX2 (Loewer et al.

2010; Wang et al. 2013).

While linc-ROR guides reprogramming, lncRNA-1064 was

shown to support neural differentiation (Weng et al. 2018).

Knockdown of lncRNA-1064 led to a decrease in neural line-

age markers and reduced neural differentiation of mouse ES

cells in vitro and in vivo in a murine teratoma model (Weng

et al. 2018). Mechanistic analysis revealed lncRNA-1064 con-

tains multiple miRNA target sites, with the most favorable

being for mir-200c (Weng et al. 2018). lncRNA-1064-medi-

ated sequestration of miR-200c transcripts enables ZEB1/2 to

reach their gene targets to signal for neural differentiation

(Weng et al. 2018). This interaction is similar to that of

lncRNA AK048794, which functions as a ceRNA with miR-

592 (Zhou et al. 2016). In mouse ES cells, miR-592 was found

to bind the 39UTR of FAM91A1, reducing its protein levels

and those of pluripotency regulators Oct4,Sox2, and Nanog,

although the downstream mechanism is less clear. Alto-

gether, these studies describe instances of lncRNAs function-

ing as ceRNAs, sequestering miRNAs, and preventing

degradation of miRNA targets to either support or undermine

pluripotency. Often, lncRNAs acting as ceRNAs can be a part

of a multi-component network, such as the case of AK048794

(Zhou et al. 2016).

Because of their prevalence and well-delineated mecha-

nism, lncRNA control of miRNA levels and availability could

be harnessed to modulate miRNA levels in therapeutic con-

texts,particularly forcases wherea wholesale loss of the target

miRNA would be phenotypically disadvantageous (Kleaveland

et al. 2018). Effective ceRNA activity would require a minimum

threshold level for the lncRNA. Therefore, one important con-

sideration pertaining to ceRNA/sponge function may be the

relative abundance of the lncRNA, miRNA, and mRNA target.

This is because the number of miRNA and target molecules

would typically outnumber that of a lncRNA (Palazzo and Lee

2015). Another key consideration is whether, and how, RNA

structure changes in response to the binding of proteins, other

RNAs, or even small molecule metabolites to regulate the activ-

ity of ceRNA, as this could inﬂuence access to speciﬁcsites/

sequences on the RNA. This is an aspect of lncRNA regulation

that has escaped sustained attention so far, but is likely to be

an important aspect of ceRNA, and, more generally, lncRNA

regulation.

Multimodal interactions

Since lncRNA functions can vary widely, it could be expected

that their functions are multifaceted (Figure 3) and not nec-

essarily dependent on a single mechanism even in a single

cell type. Previously characterized as a lncRNA, Tug1, is pro-

duced from a highly conserved locus (Young et al. 2005), and

is involved in many developmental processes including pho-

toreceptor speciﬁcation, axonal differentiation, and osteo-

genesis regulation, where some functions have been found

to be mediated by protein interactions with key cell-fate reg-

ulators such as LIN28 (Young et al. 2005; Guo et al. 2018; He

et al. 2018). While knockout mice are viable, they display

sterility with complete penetrance due to defects in sper-

matogenesis (Lewandowski et al. 2019). Intriguingly, in ad-

dition to having two distinct noncoding functions, one of

Tug1’s functions is dependent on an encoded micropeptide.

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Table 1 Paradigmatic modes of lncRNA interaction and functional output using ES cells, differentiation and developmental processes as

models for cell fate speciﬁcation

lncRNA Mode of action Interactor(s) Result Reference

Airn Transcriptional Igf2r Silences Igf2r cluster to guide development Santoro et al. (2013)

AK028326 Transcriptional Oct4 Positively regulates Oct-4 to promote self-renewal Sheik Mohamed et al.

(2010)

Apela RNA Transcriptional p53, hnRNPL Interacts with hnRNPL to repress p53-induced apoptosis M. Li et al. (2015)

Braveheart Transcriptional PRC2 (Suz12), MesP1 Regulates cardiac lineage commitment in ES cells Klattenhoff et al. (2013)

Chaserr Transcriptional CHD2 Inﬂuences cell differentiation through regulation of Chd2 Rom et al. (2019)

Deanr1 Transcriptional SMAD2/3, FoxA2 Recruits SMAD2/3 to the FOXA2 promoter to promote

endoderm differentiation

Jiang et al. (2015)

DIGIT Transcriptional GSC, BRD3 Supports endoderm differentiation Daneshvar et al. (2016)

Evx1as Transcriptional Evx1 Promotes Evx1 expression to regulate mesendodermal dif-

ferentiation

Luo et al. (2016)

Fendrr Transcriptional PRC2, Trithorax

group

Targets promoters for proper heart and body wall formation Grote et al. (2013)

FIRRE Transcriptional CTCF Preservation of silencing of inactive X chromosome Yang et al. (2015)

Haunt Transcriptional HOXA Inhibits HOXA expression and ES cell differentiation, whereas

the Haunt locus is an enhancer for HOXA

Yin et al. (2015)

HERVH Transcriptional Oct4 Recruits Oct4 to maintain pluripotency Lu et al. (2014)

HOTTIP Transcriptional WDR5 Binds WDR5 to activate developmental regulators Wang et al. (2011)

LncPress1 Transcriptional SIRT6 Binds SIRT6 to promote expression of pluripotency-related

genes

Jain et al. (2016)

lncR492 Transcriptional HuR Associates with HuR to promote pluripotency Winzi et al. (2018)

lncRNA_ES1 Transcriptional SUZ12, SOX2 Interacts with SUZ12 and SOX2 to prevent differentiation Ng et al. (2012)

lncRNA_ES2 Transcriptional SUZ12, SOX2 Interacts with SUZ12 and SOX2 to prevent differentiation Ng et al. (2012)

Meg3/Gtl2 Transcriptional PRC2 (JARID2) Regulates recruitment of PCR2 to chromatin in iPS cells Kaneko et al. (2014)

Panct1 Transcriptional TOBF1 Regulates recruitment of TOBF1 to Oct-Sox Motifs to support

the pluripotent state

Chakraborty et al. (2017)

pRNA Transcriptional TIP5, TTF1 Interaction with TIP5, TTF1 contributes to heterochromatin

formation required for differentiation

Savi´

cet al. (2014)

RMST Transcriptional SOX2 Associates with SOX2 to regulate neural differentiation Ng et al. (2013)

tsRMST Transcriptional PRC2, NANOG, WNT Associates with PRC2, NANOG, WNT to repress differentia-

tion

Yu and Kuo (2016)

TUNA

(megamind)

Transcriptional NCL, PTBP1,

hnRNP-K

Associates with speciﬁed RBPs to

activate pluripotency genes and

neural differentiation genes

Lin et al. (2014)

yyT Transcriptional Brachyury Regulates Brachyury (T) in mesoderm speciﬁcation Frank et al. (2019)

AK048794 ceRNA miR-592 Sponges miR-592 to support pluripotency Zhou et al. (2016)

Cyrano ceRNA mir-7 Inhibit mir-7 to support self-renewal Smith et al. (2017)

H19 ceRNA let-7 microRNAs Modulates let-7 to impede muscle differentiation Kallen et al. (2013)

HPAT5 ceRNA let-7 microRNAs Modulates let-7 to promote pluripotency Durruthy-Durruthy et al.

