Chemical crosslinking enhances RNA immunoprecipitation for efficient identification of binding sites of proteins that photo-crosslink poorly with RNA

Abstract

In eukaryotic cells, proteins that associate with RNA regulate its activity to control cellular function. To fully illuminate the basis of RNA function, it is essential to identify such RNA-associated proteins, their mode of action on RNA, and their preferred RNA targets and binding sites. By analyzing catalogs of human RNA-associated proteins defined by ultraviolet light (UV)-dependent and -independent approaches, we classify these proteins into two major groups: (i) the widely recognized RNA binding proteins (RBPs), which bind RNA directly and UV-crosslink efficiently to RNA, and (ii) a new group of RBP-associated factors (RAFs), which bind RNA indirectly via RBPs and UV-crosslink poorly to RNA. As the UV crosslinking and immunoprecipitation followed by sequencing (CLIP-seq) approach will be unsuitable to identify binding sites of RAFs, we show that formaldehyde crosslinking stabilizes RAFs within ribonucleoproteins to allow for their immunoprecipitation under stringent conditions. Using an RBP (CASC3) and an RAF (RNPS1) within the exon junction complex (EJC) as examples, we show that formaldehyde crosslinking combined with RNA immunoprecipitation in tandem followed by sequencing (xRIPiT-seq) far exceeds CLIP-seq to identify binding sites of RNPS1. xRIPiT-seq reveals that RNPS1 occupancy is increased on exons immediately upstream of strong recursively spliced exons, which depend on the EJC for their inclusion.

Keywords: CLIP-seq; RNA binding proteins; UV crosslinking; exon junction complex; formaldehyde crosslinking; pre-mRNA splicing.

FIGURE 1.

RNA binding proteins that UV-crosslink poorly to RNA are widespread in RNA metabolism. (A) Venn diagram showing the overlap between RBPs defined based on UV crosslinking-dependent RNA interactome capture (RIC, from Hentze et al. 2018) and those defined by protein:protein interaction network of annotated RBPs (annotated and predicted RNA-associated proteins, from Brannan et al. 2016). (B) Venn diagram as in A showing overlap between integrated human RBPome defined based on UV-crosslinkability (ihRBPome, from Trendel et al. 2019) and annotated and predicted RNA-associated proteins (from Brannan et al. 2016) based on protein:protein interaction network of annotated RBPs. (C) Top sixteen gene ontology terms (biological process) enriched among RAFs. Legend on the right indicates the number of genes in each term. Some redundant GO terms were removed. (D) GO terms (cellular compartment) enriched among RAFs as in C. (E) GO terms (molecular function) enriched among RAFs as in C. (F) Major processes in the life-cycle of eukaryotic messenger RNAs (left) along with some examples of RAFs involved in each of the steps. Also shown are some examples of RAFs that are components of key membraneless RNP compartments or large RNP complexes (right).

FIGURE 2.

Formaldehyde crosslinking stabilizes an RAF-EJC interaction. (A) Western blots showing proteins on the right in the total extract (TE) or FLAG immunoprecipitation (IP) fractions of HEK293 Flp-In cells expressing FLAG-MAGOH. The presence or absence of RNase A and the amount of NaCl present in the extracts during the IP are indicated above each lane. The dotted line indicates where gel images were spliced together. Data are representative of two biological replicates. (B) Western blots showing proteins (labeled on the right) in the insoluble pellet, soluble extract, and anti-FLAG immunoprecipitate fractions (indicated on the top) of HEK293 Flp-In cells. The lysis and IP conditions are also indicated on the top. Also indicated above each lane is the expression of FLAG-RNPS1 (+) or FLAG epitope only (−) in the cells, and the formaldehyde concentration used for in vivo crosslinking. Data are representative of three biological replicates.

FIGURE 3.

Formaldehyde crosslinking enhances RIPiT-seq signal for CASC3. (A) A meta-exon plot showing nRIPiT and xRIPiT read densities in the 150 nt window from the end of exons of protein-coding genes (excluding final exons). (B) Comparison of gene-level CASC3 read density (RPKM_RIPiT-seq) in native RIPiT (nRIPiT, squares) and formaldehyde-crosslinked RIPiT (xRIPiT; circles) for canonical (darker-shaded shapes) and noncanonical regions (lighter-shaded shapes), and for intronless genes (empty shapes). Along the x-axis, genes are binned into twenty bins where each bin contains exons from genes within a twofold expression level range based on RNA-seq. Error bars represent the standard error of the mean signal in each bin. (C) A comparison of linear fit coefficients (or intercepts, in log space) of the six classes in B. Classes are labeled on the bottom. The coefficient for the average of the two intronless classes was set to 1 and all intercepts were adjusted accordingly. The fold-change as compared to the average of the two intronless classes is shown above each bar. (D) Percentage of all canonical EJC regions where read count is greater than or equal to twofold as compared to read counts on intronless genes of similar expression level.

