Spatial correlation statistics enable transcriptome-wide characterization of RNA structure binding

Busa, Veronica F.; Favorov, Alexander V.; Fertig, Elana J.; Leung, Anthony Kwan

doi:10.1016/j.crmeth.2021.100088

Cited by 5 publications

(4 citation statements)

References 80 publications

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“…Interestingly, the observation applies to the RBP FXR2, which exhibits less co-location than expected in HepG2 but more co-location than expected in K562, despite being an rG4 Binding Protein (RG4BP). 73 Surprisingly, HNRNPL consistently exhibits low co-location with prG4s despite also being an RG4BP. This confirms that while some RBPs are known to bind rG4s, rG4s are not their only or primary target.…”

Section: Resultsmentioning

confidence: 95%

Toward a Better Understanding of G4 Evolution in the 3 Living Kingdoms

Vannutelli,

Ouangraoua,

Perreault

2023

Evol Bioinform Online

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Background: G-quadruplexes (G4s) are secondary structures in DNA and RNA that impact various cellular processes, such as transcription, splicing, and translation. Due to their numerous functions, G4s are involved in many diseases, making their study important. Yet, G4s evolution remains largely unknown, due to their low sequence similarity and the poor quality of their sequence alignments across several species. To address this, we designed a strategy that avoids direct G4s alignment to study G4s evolution in the 3 species kingdoms. We also explored the coevolution between RBPs and G4s. Methods: We retrieved one-to-one orthologous genes from the Ensembl Compara database and computed groups of one-to-one orthologous genes. For each group, we aligned gene sequences and identified G4 families as groups of overlapping G4s in the alignment. We analyzed these G4 families using Count, a tool to infer feature evolution into a gene or a species tree. Additionally, we utilized these G4 families to predict G4s by homology. To establish a control dataset, we performed mono-, di- and tri-nucleotide shuffling. Results: Only a few conserved G4s occur among all living kingdoms. In eukaryotes, G4s exhibit slight conservation among vertebrates, and few are conserved between plants. In archaea and bacteria, at most, only 2 G4s are common. The G4 homology-based prediction increases the number of conserved G4s in common ancestors. The coevolution between RNA-binding proteins and G4s was investigated and revealed a modest impact of RNA-binding proteins evolution on G4 evolution. However, the details of this relationship remain unclear. Conclusion: Even if G4 evolution still eludes us, the present study provides key information to compute groups of homologous G4 and to reveal the evolution history of G4 families.

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Section: Resultsmentioning

confidence: 95%

Toward a Better Understanding of G4 Evolution in the 3 Living Kingdoms

Vannutelli,

Ouangraoua,

Perreault

2023

Evol Bioinform Online

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“…C. Spatial correlation of PARP13-and TRIM25-bound regions across the HEK293T transcriptome calculated by nearBynding (Busa et al 2021). Black line indicates mean shuffled background signal with standard error (n = 10,000) and blue line represents mean spatial correlation signal with standard error (n=3).…”

Section: Resultsmentioning

confidence: 99%

“…Although TRIM25 shares many targets with PARP13, it was unclear whether the proteins bind in proximity to each other. Using the transcriptome-wide cross-correlation tool nearBynding (Busa et al 2021), we spatially compared the binding peaks of PARP13 and TRIM25 to determine the location of PARP13 binding relative to TRIM25. TRIM25-bound regions positively correlated with PARP13-bound regions identified for both ssRNA and 3p-RNA treatments at relative position 0, which shows that when PARP13 and TRIM25 bind the same transcript, they bind at approximately the same location (Fig.…”

Section: Parp13 Supports Expression Of Transcripts Related To the Isg...mentioning

confidence: 99%

Transcriptome Regulation by PARP13 in Basal and Antiviral States in Human Cells

Busa

Ando

Aigner

et al. 2022

Preprint

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The RNA-binding protein PARP13 is a primary factor in the innate antiviral response. PARP13 suppresses translation and drives decay of bound viral and host RNA. PARP13 interacts with many proteins encoded by interferon-stimulated genes (ISG) to activate antiviral pathways including post-translational addition of ISG15, or ISGylation. We performed enhanced crosslinking immunoprecipitation (eCLIP) and RNA-seq in human cells to investigate PARP13's role in transcriptome regulation for both basal and antiviral states. We find that the antiviral response shifts PARP13 target localization but not its binding preferences and that PARP13 supports the expression of ISGylation-related genes, including PARP13's cofactor, TRIM25. We elucidate a transcriptome-wide periodicity of PARP13 binding around TRIM25 and show they associate in part via RNA-protein interactions. Taken together, our study implicates PARP13 in creating and maintaining a cellular environment poised for an antiviral response through limiting PARP13 translation, regulating access to distinct mRNA pools, and elevating ISGylation machinery expression.

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“…The null models for colocalization analysis can be constructed using either analytical tests or permutation tests (Monte Carlo simulations) [2] . Analytical tests make inferences about population parameters based on assumptions about the underlying data distribution, such as GREAT (binomial test) [11] , LOLA (Fisher’s exact test) [12] , GenomeRunner (chi-square test and binomial test) [13] and nearBynding (Welch’s t-test) [14] . On the other hand, permutation tests, i.e., methods incorporate Monte Carlo simulations, create a null distribution of the test statistic by repeatedly sampling data, such as BEDTools [15] , HyperBrowser [16] , MULTOVL [17] , IntervalStats [18] , GenometriCorr [19] , GAT [20] , BITS [21] , ChIPseeker [22] , regioneR [23] , StereoGene [24] , OLOGRAM [25] and Bedshift [26] .…”

Section: Introductionmentioning

confidence: 99%