(2016)

linc-ROR ceRNA miR-145 Inhibits miR-145 suppression of self-renewal genes Wang et al. (2013)

lncRNA-1064 ceRNA miR-200c Inhibits miR-200c to regulate neural differentiation Weng et al. (2018)

MD1 ceRNA miR-133, miR-135 Supports differentiation by inhibiting miR-133, miR-135 Cesana et al. (2011)

T-UCstem1 ceRNA miR-9, PRC2 Maintains self-renewal by modulating miR-9 and PRC2 Fiorenzano et al. (2018)

HOTAIR Scaffold PRC2, LSD1 Originally proposed to coordinate PRC2 and LSD1 complexes

for proper embryonic development, although contested in

the more recent literature

Tsai et al. (2010), Li et al.

(2013), Amândio et al.

(2016), Li et al. (2016),

Selleri et al. (2016),

Portoso et al. (2017)

XIST Scaffold X chromosome,

PRC2

Inactivates X chromosome for dosage control (Brown et al. 1991)

Cyrano Multimodal/Other Stat3, signaling net-

work

Supports ES cell maintenance Smith et al. (2018)

Tug1 Multimodal/Other Lin28A, Fragile X

mental retardation

protein

Regulates various differentiation processes including osteo-

genesis, neuronal differentiation and spermatogenesis

Young et al. (2005), Guo

et al. (2018), 1; He

et al. (2018), 1;

Lewandowski et al.

(2019)

Zeb2-NAT Other Zeb2 Facilitates Zeb2 processing to regulate EMT and pluripotency Beltran et al. (2008);

Bernardes de Jesus

et al. (2018)

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Speciﬁcally, the Tug1 locus represses downstream genes in a

cis-manner, while Tug1 RNA itself regulates genes that then

are dysregulated upon knockout. Additionally, the 59con-

served region of the Tug1 gene encodes a peptide, TUG1-

BOAT that inﬂuences mitochondrial membrane potential

(Lewandowski et al. 2019).

The lncRNA Cyrano (OIP5-AS1,1700020I14Rik,linc-oip5,

Oip5os1) is another illustration of multimodal function.

While lncRNAs generally display limited sequence conserva-

tion even between closely related species, a 300 nt region

shows very high conservation in tetrapods, with 100 nucle-

otides conserved between zebraﬁsh and humans (Ulitsky

et al. 2011). Uniquely, there is a sequence stretch that has

nearly perfectly complementarity to miR-7 in all tetrapods

examined, which regulates miR-7 degradation (Kleaveland

et al. 2018). The conserved sequence folds into a conserved

secondary structure within which embeds the mir-7 binding

sequence and partially masks it by base pairing (G. Varani

unpublished results). How the RNA structure affects protein

recruitment and degradation remains unclear, however, and

it might be that the structure itself is incidental to miR-7

degradation. Still, it provides tantalizing suggestions that

secondary and higher order structure might be essential com-

ponents of lncRNA regulatory activities.

Cyrano shows rare maternal and zygotic expression during

early development (Karlic et al. 2017), and in various mam-

malian cells it is a proliferation regulator (Smith et al. 2017;

Deng et al. 2018; X. Liu et al. 2018; Naemura et al. 2018).

Using proteomic analyses, Cyrano was found to interact with

a developmental/signaling protein network, through which

it partially supports mouse ES cell characteristics (Smith et al.

2018). Further, Cyrano inhibition of mir-7, which targets the

pluripotency regulator Nanog, as well as Itga9, contributes to

regulation of stemness and cellular adhesion (Smith et al.

2017).

Cyrano has also been shown to exist in a multi-component

RNA network with the circular RNA Cdr1as and several

miRNAs, where it maintains appropriate Cdr1as levels in

Figure 3 By engaging in diverse

interaction patterns, a single

lncRNA can impact multiple cellu-

lar processes. LncRNAs harness

different mechanisms and access

multiple networks of interacting

partners in a context-speciﬁc

manner. (A)TUG1 can (1) regulate

neighboring genes in cis, (2) func-

tion in trans to regulate target

genes, (3) be translated into a

micropeptide that regulates mito-

chondrial membrane potential,

and (4) interact with proteins such

as Lin28A to regulate various cell

fate decisions. (B) Cyrano’s multi-

faceted functions are illustrated

by (1) its ability to inhibit miR-7-

mediated repression of Itga9 and

Nanog, (2) interact with proteins

to support maintenance of the

self-renewing pluripotent state,

and (3) function in a multi-RNA

regulatory network to impact

Cdr1as expression, and, ultimately,

neuronal activity and neuropsy-

chiatric behavior in mice.

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the brain to control neuronal activity and neuropsychiatric

behavior in mice (Piwecka et al. 2017; Kleaveland et al.

2018). However, despite displaying strong expression and

signiﬁcant molecular and cellular phenotypes, Cyrano is an

example of a lncRNA that does not show an overt develop-

mental phenotype with differing knockout strategies (Han

et al. 2018; Kleaveland et al. 2018), which raises the question

of whether there could be compensatory mechanisms for its

function.

LncRNA Properties that Underlie Their Intermolecular

Interactions

Sequence

A challenge in the lncRNA ﬁeld is identifying the function of a

lncRNA based on analysis of its sequence content alone, which

is most easily accessible. This challenge stems from the fact

that most proteins interact with RNA through sequence motifs

that are degenerate and have hidden structural preferences

(Dominguez et al. 2018). Compounding the difﬁculty is that

the order of protein-binding modules within a lncRNA is

likely to be less important than the mere presence of the

binding modules themselves. Thus, two lncRNAs may encode

identical functions through different sequence solutions.

An additional obstacle lies in the fact that existing se-

quence alignment algorithms, which, in large part, have been

designed to detect linear sequence relationships between

evolutionarily related nucleic acid or protein species, often

fail to detect signiﬁcant homology between lncRNAs (Altschul

et al. 1990; Rice et al. 2000; Edgar and Batzoglou 2006;

Wheeler and Eddy 2013). In order to address this problem,

Kirk and colleagues recently developed a method called

SEEKR (Sequence evaluation through k-mer representation)

to quantify nonlinear sequence similarity between lncRNAs

(Kirk et al. 2018). Rather than evaluating similarity between

lncRNAs based on the extent of linear sequence homology,

SEEKR functions by counting the abundance of all possible

combinations of sequence substrings at a given length, k,

within a lncRNA, and then scaling these abundances by the

extent to which they differ from the mean abundance of each

k-mer in the group of lncRNAs being analyzed. The extent of

nonlinear similarity between lncRNAs as deﬁned by SEEKR

was found to correlate signiﬁcantly with lncRNA subcellular

localization and with protein binding, although the ability to

predict either of these two properties from k-mer content

alone was minimal. Using a transgenic assay to monitor the

ability of a lncRNA to induce Xist-like repression, it was

found that k-mer content, but not linear sequence homology,

strongly correlated with the ability of lncRNAs to induce this

type of repression. In a subsequent study, Sprague and col-

leagues found substantial levels of nonlinear sequence simi-

larity between functional domains in Xist, and domains in the

lncRNA Rsx, a marsupial lncRNA that has been proposed to be

a functional analog of Xist that arose through convergent evo-

lution (Grant et al. 2012; Sprague et al. 2019). Collectively,

these data support the notions that different lncRNAs can en-

code similar function through different spatial arrangements

of related, but not necessarily identical, sequence motifs, and

that k-mer based classiﬁcation provides an approach to detect

such similarities. The weak-to-modest predictive power of

k-mer content in most scenarios hints at ubiquitous and difﬁ-

cult-to-model roles for RNA structure in lncRNA function (Kirk

et al. 2018). Nevertheless, k-mer based classiﬁcation schemes,

which have been broadly used in other biological contexts

(Blaisdell 1989; Burge et al. 1992; Kari et al. 2015; Lees

et al. 2016; Pandey et al. 2018), represent promising avenues

that may ultimately aid in the functional classiﬁcation of

lncRNAs from sequence content alone, much in the way that

functional domains can now be routinely identiﬁed in proteins

(UniProt Consortium 2015).