FIGURE 4.

xRIPiT-seq and CLIP-seq are robust and comparable approaches to identify CASC3 binding sites. (A) A meta-exon plot showing xRIPiT and CLIP read counts in the 150 nt window at exon ends. Read normalization was carried out as in Figure 3A. (B) Comparison of gene-level CASC3 read density (RPKM_IP-seq) in xRIPiT (diamonds) and CLIP (triangles) for canonical (darker-shaded shapes) and noncanonical regions (lighter-shaded shapes), and for intronless genes (empty shapes). Gene binning and error bars are as in Figure 3B. (C) Comparison of the linear fit coefficients (or intercepts, in log space) of the six classes in B. Classes are labeled on the bottom. (D) Percentage of all canonical EJC regions where read depth is greater than or equal to twofold as compared to intronless gene read counts in the indicated data sets. (E) Venn diagram showing counts of canonical regions from the top 20% expressed genes where CASC3 footprint read depth in nRIPiT, xRIPiT and CLIP is greater than or equal to twofold as compared to intronless gene read counts.

FIGURE 5.

xRIPiT-seq outperforms nRIPiT-seq and CLIP-seq to detect RNPS1 binding sites. (A) A meta-exon plot showing RNPS1 nRIPiT and xRIPiT footprint read counts at each position in the 150 nt window from exon ends. (B) Comparison of gene-level RNPS1 read density (RPKM) in nRIPiT (squares) and xRIPiT (circles) for canonical (darker-shaded shapes) and noncanonical regions (lighter-shaded shapes), and for intronless genes (empty shapes). Gene bins along the x-axis and error bars are as in Figure 3B. (C) Comparison of linear fit coefficients (or intercepts, in log space) of the six classes in B, which are labeled on the bottom. (D) Percentage of all canonical EJC regions where read depth is greater than or equal to twofold as compared to intronless read counts in the indicated data sets. (E) A meta-exon plot showing RNPS1 xRIPiT and CLIP footprint read counts at each position in the 150 nt window from exon ends. (F) Comparison of normalized read density (RPKM) in xRIPiT (diamonds) and CLIP (triangles) for canonical (darker-shaded shapes) and noncanonical regions (lighter-shaded shapes), and for intronless genes (empty shapes). The bins of genes along the x-axis and the error bars are as in Figure 3B. (G) Comparison of linear fit coefficients (or intercepts, in log space) of the six data sets in F. (H) Percentage of all canonical EJC regions where read depth is greater than or equal to twofold as compared to intronless gene read counts. (I) Venn diagram showing counts of canonical regions from the top 20% expressed genes where RNPS1 footprint read depth in nRIPiT, xRIPiT, and CLIP is greater than or equal to twofold as compared to intronless gene read counts.

FIGURE 6.

Comparison of xRIPiT-seq and CLIP-seq signal for RNPS1 and CASC3 occupancy on high- and low-scoring RS exons and their neighboring exons. (A) A schematic of recursively spliced (RS) exon and its neighboring exons. Empty rectangles: constitutive exons; shaded rectangle: RS exon; black line: intron; dotted lines: possible exon splicing patterns; shaded ovals: RNPS1-EJC; RNPS1-EJCs upstream of RS junction suppresses RS, whereas the complex on one exon further upstream (shown on shaded background) stabilizes the downstream complex. The number below each exon represents the number of exons for which data is presented in panels B and C. (B) Box plots showing CASC3 xRIPiT-seq and CLIP-seq read densities on high-scoring versus low-scoring RS exon and its three preceding exons. Each set of four boxplots is arranged directly below the RS exon or the one of its three preceding exons it corresponds to in A. (Top) Wilcoxon rank-sum test P-values. (C) Box plots as in B showing RNPS1 xRIPiT-seq and CLIP-seq exonic read densities. (D) Integrated genome viewer tracks showing read coverage (normalized for library size) on TMA16’s RS exon and its three preceding exons. (E) Box plots showing RNPS1 and CASC3 xRIPiT-seq and CLIP-seq genic read densities on genes that contain a high-scoring RS exon (n = 5001) and those containing only low-scoring RS exons (n = 5001). (Top) Wilcoxon rank-sum test P-values.

FIGURE 7.

A schematic summarizing suitable approaches for identification of binding sites of RBPs versus RAFs. RNA (dark line) is shown bound by RBPs (lower two ovals) and an RAF (upper oval). Methods suitable for binding site enrichment of the two classes of RNA-associated proteins are on the right.