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

Abstract

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.

Keywords: CTCF; R-DeeP; RNA; RNA dependence; RNA-binding protein; RNase; density gradient; mass spectrometry; proteome-wide; proteomics.

Figure 1.. R-DeeP: A Proteome-wide Screen to Identify RNA-dependent Protein

(A) Untreated (Control) or RNase-treated (RNase) HeLa S3 cell lysates were prepared and loaded on the top of 5% to 50% sucrose density gradients. Following ultracentrifugation, triplicates of the gradients were fractionated and subjected to mass spectrometry and western blot analysis for validation of the screen. The raw mass spectrometry data were fitted using Gaussian curves to generate various parameters (position of the maxima, amplitude difference, shift distance and amount of protein as given by the area under the curve at each peak). (B) Heatmap showing the total amount of each protein as a sum of all fractions in the control and RNase replicates. The replicates are strongly positively correlated (Pearson’s coefficient R > 0.94 for all replicate pairs). (C) Distribution of the amount of each protein per fraction in pairs of replicates after a fraction-wise normalization step. The higher the color intensity, the higher the number of points at this position. (D) Heatmaps of the sub-categories for shifting proteins (left shift, right shift or precipitated), representing the enrichment in the control (green) or in the RNase (red) fractions. (E) Graph depicting the position of the maxima for each shift in the control and the RNase sample according to the mean fit of three replicates. The bar-graph inset (bottom right) shows the number of shifts in each sub-category. (F) Classification of the proteins analyzed in the R-DeeP screen according to their shifting behavior and their prior classification (Table S1). RBP* = RBP or RBP candidate. (G) Violin plots representing the distribution of the isoelectric points of the proteins classified as in (F). RBP* = RBP or RBP candidate. The bar indicates the median in each group (p<0.05 between all groups). See also Figures S1, S2, and Tables S1, S2, S3 and S4.

Figure 2.. Validation of the R-DeeP Screen

(A) Mass spectrometry and western blot (WB) analysis for HNRNPU. Top panel: graphical representation of the protein amount in the 25 fractions of the sucrose density gradient as analyzed by mass spectrometry. Raw data (mean of 3 replicates) are indicated by the lines with markers. The lines without markers correspond to the respective Gaussian fit (control in green; RNase in red). The overall protein amount of the raw data was normalized to 100. Middle panel: WB analysis of the protein in 25 fractions of representative control and RNase samples. Bottom panel: graph of the quantitative analysis of three WB replicates depicting the mean of three experiments with standard error of the mean (SEM). (B) Same as in (A) for ASNS. (C) Graphical representation as in (A) of the amount of REEP4, HMGN1, CASP7 and THYN1 in the 25 fractions of the gradient. (D) Representative CLIP analysis of HNRNPU (positive control), ASNS (negative control), REEP4, HMGN1, CASP7 and THYN1. WB = western blot. CLIP = CLIP autoradiography. Green arrows indicate the presence of RNA bound to the protein at the respective size. Red arrows indicate the absence RNA. See also Figure S3 and Table S4.

Figure 3.. R-DeeP Analysis of Protein Interaction Networks

(A) Distribution of human proteins listed in the CORUM database in “RBP”: protein listed in at least one of the RBP resources (Table S1); “RBP-interacting”: protein engaged only in complexes with RBPs; “RBP-indirect”: protein engaged only in complexes with “RBP-interacting” proteins; “RBP-independent”: protein engaged in complexes with neither of the three RBP-related categories. (B) Proportion of RNA-dependent proteins found in the R-DeeP screen for the categories defined in (A). (C) STRING database (Szklarczyk et al., 2015) representation of the mSIN3A CORUM complex and its R-DeeP analysis. The table and the graphs illustrate the presence of a common peak in the control sample around fraction 18.6 ± 0.4. All proteins of the complex are RNA-dependent. (D) Same as in (C) for the MCM CORUM complex. See also Figures S4 and S5.