Structure

LncRNAs function through sequence-speciﬁc interactions

with proteins that recognize stretches of sequence, as well

as with other RNAs or DNAs by base pairing through Watson–

Crick or Hoogsteen structures or by forming triple helices.

However, intermolecular recognition is often dependent on,

or regulated by, speciﬁc secondary and tertiary structural

features of the lncRNA. Intriguingly, when chemical modiﬁ-

cation techniques have been used to probe lncRNA structure,

it has generally revealed high levels of base pairing, more than

in mRNAs and comparable to the ribosome or self-splicing

introns—contexts in which secondary structure is complex

and essential (Somarowthu et al. 2015; Hawkes et al.

2016). Conversely, in cells, RBPs such as hnRNPs (Dreyfuss

et al. 1993) might keep lncRNAs less tightly folded. Further-

more, the observation of secondary structure does not neces-

sarily imply function, especially in the absence of clear

evolutionary conservation through covariation, as observed

for the ribosome. Similarities shared with the ribosome and

RNA enzymes could indicate a lncRNA architecture com-

posed of structured domains, possibly ﬂexibly connected,

that establish interactions with other RNAs, chromatin, or

speciﬁc protein complexes to bring them within functional

proximity. While this modular structure hypothesis is appeal-

ing because it would provide for intricate functional speciﬁc-

ities even in the absence of sequence conservation, it remains

to be investigated at the molecular level.

It also remains to be investigated the extent to which

secondary structures are functional, and whether they co-

alesce to form tertiary and higher order structures and inter-

actions. Thus far, relatively few lncRNAs have been

characterized at the secondary structure level, including

H19 (Hurst and Smith 1999; Juan 2000), Xist (Wutz et al.

2002; Fang et al. 2015; Lu et al. 2016; Smola et al. 2016;

F. Liu et al. 2017), Braveheart (Xue et al. 2016), HOTAIR

(Somarowthu et al. 2015), COOLAIR (Hawkes et al. 2016),

or lincRNA-p21 (Chillón and Pyle 2016) and several others.

A few studies indicate nevertheless that lncRNA structure is

important for regulation, and the principles learned through

these examples might be more broadly applicable to other

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lncRNAs. One study showed that regulation of transcription of

the E-cadherin gene is regulated by a sense lncRNA in epithe-

lial cells that is independently transcribed upstream of the

promoter (Pisignano et al. 2017). The structure of this RNA is

controlled by a SNP that modiﬁes local RNA secondary struc-

ture and affects loading of epigenetic enzymes that then reg-

ulate the downstream promoter. How common regulation

through conformational switching is remains unclear, but

RNA is structurally malleable and physically well-suited for

this mechanism of gene regulation. Riboswitches, for exam-

ple, are RNA structures that toggle between distinct confor-

mational states upon binding of small molecules and are

widespread in bacterial gene regulation (Mandal and

Breaker 2004). Riboswitch-like mechanisms of regulation

might be present in lncRNAs as well, but their prevalence

and function remain to be investigated. A study indicates

regulation by conformational switching is provided by the

roX lncRNA, which targets the MSL complex to the

Drosophila melanogaster X chromosome as part of the dosage

compensation process that occurs in male ﬂies. MSL is

recruited to roX lncRNA by a conserved stem-loop structure;

once bound by the MSL-component MLE, this stem-loop un-

folds to form an alternate RNA structure that appears to trap

MSL on roX (Ilik et al. 2013, 2017; Quinn et al. 2016). In the

case of Xist, structured regions, but also regions notable for

their absence of structure, likely serve to recruit different

subsets of proteins along the length of the lncRNA (Wutz

et al. 2002; Fang et al. 2015; Smola et al. 2016; F. Liu et al.

2017). Here, toggling between different conformational

states might deﬁne the subset of proteins that associate with

the lncRNA under distinct cellular conditions.

Resource Toolkit for LncRNA Interaction Proﬁling

RNAs are largely dependent on proteins for their produc-

tion, processing, transport, and localization. Based on reagent

availability and ease-of-study, approaches to investigate RNA–

protein interactions have historically been protein-centric.

As the diversity and functionality of lncRNAs emerged and

expanded, these tools, including RNA-immunoprecipitation

(RIP), CLIP and its variations, including HITS-CLIP, PAR-

CLIP, iCLIP, and Fast-iCLIP, have begun to reveal the magni-

tude of protein–lncRNA interactions (Ule et al. 2005; Hafner

et al. 2010; König et al. 2010; Flynn et al. 2015; J.-H. Li et al.

2015; Zarnegar et al. 2016). Similarly, efforts are ongoing to

deﬁne the complete repertoire of RNA-binding proteins using

proteomics-based methods such as OOPS, R-DeeP, XRNAX,

and DIF-FRAC, which often incorporate RNA dependency in

their analysis (Mallam et al. 2018; Caudron-Herger et al.

2019; Queiroz et al. 2019; Trendel et al. 2019), and reveal

surprisingly widespread RNA-dependent protein function-

ality, even for well characterized proteins such as CTCF

(Caudron-Herger et al. 2019).

The expanding RNA functionalities highlight the need for

RNA-centered methods to empirically determine binding

partners of lncRNAs (Table 2). New computational resources

that enable queries on previously identiﬁed interactors, and

those that allow for prediction of new interacting candidates

have therefore seen remarkable growth in just the last few

years.

Experimental methods

Capture hybridization analysis of RNA targets: Capture

hybridization analysis of RNA targets (CHART) methodology

allows for the identiﬁcation of chromatin and protein inter-

actors of lncRNAs. First, regions that are accessible for probe-

based isolation are mapped using RNase H-dependent

digestion. DNA oligonucleotides can then be used to isolate

the RNA in an afﬁnity puriﬁcation step, followed by high-

throughput sequencing to determine DNA segments con-

tacted by the lncRNA, or mass spectrometry, to determine

the protein interactome of the candidate lncRNA (Simon et al.

2011).

Chromatin isolation by RNA puriﬁcation: Similar to

CHART, chromatin isolation by RNA puriﬁcation (ChIRP)

or dChIRP (Chu et al. 2011; Quinn et al. 2014) uses a

probe-based afﬁnity approach built on biotinylated oligonu-

cleotides that tile the lncRNA. Isolated interactors that bind

the lncRNA (ChIRP), or bind to a speciﬁc domain (dChIRP),

can be analyzed by high-throughput sequencing, or by meth-

ods that detect proteins, such as mass spectrometry or West-

ern blotting.

RNA antisense puriﬁcation: The RNA antisense puriﬁcation

(RAP) method (McHugh et al. 2015), instead of using short

probes (20 nt) as in CHART or ChIRP, utilizes longer probes

of 60 nt to increase the stability of the interaction in afﬁnity

pulldowns.

