Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5644_Библиотеки_им_академика_М_И_Перельмана

Methods based on psoralen cross-linking suer three main limitations [60]: (i) the preference of psoralen for pyrimidines, particularly uracils, can result in the prefer­ential stabilization and capture of pyrimidine-rich duplexes, hence making these methods not quantitative; (ii) although intramolecular ligation is favored between RNA strands in a duplex, any two RNA strands in a sample can be intermolecularly ligated, resulting in a number of artifactual RNA–RNA interactions being mapped; and (iii) the vast majority of the adapter-ligated RNA fragments are non-chimeric, hence signicantly increasing the required sequencing depth of these experiments. Potential workarounds have been proposed for some of these issues. For instance, SPLASH [54] and COMRADES [57], respectively, take advantage of biotin- or azido-modied psoralen derivatives to enable the streptavidin-mediated enrich­ment of RNA duplexes, while PARIS [53] exploits a native-denatured 2-dimensional gel electrophoresis approach to enrich the cross-linked RNA duplexes. Additionally, in order to account for nonspecic ligation events, the COMRADES approach generates a control sample by reversing psoralen cross-links and melting RNA duplexes prior to ligation.

Concerning the identication of RNA–RNA interactions via chemical probing and MaP [35, 48], the main limitation is that equilibrium uctuations are very rare, so are correlated modication events, hence requiring extreme sequencing depths to be detected, making this type of approach only suited for the targeted analysis of individual RNAs.

Although RT drop-o-based probing experiments do not preserve any informa­tion about the relationships between individual nucleotides in the RNA strand, as each cDNA molecule can only capture a single modication site, several computa­tional methods have been devised in an attempt to mitigate this limitation [71–73]. These algorithms take advantage of a partition function-based approach to sam­ple a large number of possible structures from the theoretical ensemble a given RNA can populate and then use the experimentally determined reactivity prole to select a parsimonious set of structures that better explains the observed experimental

reactivities. All these methods have been thoroughly reviewed elsewhere [74], so they will not be discussed in detail in this chapter.

For example, the antibiotic spectinomycin has been previously shown to specif­ically interact with residues G1064 and C1192 in the E. coli 16S rRNA [80]. Accordingly, DMS chemical probing readout by MaP of the 16S rRNA from living bacteria treated with spectinomycin shows strong protection of C1192 from DMS due to hydrogen bonding between C1192 and spectinomycin [81]. More recently, SHAPE probing has been proposed as a rapid way to conduct high-throughput fragment-based screening of small-molecule RNA ligands [82]. In this method, a synthetic RNA molecule is designed to contain both the target RNA structure and a control structure. Nonspecic binders can be readily identied as they will result in SHAPE reactivity changes for both the target and control structures, while specic binders will only show signicant reactivity changes on the target structure. An alternative SHAPE-based approach has also been proposed to map the interaction of small molecules with RNA on a transcriptome-wide scale [83]. In this approach, the small molecule of interest is functionalized with an acylimidazole-substituted linker to enable adduct formation with the ribose 2′-OH at structurally exible nucleotides, further allowing the detection of small molecule–RNA interactions via RT drop-o or MaP-based readouts.

in the cellular context is denitely the biggest. Although several proof-of-concept studies demonstrated the targeted deconvolution of the RNA structure ensembles of individual transcripts, to date no one has attempted exploring RNA structure heterogeneity on a transcriptome-wide scale. Achieving such resolution of the RNA structurome is essential in the perspective of using RNA as a drug target. Pathologically relevant RNAs might populate multiple conformations, and only one of these conformations might actually be responsible for the pathological phenotype, hence making the deconvolution of the RNA structure ensemble a crucial step in target identication. Meeting this challenge requires the cooperative development of improved experimental and computational strategies. For instance, ensemble deconvolution from MaP-based chemical probing experiments has so far been attempted mostly in conjunction with DMS probing, owing to the typically higher signal-to-noise ratio of DMS as compared to other probes, such as SHAPE reagents. This, however, poses substantial limits to our ability to eciently identify structurally heterogeneous regions as DMS can only probe roughly half of the bases in RNA molecules. The increased signal-to-noise ratio of recently developed SHAPE probes [42], possibly combined with the development of engineered RTs with improved read-through apabilities [84], might help advance with respect to the current state of the art. Furthermore, although powerful, ensemble decon­volution from MaP-based chemical probing experiments likely only provides a coarse-grained representation of the actual ensemble, as small local structural dierences might not be eciently captured. In this regard, the ability to combine the information from orthogonal probing reagents (e.g. DMS and SHAPE), or from orthogonal experimental approaches (e.g. chemical probing and RNA duplex mapping), might help improve the resolution over each individual method, possibly providing more holistic solutions to map cellular RNA structure ensembles.