Figure 4.. Interaction Networks after RNase Treatment

(A) Graph showing the apparent molecular weight and the fraction of the first maximum in the gradient for the reference proteins (RNase A: 14 kDa, BSA: 60 kDa, Aldolase: 4 * 40 = 160 kDa, Catalase: 4 * 60 = 240 kDa and Ferritin: 24 * 20 = 480 kDa). These were used to roughly calibrate the sucrose density gradient and to estimate the apparent molecular weight (MW) of each protein after RNase treatment. The black dashed line indicates the extrapolated relation between R-DeeP gradient fraction and MW. With respect to their position on the graph, proteins were classified into four categories after RNase treatment: at monomeric MW (green), larger than expected from their MW indicating remaining complexes (blue), smaller than monomeric MW (red) and precipitated proteins (yellow). The pie chart indicates the percentage of proteins per category. (B) Illustration of the RNA-dependent shift of the UPF complex as defined in CORUM. UPF1 and UPF3B shift exactly to their monomeric weight, while UPF2 has an apparent molecular weight close to twice its predicted molecular weight. The name and positions (triangles) of the subunits are indicated on the graph from (A). (C) Same as in (B) for the RFC complex from CORUM, of which all subunits shifted but remained in a smaller complex after RNase treatment. See also Figure S6.

Figure 5.. Quantitative Analysis of the RNA-dependent Shifts

(A) Graph indicating the behavior of each protein for each pair of control and RNase peaks depicting a shifting coefficient (protein amount at maxima * loss or gain). The colors indicate proteins with no significant shift (red), with significant shifts from one control to one RNase peak (dark green) or with shifts between multiple peaks (light green). For the latter category, all possible combinations of control and RNase peaks are depicted. Proteins shifting with their full amount from a control to an RNase peak are located at the top right part of the graph (complete shift). Proteins with no change are located at the bottom left part of the graph (no shift). In between, proteins are found with a partial shift. Exemplary proteins are indicated on the graph. (B) Schematic illustration and R-DeeP analysis of the partially shifting NPM3. (C) Same as in (B) for the completely shifting HNRNPU. (D) Mass spectrometry R-DeeP analysis of partially shifting established RBPs like the splicing factors LSM8 (U6 snRNA-associated Sm-like protein LSm8), SF3B6 (Splicing factor 3B subunit 6) and SYF1 (Pre-mRNA-splicing factor SYF1) and the chromatin factor SMC4 (Structural maintenance of chromosomes protein 4). (E) Violin plots representing the distribution of the pi of proteins with full or partial shifts. The bar indicates the median, **p<0.01, two-sided t-test.

Figure 6.. RNA Dependence of CTCF Interaction with Chromatin

(A) Mass spectrometry (left panel) and western blot analysis (right panel) as in Figure 2A for CTCF. (B) Chromatin immunoprecipitation followed by qPCR analysis of two control regions not interacting with CTCF and six specific CTCF binding sites without (green) or with (red) RNase treatment. The mean of three experiments with SEM is depicted. **p<0.01, *p<0.05, two-sided t-test. (C) Left panel: western blot analysis of CTCF and Histone H3 in nucleoplasmic and chromatin fractions without (green) or after (red) RNase treatment. Right panel: quantitative analysis of three western blot replicates including standard deviation (SD). *p<0.05, two-sided t-test. (D) Confocal laser scanning fluorescence microscopy images showing CTCF (immunofluorescence, red), DNA (DAPI, green) and actin (immunofluorescence, grey) in control (Control) or RNase A-treated (RNase) HeLa cells. Scale bars: 20 μm. (E) Violin plots depicting the Pearson’s correlation coefficient of the CTCF and DNA fluorescent signal in untreated (control) as compared to RNase A-treated (RNase) HeLa cells. Three replicates with 30 cells each were analyzed. Bars indicate the median of each distribution. **p<0.01. two-sided t-test. See also Figure S7 and Table S4.

Figure 7.. R-DeeP Database: Database for RNA-dependent Proteins

The R-DeeP database provides user-friendly access to the RNA dependence analysis for currently 4765 proteins. Multiple search options offer the possibility to display information for multiple proteins at once. in addition to peaks and shift information, graphical view and download options, the database also offers details about the protein with links to other protein databases, summarizing information about the actual RBP resources and integrates the analysis of the subunits of CORUM complexes for each protein. in addition, to help reconstructing complexes, a search option for potential interaction partners from the lists of co-segregating proteins has been implemented for each fraction from the control or RNase sample.