RNA pulldown: The above-mentioned methods to investigate

lncRNA-partner molecules in intact cells were preceded by

probe-based isolation of RNAs in cell extracts in pulldown

experiments similar to protein coimmunoprecipitation.

Mapping RNA-genome interactions: Mapping RNA-genome

interactions (MARGI) uses proximity ligation to connect

chromatin-associated RNAs to their genomic targets, thus

revealing native RNA-chromatin interactions. Variations in

the approach were designed to differentiate between direct

interactions (diMARGI), mediated by protein or RNA-te-

thered interactions, of RNA–DNA chimeras, or passive inter-

actions (pxMARGI) (Sridhar et al. 2017).

Global RNA interaction with DNA by deep sequencing:

Global RNA interaction with DNA by deep sequencing (GRID-

Seq) harnesses in situ ligation to identify genome-wide con-

tacts between RNA and chromatin. The developers of this

method included mouse ES cells and found distinct cis- and

trans- chromatin interacting RNAs tied to cell-speciﬁc gene

expression patterns. GRID-Seq particularly enriches for chro-

matin interactions with nascent RNAs (Li et al. 2017).

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Chromatin associated RNA sequencing: Chromatin associ-

ated RNA sequencing (CHAR-Seq) is an in situ proximity

ligation approach coupled with enzymatic chromatin diges-

tion to detect RNA–DNA contacts genome-wide. After se-

quencing, CHAR-Seq maps the genomic interacting sites of

multiple classes of chromatin-associated RNAs including na-

scent transcripts, ncRNAs involved in regulation of dosage

compensation, and trans-interacting RNAs involved in RNA

processing (Bell et al. 2018).

RNA and DNA interacting complexes ligated and se-

quenced: RNA and DNA interacting complexes ligated and se-

quenced (RADICL-Seq) identiﬁes genome-wide RNA-chromatin

interactions in cross-linked nuclei. Thus far, it has been applied to

two cell types—mouse ES cells and mouse oligodendrocyte

progenitors—that can differentiate toward multiple cell fates.

This approach revealed cell-type speciﬁcRNA–chromatin inter-

actions, and was able to identify unique genome occupancy

patterns for different classes of transcripts (Bonetti et al. 2019).

Proﬁling interacting RNAs on chromatin: Proﬁling interact-

ing RNAs on chromatin (PIRCh-Seq) is an antibody-dependent

approach that proﬁles RNA–chromatin interactions, with less

enrichment of nascent RNAs cotranscriptionally tethered by

RNA polymerases to chromatin. PIRCh-Seq has been used to

identify the chromatin-associated transcriptome in both hu-

man and mouse ES cells and ﬁbroblasts, as well as mouse

neuronal progenitors, where the authors found cell- and al-

lele-speciﬁc RNA–chromatin interactions (Fang et al. 2019).

Microscopy: As previously discussed, lncRNA localization can

provide functional clues. The advent of single molecule im-

aging approaches facilitates the localization of RNA relative to

other interacting molecules. For example, labeled FISH probes

used in single molecule ﬂuorescence in situ microscopy

(smFISH) (Cabili et al. 2015; Dunagin et al. 2015), followed

by immunoﬂuorescence microscopy with three-dimensional

and quantitative ﬂuorescence image analysis allows for the

visualization of colocalized lncRNA and protein interactors

within subcellular domains (Lino Cardenas et al. 2018). Vari-

ations, including merFISH (Chen et al. 2015) and seqFISH

(Shah et al. 2016), depend on barcoding in sequential rounds

of hybridization to enable the detection of many transcripts

simultaneously. The resulting data complexity creates the need

for computational tools such as trendsceek (Edsgärd et al.

2018) to analyze these data. We can expect lncRNA monitor-

ing in spatial transcriptomics to increase as different function-

alities continue to emerge.

Bioinformatics

NPInter: Hao et al. (2016) is a repository of functional inter-

actions between noncoding RNAs and interacting partners

including small and large RNAs, DNA and proteins. At the

core of NPInter is a manual curation process based on pub-

lished literature, with a primary focus on experimentally

veriﬁed physical interactions, supplemented with in silico

predictions supported by high-throughput sequence data.

The latest version contains .900,000 interactions between

noncoding RNAs and other biomolecules from 22 organisms.

For RNAs, accession IDs from NONCODE, Ensembl, and

RefSeq are supported, and integration with the UCSC Ge-

nome Browser facilitates visualization of binding sites for

human, mouse, and yeast genomes.

POSTAR: Hu et al. (2017), Zhu et al. (2018) is a database that

enables exploration of post-transcriptional regulatory interac-

tions, based primarily on 1200 CLIP-Seq data sets. The aim of

POSTAR is to contribute a better understanding of how RBPs

impact post-transcriptional regulatory processes in six species.

Its integration with the UCSC Genome Browser facilitates

rapid visualization of RBP binding sites within transcripts.

RAID: The RAID database (Zhang et al. 2014; Yi et al. 2017),

formerly CLIPdb, incorporates experimentally derived and

Table 2 Methods to Investigate lncRNA function in cell fate determination

Approach Type Readout Output

Differential expression analysis Experimental Indirect Gene expression differences between cell types with

differentiation, or in a condition of interest

Expression correlation (+/2target or effector) Experimental Indirect Network generation to identify similarly expressed

gene clusters, including candidate target molecules

or possible upstream regulators

Afﬁnity puriﬁcation/proximity ligation and deep

sequencing (CHART/ChIRP/RAP/MARGI/GRID-Seq/

CHAR-Seq/Radicl-Seg/PIRCh-Seq)

Experimental Direct Chromatin targets

Afﬁnity puriﬁcation and mass spectrometry/Western

blot (CHART/ChIRP/RAP)

Experimental Direct Protein interactions

Single molecule ﬂuorescence in situ hybridization

(smFISH) +/2immunoﬂuorescence

Experimental Direct Subcellular localization and colocalization with targets

or effectors

Conservation analysis Experimental Direct Applicability of function across species

Structural analysis Experimental Direct Secondary structure and functional domain identiﬁ-

cation; 3D structure

Bioinformatics tools Computational Indirect Predict interactions, assess k-mer content, make

structural inferences

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computationally predicted RNA interactions from the pub-

lished literature, as well as other databases. RAID includes

data for 60 species and .1.2 million individual RNA–protein

and 4 million RNA–RNA interactions, respectively. A score

that is based on the evidence supporting the interaction in-

dicates the conﬁdence in each interaction.

RNA–protein interaction prediction: Using protein and RNA

sequence data, the family of RPISeq machine learning qual-

iﬁers (Muppirala et al. 2011) provides RNA–protein interac-

tion probabilities. Different versions of the tool provide the

probability of interaction between a speciﬁc RNA and pro-

tein, a speciﬁc RNA and up to 100 proteins, or a speciﬁc pro-

tein and up to 100 RNAs. Additionally, the sequence of a

speciﬁc protein can be used to query the RPIntDB, which

contains .30,000 individual RNA–protein interactions.

RNA–protein interaction predictor: RPI-Pred (Suresh et al.

2015) is a Support Vector Machine based prediction tool that

uses RNA and protein sequence information and protein

structural fragment data. Users can also test multiple candi-

dates including assessing the potential of a single RNA to

interact with multiple proteins or multiple RNAs interacting

with a single protein.

StarBase: This RNA-centric resource (Yang et al. 2011; Li

et al. 2014) details interactions between various classes of

long and short RNAs, as well as between RNAs and proteins

as extracted from CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP,

CLASH), degradome-seq, and RNA–RNA interactome data.

Users are also able to impute downstream effects of these

interactions based on accompanying gene expression data.

Combined approaches

Structure determination and analysis: The secondary struc-

ture of RNA can be predicted from thermodynamic principles,

but inaccuracy in the parameters means that experimental

input is required to generate a reliable model. This is most

often provided in the form of constraints on secondary struc-

ture generated from either evolutionary considerations (con-

servation of base pairs, ideally by covariation) and/or direct

experimental mapping of secondary structure using either

enzymatic, or, most often, chemical techniques such as SHAPE

and dimethyl sulfate (DMS) mapping (Kirk et al. 2018), psor-

alen crosslinking (Lu et al. 2016), and high throughput liga-

tion followed by deep-sequencing (Ramani et al. 2015).

Detection can be achieved efﬁciently through deep-sequencing,

although capillary electrophoresis, and even polyacrylamide

gels, can be used at much lower cost when studying single

lncRNAs. Because folding in vitro and in cells might differ

because of kinetic constraints on cotranscriptional RNA fold-

ing and the presence of RBPs (Leamy et al. 2016), techniques

are being developed to probe RNA secondary structures in

cellular contexts as well. Here, the primary limitation is

sensitivity and the requirement to have sufﬁcient RNA

for detection, which could require overexpression since most

lncRNAs are present at relatively low copy number. Higher

resolution methods such as SAXS (Small Angle X-ray Scatter-

ing) (Rambo and Tainer 2013) or X-ray crystallography and

NMR currently have very low throughput and can be used

only to investigate a few paradigmatic RNAs or systems of

particularly high biological interest.

Conclusion and Perspectives

This review summarizes multiple lines of evidence showing

that lncRNAs regulate cellular plasticity and cell fate deter-

mination, often through combination of multiple mecha-

nisms. By adding a further layer of complexity to gene

regulation, they broadly contribute to gene expression regu-

lation to either (i) maintain a blanket undifferentiated state,

(ii) promote exit toward a speciﬁed cell type, (iii) reprogram

cells to a pluripotent ground state, or (iv) contribute to cell

speciﬁcation control in organismal development. LncRNAs

perform these complex functions in integrated networks with

a diverse set of cellular players with which they interact

physically and/or functionally.

Progress toward the phenotypic assessment of lncRNA

depletion occurs through loss-of-function and gain-of-

function approaches (Liu and Lim 2018), facilitated by the

advent of CRISPR/Cas9 technologies. Indeed, high-throughput

screens using CRISPR interference identiﬁed .300 lncRNAs

that impacted iPS cell growth, with a smaller subset inﬂu-

encing pluripotency maintenance as determined by OCT4

expression (S. J. Liu et al. 2017). Functional ablation ap-

proaches include poly-A signal insertion proximal to the tran-

scription start site, although a drawback of this insertion is

residual background expression, as well as deletion of the

lncRNA locus, which results in total loss of lncRNA function,

but which may also affect unannotated regulatory elements.

Even the genetic manipulation of smaller sequences such

as promoters or single exons for well-annotated intergenic

lncRNAs should be carried out with caution to avoid modifying

regulatory genomic sequences. It should also be noted that DNA-

targeting approaches have resulted in differing phenotypes, as

exempliﬁed by Fendrr. Studies using a reporter gene replacement

strategy for Fendrr found abnormalities in lung development and

lethality at a later time point (Sauvageau et al. 2013; Lai et al.

2015), compared with the heart and body wall abnormali-

ties resulting in prenatal lethality in earlier investigations

(Grote et al. 2013). Regardless of the speciﬁctechnicalap-

proach for DNA sequence manipulation, it will be important

to study the impact of lncRNA expression ablation on the

function and regulation of the interacting proteins, RNAs,

and chromatin in development. Speciﬁc molecular targeting

of the lncRNA itself using CRISPR/Cas13 (Abudayyeh et al.

2017; Cox et al. 2017) could facilitate such investigations.

Intriguingly, at least several lncRNAs displaying differ-

ing levels of conservation, such as Malat1,Neat1,Cyrano,

Braveheart,Evx1as, and Visc-2, and found to have profound

molecular or cellular functions, had no overt developmental

phenotype in knockout animals (Han et al. 2018). This

1104 K. N. Smith et al.

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suggests either a primary role for lncRNAs in ﬁne-tuning

developmental functions, distinct roles in speciﬁc cellular

processes requiring situational study, or yet unearthed com-

pensatory functions, potentially by related/familial lncRNAs.

Another explanation for the absence of animal phenotypes

could be off-target effects of knockdown approaches using

RNA interference or antisense oligonucleotide-dependent

depletion (Matsui and Corey 2017). Increased use of gene

editing approaches such as CRISPR/Cas9 will help to clarify

lncRNA functionality in cell and animal models.

Improvements are needed to allow study of lncRNA inter-

actions at the single cell and single molecule level. While

technically feasible to a limited extent using imaging tech-

nologies, these methods remain specialized and low through-

put. These studies would allow the determination of cell fate

as single cell expression and epigenetic studies have indicated

substantial heterogeneity even within clonal cellular subpop-

ulations. Perhaps dynamic lncRNA interactions contribute to

this heterogeneity to dictate differing cell fates as well.

Related to this heterogeneity, it is still unclear whether a

classiﬁcation system that would allow prediction of lncRNA

interactions will be found, but, if such a system existed, it is

unlikely to be based on broad segments of sequence conser-

vation because these are generally absent in lncRNAs.

However, it might be possible to base classiﬁcation on the

identiﬁcation of shorter sequence stretches (k-mers) or struc-

tural features of the lncRNA that facilitate interactions with

other biomolecules. Identiﬁcation of the relevant structural

elements would provide insight into lncRNA interactions with

noncanonical RNA binding proteins as well, including those

without conserved and/or overt RNA binding domains.

There are untapped opportunities for progress in the

mechanistic analysis of lncRNA function for better under-

standing of speciﬁc developmental processes and some down-

stream applications, including personalized therapeutics. For

example, cancers typically progress through the acquisition of

stemness features (Malta et al. 2018) and undifferentiated

tumors have poor prognosis because they share immortality

and repopulation capacity characteristics with stem cells.

LncRNAs have emerged as central oncogenic and tumor sup-

pressive factors involved in misregulated cancer pathways

(Berger et al. 2018; Chiu et al. 2018), and many lncRNAs,

such as Cyrano, have been have been shown to support cel-

lular proliferation (Smith et al. 2017; Deng et al. 2018; X. Liu

et al. 2018; Naemura et al. 2018). Studies of lncRNAs in stem

cell contexts will not only enable better understanding of

mammalian development and differentiation, but may also

eventually facilitate better treatment of cancer and degener-

ative diseases.

Acknowledgments

Grant support: National Institutes of Health (NIH) R01

GM101974 to T.M., NIH R03 HD093977 to K.N.S, NIH

R01GM121806 to J.M.C., as well as R35 GM126942 and

RO1 GM 103834 to G.V.

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Radio-lncRNAs: Biological Function and Potential Use as Biomarkers for Personalized Oncology

Article

Full-text available

Sep 2022

Long non-coding RNAs (lncRNAs) consist of at least 200 nucleotides. Although these mol- ecules do not code proteins, they carry many regulatory functions in normal cells, as well as in can- cer cells. For instance, many of these molecules have been previously correlated with tumorigenesis of different cancers and their reaction to various stress factors, such as radiotherapy, chemotherapy, or reactive oxygen species (ROS). The lncRNAs are associated not only with dysregulation in can- cers after applied treatment but also with beneficial effects that may be achieved by modulating their expression, often significantly enhancing the patients’ outcomes. A multitude of these mole- cules was previously considered as potential biomarkers of tumor development, progression, or cells’ response to radio- or chemotherapy. Irradiation, which is often used in treating numerous cancer types, is not always sufficient due to cells gaining resistance in multiple ways. In this review, studies considering lncRNAs and their reaction to radiotherapy were examined. These molecules were divided regarding their role in specific processes strictly related to irradiation, and their influ- ence on this type of treatment was explained, showing how vast an impact they have on IR-sup- ported combat with the disease. This review aims to shed some light on potential future lncRNA- based biomarkers and therapeutic targets.

A long non-coding RNA at the cortex locus controls adaptive colouration in butterflies

Preprint

Full-text available

Feb 2024

Evolutionary variation in the wing pigmentation of butterflies and moths offers striking examples of adaptation by crypsis and mimicry. The cortex locus has been independently mapped as the locus controlling colour polymorphisms in 14 lepidopteran species, suggesting it acts as a genomic hotspot for the diversification of wing patterns, but functional validation through protein-coding knockouts has proven difficult to obtain. Our study unveils the role of a novel long non-coding RNA (lncRNA) which we name ivory , transcribed from the cortex locus, in modulating colour patterning in butterflies. Strikingly, ivory expression prefigures most melanic patterns during pupal development, suggesting an early developmental role in specifying scale identity. To test this, we generated CRISPR mosaic knock-outs in five nymphalid butterfly species and show that ivory mutagenesis yields transformations of dark pigmented scales into white or light-coloured scales. Genotyping of Vanessa cardui germline mutants associates these phenotypes to small on-target deletions at the conserved first exon of ivory . In contrast, cortex germline mutant butterflies with confirmed null alleles lack any wing phenotype, and exclude a colour patterning role for this adjacent gene. Overall, these results show that a lncRNA acts as a master switch of colour pattern specification, and played key roles in the adaptive diversification of colour patterns in butterflies. Significance statement Deciphering the genetic underpinnings of adaptive variation is fundamental for a comprehensive understanding of evolutionary processes. Long non-coding RNAs (lncRNAs) represent an emerging category of genetic modulators within the genome, yet they have been overlooked as a source of phenotypic diversity. In this study, we unveil the pivotal role of a lncRNA in orchestrating colour transitions between dark and light patterns during butterfly wing development. Remarkably, this lncRNA gene is nested within the cortex locus, a genetic region known to control multiple cases of adaptive variation in butterflies and moths, including iconic examples of natural selection. These findings highlight the significant influence of lncRNAs in developmental regulation, and also underscore their potential as key genetic players in the evolutionary process itself.

Host gene and its guest: short story about relation of long-noncoding MIR31HG transcript and microRNA miR-31

Article

Full-text available

Jan 2023

Epigenetics is the changes in cellular phenotype without changes in the genotype. This term is not limited only to the modification of chromatin and DNA but also regards some RNAs like non-coding RNAs (ncRNAs), both short and long RNAs (lncRNAs) acting as molecular modifiers. Mobile RNAs, as free form or encapsulated in exosomes, can regulate neighboring cells or be placed in distant locations. It underlines the vast capacity of ncRNAs as epigenetic elements of transmission information and message of life. One of the amazing phenomenons is long non-coding microRNA-host-genes (lnc-MIRHGs) which processed transcripts function as lncRNAs and also as short RNAs named microRNAs (miRNAs). MIR31HG functions as a modulator of important biological and cellular processes including cell proliferation, apoptosis, cell cycle regulation, EMT process, metastasis, angiogenesis, hypoxia, senescence, and inflammation. However, in most cases, the role of MIR31HG is documented only by one study and there is a lack of exact description of molecular pathways implicated in these processes, and for some of, such as response to irradiation, no studies were done. In this review, MIR31HG, as an example of lnc-MIRHGs, was described in the context of its’ known function and its potential uses as a biomarker in oncology.

LncRNA Osilr9 coordinates promoter DNA demethylation and the intrachromosomal loop structure required for maintaining stem cell pluripotency

Article

Full-text available

Dec 2022
MOL THER

Nuclear reprogramming of somatic cells into a pluripotent status has the potential to create patient-specific pluripotent stem cells (iPSCs) for regenerative medicine. Currently, however, the epigenetic mechanisms underlying this pluripotent reprogramming are poorly understood. To delineate this epigenetic regulatory network, we utilized a chromatin RNA in situ reverse transcription sequencing (CRIST-seq) approach to identify long noncoding RNAs (lncRNAs) embedded in the 3-dimensional intrachromosomal architecture of stem cell core factor genes. By combining CRIST-seq and RNA-seq, we identified Osilr9 (Oct4-Sox2 interacting lncRNA 9) as a pluripotency-associated lncRNA. Osilr9 expression was associated with the status of stem cell pluripotency in reprogramming. Using shRNA knockdown, we showed that this lncRNA was required for the optimal maintenance of stem cell pluripotency. Overexpression of Osilr9 induced robust activation of endogenous stem cell core factor genes in fibroblasts. Osilr9 participated in the formation of the intrachromosomal looping required for maintenance of pluripotency. After binding to the Oct4 promoter, Osilr9 recruited the DNA demethylase TET1, leading to promoter demethylation. These data demonstrate that Osilr9 is a critical chromatin epigenetic modulator that coordinates the promoter activity of core stem cell factor genes, highlighting the critical role of pluripotency-associated lncRNAs in stem cell pluripotency and reprogramming.

C10orf55, CASC2, and SFTA1P lncRNAs Are Potential Biomarkers to Assess Radiation Therapy Response in Head and Neck Cancers

Article

Full-text available

Oct 2022

Long non-coding RNAs have proven to be important molecules in carcinogenesis. Due to little knowledge about them, the molecular mechanisms of tumorigenesis are still being explored. The aim of this work was to study the effect of ionizing radiation on the expression of lncRNAs in head and neck squamous cell carcinoma (HNSCC) in patients responding and non-responding to radiotherapy. The experimental model was created using a group of patients with response (RG, n = 75) and no response (NRG, n = 75) to radiotherapy based on the cancer genome atlas (TCGA) data. Using the in silico model, statistically significant lncRNAs were defined and further validated on six HNSCC cell lines irradiated at three different doses. Based on the TCGA model, C10orf55, C3orf35, C5orf38, CASC2, MEG3, MYCNOS, SFTA1P, SNHG3, and TMEM105, with the altered expression between the RG and NRG were observed. Analysis of pathways and immune profile indicated that these lncRNAs were associated with changes in processes, such as epithelial-to-mesenchymal transition, regulation of spindle division, and the p53 pathway, and differences in immune cells score and lymphocyte infiltration signature score. However, only C10orf55, CASC2, and SFTA1P presented statistically altered expression after irradiation in the in vitro model. In conclusion, the expression of lncRNAs is affected by ionization radiation in HNSCC, and these lncRNAs are associated with pathways, which are important for radiation response and immune response. Potentially presented lncRNAs could be used as biomarkers for personalized radiotherapy in the future. However, these results need to be verified based on an in vitro experimental model to show a direct net of interactions.

A universal co‐expression gene network and prognostic model for hepatic‐biliary‐pancreatic cancers identified by integrative analyses

Article

Full-text available

Sep 2022

Hepatic, biliary and pancreatic cancers are a diverse set of malignancies with poor prognoses. It is possible that common molecular mechanisms are involved in the carcinogenesis of these cancers. Here, we identified LINC01537 and seven protein‐coding genes by integrative analysis of transcriptomes of mRNAs, miRNAs and lncRNAs from cholangiocarcinoma, hepatocellular carcinoma and pancreatic adenocarcinoma cohorts in TCGA. A predictive model constructed from seven biomarkers was established to successfully predict the survival rate of patients, which was then further verified in external cohorts. Additionally, patients with high‐risk scores in our model were prone to epithelial‐mesenchymal transition (EMT). Finally, activation of the biomarker PDE2A significantly attenuated migration and EMT in the HepG2 liver cancer cell line.

Long non‐coding RNA OIP5‐AS1 (Cyrano): A context‐specific regulator of normal and disease processes

Article

Full-text available

Jan 2022

Long non-coding (lnc) RNAs have been implicated in a plethora of normal biological functions, and have also emerged as key molecules in various disease processes. OIP5-AS1, also commonly known by the alias Cyrano, is a lncRNA that displays broad expression across multiple tissues, with significant enrichment in particular contexts including within the nervous system and skeletal muscle. Thus far, this multifaceted lncRNA has been found to have regulatory functions in normal cellular processes including cell proliferation and survival, as well as in the development and progression of a myriad disease states. These widespread effects on normal and disease states have been found to be mediated through context-specific intermolecular interactions with dozens of miRNAs and proteins identified to date. This review explores recent studies to highlight OIP5-AS1's contextual yet pleiotropic roles in normal homeostatic functions as well as disease oetiology and progression, which may influence its utility in the generation of future theranostics.

Triplex-forming peptide nucleic acids as emerging ligands to modulate structure and function of complex RNAs

Article

Jan 2024

Over the last three decades, our view of RNA has changed from a simple intermediate supporting protein synthesis to a major regulator of biological processes. In the expanding area of RNA research, peptide nucleic acid (PNA) is emerging as a promising ligand for triple-helical recognition of complex RNAs. As discussed in this feature article, the key advantages of PNAs are high sequence specificity and affinity for RNA (>10 fold higher than for DNA) that are difficult to achieve with small molecule ligands. Emerging studies demonstrate that triple-helical binding of PNAs can modulate biological function and control dynamic conformational equilibria of complex folded RNAs. These results suggest that PNA has a unique potential as a research tool and therapeutic compound targeting RNA. The remaining problems hampering advances in these directions are limitations of sequences that can be recognized by Hoogsteen triplexes (typically purine rich tracts), poor cellular uptake and bioavailability of PNA, and potential off-target effects in biological systems. Recent exciting studies are discussed that illustrate how synthetic nucleic acid chemistry provides innovative solutions for these problems.

Improved Triplex‐Forming Isoorotamide PNA Nucleobases for A−U Recognition of RNA Duplexes

Article

Full-text available

Oct 2023
CHEM-EUR J

Four new isoorotamide (Io)‐containing PNA nucleobases have been designed for A−U recognition of double helical RNA. New PNA monomers were prepared efficiently and incorporated into PNA nonamers for binding A−U in a PNA:RNA2 triplex. Isothermal titration calorimetry and UV thermal melting experiments revealed slightly improved binding affinity for singly modified PNA compared to known A‐binding nucleobases. Molecular dynamics simulations provided further insights into binding of Io bases in the triple helix. Together, the data revealed interesting insights into binding modes including the notion that three Hoogsteen hydrogen bonds are unnecessary for strong selective binding of an extended nucleobase. Cationic monomer Io8 additionally gave the highest affinity observed for an A‐binding nucleobase to date. These results will help inform future nucleobase design toward the goal of recognizing any sequence of double helical RNA.

Recent Development in Biomedical Applications of Oligonucleotides with Triplex-Forming Ability

Article

Full-text available

Feb 2023

A DNA structure, known as triple-stranded DNA, is made up of three oligonucleotide chains that wind around one another to form a triple helix (TFO). Hoogsteen base pairing describes how triple-stranded DNA may be built at certain conditions by the attachment of the third strand to an RNA, PNA, or DNA, which might all be employed as oligonucleotide chains. In each of these situations, the oligonucleotides can be employed as an anchor, in conjunction with a specific bioactive chemical, or as a messenger that enables switching between transcription and replication through the triplex-forming zone. These data are also considered since various illnesses have been linked to the expansion of triplex-prone sequences. In light of metabolic acidosis and associated symptoms, some consideration is given to the impact of several low-molecular-weight compounds, including pH on triplex production in vivo. The review is focused on the development of biomedical oligonucleotides with triplexes.

RADICL-seq identifies general and cell type–specific principles of genome-wide RNA-chromatin interactions

Article

Full-text available

Feb 2020

Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodeling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed; however, these methods have some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA–chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared with existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type–specific RNA-chromatin interactions, and highlights the role of transcription in the establishment of chromatin structure.

PIRCh-seq: functional classification of non-coding RNAs associated with distinct histone modifications

Article

Full-text available

Dec 2019
GENOME BIOL

Abstract We develop PIRCh-seq, a method which enables a comprehensive survey of chromatin-associated RNAs in a histone modification-specific manner. We identify hundreds of chromatin-associated RNAs in several cell types with substantially less contamination by nascent transcripts. Non-coding RNAs are found enriched on chromatin and are classified into functional groups based on the patterns of their association with specific histone modifications. We find single-stranded RNA bases are more chromatin-associated, and we discover hundreds of allele-specific RNA-chromatin interactions. These results provide a unique resource to globally study the functions of chromatin-associated lncRNAs and elucidate the basic mechanisms of chromatin-RNA interactions.

Regulation of CHD2 expression by the Chaserr long noncoding RNA gene is essential for viability

Article

Full-text available

Nov 2019

Chromodomain helicase DNA binding protein 2 (Chd2) is a chromatin remodeller implicated in neurological disease. Here we show that Chaserr, a highly conserved long noncoding RNA transcribed from a region near the transcription start site of Chd2 and on the same strand, acts in concert with the CHD2 protein to maintain proper Chd2 expression levels. Loss of Chaserr in mice leads to early postnatal lethality in homozygous mice, and severe growth retardation in heterozygotes. Mechanistically, loss of Chaserr leads to substantially increased Chd2 mRNA and protein levels, which in turn lead to transcriptional interference by inhibiting promoters found downstream of highly expressed genes. We further show that Chaserr production represses Chd2 expression solely in cis, and that the phenotypic consequences of Chaserr loss are rescued when Chd2 is perturbed as well. Targeting Chaserr is thus a potential strategy for increasing CHD2 levels in haploinsufficient individuals. The conserved long noncoding RNA Chaserr is transcribed upstream of the chromatin remodeler Chd2. Here, using mouse genetics and high throughput assays, the authors show that Chaserr inhibits expression of Chd2 in cis and is required for postnatal mouse development.

Systematic Discovery of Endogenous Human Ribonucleoprotein Complexes

Article

Full-text available

Oct 2019

RNA-binding proteins (RBPs) play essential roles in biology and are frequently associated with human disease. Although recent studies have systematically identified individual RNA-binding proteins, their higher-order assembly into ribonucleoprotein (RNP) complexes has not been systematically investigated. Here, we describe a proteomics method for systematic identification of RNP complexes in human cells. We identify 1,428 protein complexes that associate with RNA, indicating that more than 20% of known human protein complexes contain RNA. To explore the role of RNA in the assembly of each complex, we identify complexes that dissociate, change composition, or form stable protein-only complexes in the absence of RNA. We use our method to systematically identify cell-type-specific RNA-associated proteins in mouse embryonic stem cells and finally, distribute our resource, rna.MAP, in an easy-to-use online interface (rna.proteincomplexes.org). Our system thus provides a methodology for explorations across human tissues, disease states, and throughout all domains of life.

Systematic allelic analysis defines the interplay of key pathways in X chromosome inactivation

Article

Full-text available

Jul 2019

Xist RNA, the master regulator of X chromosome inactivation, acts in cis to induce chromosome-wide silencing. Whilst recent studies have defined candidate silencing factors, their relative contribution to repressing different genes, and their relationship with one another is poorly understood. Here we describe a systematic analysis of Xist-mediated allelic silencing in mouse embryonic stem cell-based models. Using a machine learning approach we identify distance to the Xist locus and prior gene expression levels as key determinants of silencing efficiency. We go on to show that Spen, recruited through the Xist A-repeat, plays a central role, being critical for silencing of all except a subset of weakly expressed genes. Polycomb, recruited through the Xist B/C-repeat, also plays a key role, favouring silencing of genes with pre-existing H3K27me3 chromatin. LBR and the Rbm15/m6A-methyltransferase complex make only a minor contribution to gene silencing. Together our results provide a comprehensive model for Xist-mediated chromosome silencing.

RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions

Preprint

Full-text available

Jun 2019

Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodelling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed. However, they still present some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA-chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared to existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type-specific RNA-chromatin interactions, and emphasizes the role of transcription in the establishment of chromatin structure.

Non-linear sequence similarity between the Xist and Rsx long noncoding RNAs suggests shared functions of tandem repeat domains

Article

Full-text available

May 2019

The marsupial inactive X chromosome expresses a long noncoding RNA (lncRNA) called Rsx that has been proposed to be the functional analog of eutherian Xist Despite the possibility that Xist and Rsx encode related functions, the two lncRNAs harbor no linear sequence similarity. However, both lncRNAs harbor domains of tandemly repeated sequence. In Xist, these repeat domains are known to be critical for function. Using k-mer based comparison, we show that the repeat domains of Xist and Rsx unexpectedly partition into two major clusters that each harbor substantial levels of nonlinear sequence similarity. Xist Repeats B, C, and D were most similar to each other and to Rsx Repeat 1, whereas Xist Repeats A and E were most similar to each other and to Rsx Repeats 2, 3, and 4. Similarities at the level of k-mers corresponded to domain-specific enrichment of protein-binding motifs. Within individual domains, protein-binding motifs were often enriched to extreme levels. Our data support the hypothesis that Xist and Rsx encode similar functions through different spatial arrangements of functionally analogous protein-binding domains. We propose that the two clusters of repeat domains in Xist and Rsx function in part to cooperatively recruit PRC1 and PRC2 to chromatin. The physical manner in which these domains engage with protein cofactors may be just as critical to the function of the domains as the protein cofactors themselves. The general approaches we outline in this report should prove useful in the study of any set of RNAs.

lncRNA-Induced Spread of Polycomb Controlled by Genome Architecture, RNA Abundance, and CpG Island DNA

Article

Jun 2019
MOL CELL

Long noncoding RNAs (lncRNAs) cause Polycomb repressive complexes (PRCs) to spread over broad regions of the mammalian genome. We report that in mouse trophoblast stem cells, the Airn and Kcnq1ot1 lncRNAs induce PRC-dependent chromatin modifications over multi-megabase domains. Throughout the Airn-targeted domain, the extent of PRC-dependent modification correlated with intra-nuclear distance to the Airn locus, preexisting genome architecture, and the abundance of Airn itself. Specific CpG islands (CGIs) displayed characteristics indicating that they nucleate the spread of PRCs upon exposure to Airn. Chromatin environments surrounding Xist, Airn, and Kcnq1ot1 suggest common mechanisms of PRC engagement and spreading. Our data indicate that lncRNA potency can be tightly linked to lncRNA abundance and that within lncRNA-targeted domains, PRCs are recruited to CGIs via lncRNA-independent mechanisms. We propose that CGIs that autonomously recruit PRCs interact with lncRNAs and their associated proteins through three-dimensional space to nucleate the spread of PRCs in lncRNA-targeted domains.

Functional classification of noncoding RNAs associated with distinct histone modifications by PIRCh-seq

Preprint

Jun 2019

Many long noncoding RNAs (lncRNAs) regulate gene transcription through binding to histone modification complexes. Therefore, a comprehensive study of nuclear RNAs in a histone modification-specific manner is critical to understand their regulatory mechanisms. Here we develop a method named Profiling Interacting RNAs on Chromatin by deep sequencing (PIRCh-seq), in which we profile chromatin-associated transcriptome in 5 different cell types using antibodies recognizing histone H3 and 6 distinct histone modifications associated with active or repressive chromatin states. PIRCh-seq identified chromatin-associated RNAs with substantially less contamination by nascent transcripts, as compared to existing methods. We classified chromatin-enriched lncRNAs into 6 functional groups based on the patterns of their association with specific histone modifications. LncRNAs were enriched with different chromatin modifications in different cell types, suggesting lncRNAs' regulation may also be cell type-specific. By integrating profiles of RNA secondary structure and RNA m6A modification, we found that RNA bases which bind to chromatin tend to be more single stranded. We discovered hundreds of allele-specific RNA-chromatin interactions, nominating specific single nucleotide variants that alter RNA association with chromatin. These results provide a unique resource to globally study the functions of chromatin-associated lncRNAs and elucidate the basic mechanisms of chromatin-RNA interaction.

R-DeeP: Proteome-wide and Quantitative Identification of RNA-Dependent Proteins by Density Gradient Ultracentrifugation

Article

May 2019
MOL CELL

The comprehensive but specific identification of RNA-binding proteins as well as the discovery of RNA-associated protein functions remain major challenges in RNA biology. Here we adapt the concept of RNA dependence, defining a protein as RNA dependent when its interactome depends on RNA. We converted this concept into a proteome-wide, unbiased, and enrichment-free screen called R-DeeP (RNA-dependent proteins), based on density gradient ultracentrifugation. Quantitative mass spectrometry identified 1,784 RNA-dependent proteins, including 537 lacking known links to RNA. Exploiting the quantitative nature of R-DeeP, proteins were classified as not, partially, or completely RNA dependent. R-DeeP identified the transcription factor CTCF as completely RNA dependent, and we uncovered that RNA is required for the CTCF-chromatin association. Additionally, R-DeeP allows reconstruction of protein complexes based on co-segregation. The whole dataset is available at http://R-DeeP.dkfz.de, providing proteome-wide, specific, and quantitative identification of proteins with RNA-dependent interactions and aiming at future functional discovery of RNA-protein complexes.

Multimodal Long Noncoding RNA Interaction Networks: Control Panels for Cell Fate Specification

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Table S2. miRWalk Summary of miRNA Binding Sites in Itga9 and Nanog