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How many RNA-binding proteins can simultaneously bind on a single mRNA?

How many RNA-binding proteins can simultaneously bind on a single mRNA?


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Typically, how many RNA-binding proteins can simultaneously bind to a single mRNA? Or said differently, how many "binding sites" does an mRNA have? What order of magnitude?

I am interested in RNA granules like stress granules or P-bodies. They contain, inter alia, mRNA and RNA-binding proteins. I am not a biologist and I didn't come across this information so far in the related literature.


See this paper. They have studied RBP-protected sites in the entire human transcriptome by RNA-protein crosslinking followed by RNAse digestion and sequencing: PIPseq.

Figure 1 of the paper shows distribution of protein protected sites in RNAs. They also correlate it with different regions of mRNA and its expression.

They show number of protein protected sites (PPS) per transcript but that is not a proper metric in my opinion. The number should be normalized with transcript length so that you get density of protected sites. From figure 4 (see below) you can roughly estimate that average PPS density is close to 0.6 which means that 60% of any RNA is expected to be protein bound.

Figure 4

Other points to be noted:

  • Highly translated mRNAs will have multiple ribosomes on their CDS and are likely to be more protected.
  • Sequestered RNAs in stress granules will also have high density of PPS
  • Footprint of different RBPs will be different. So number of proteins that can bind to a mRNA will differ between different RBPs.

Further reading:


Dissection of the target specificity of the RNA-binding protein HOW reveals dpp mRNA as a novel HOW target

David Israeli, Ronit Nir, Talila Volk Dissection of the target specificity of the RNA-binding protein HOW reveals dpp mRNA as a novel HOW target. Development 1 June 2007 134 (11): 2107–2114. doi: https://doi.org/10.1242/dev.001594

Regulation of RNA metabolism plays a major role in controlling gene expression during developmental processes. The Drosophila RNA-binding protein Held out wing (HOW), regulates an array of developmental processes in embryonic and adult growth. We have characterized the primary sequence and secondary structural requirements for the HOW response element (HRE), and show that this site is necessary and sufficient for HOW binding. Based on this analysis, we have identified the Drosophila TGFβ homolog, dpp, as a novel direct target for HOW negative regulation in the wing imaginal disc. The binding of the repressor isoform HOW(L) to the dpp3′ untranslated region (UTR) leads to a reduction of GFP-dpp3′UTR reporter levels in S-2 cells, in an HRE site-dependent manner. Moreover, co-expression of HOW(L) in the wing imaginal disc with a dpp-GFP fusion construct led to a reduction in DPP-GFP levels in a dpp-3′UTR-dependent manner. Conversely, a reduction of the endogenous levels of HOW by targeted expression of HOW-specific double-stranded RNA led to a corresponding elevation in dpp mRNA level in the wing imaginal disc. Thus, by characterizing the RNA sequences that bind HOW, we demonstrate a novel aspect of regulation, at the mRNA level,of Drosophila DPP.


MATERIALS AND METHODS

Statistics and data presentation

For experiments involving cultured cells, samples generated from individual wells or plates were considered biologic replicates. For zebrafish experiments, each larval fish was considered a biologic replicate. In the figure legend for each experiment, the number of independent biologic replicates and how the data are presented in the figure (typically mean ± sem ) are clearly indicated. Significance values calculated with unpaired Student's t test are clearly indicated within data figures. Data were plotted/fit and statistics generated using Prism 7 or 8 (GraphPad, La Jolla, CA, USA).

Reagents

All HNE used in this study was HNE(alkyne) (referred to as HNE in the manuscript/figures for clarity) and was synthesized as previously reported ( 37 ). Unless otherwise indicated, all other chemical reagents were bought from MilliporeSigma (Burlington, MA, USA) at the highest availability purity. Tris(2-carboxyethyl)phosphine (TCEP) was from Chem-Impex International (Wood Dale, IL, USA). Puromycin was from Santa Cruz Biotechnology (Dallas, TX, USA). Actinomycin D was from MilliporeSigma. AlamarBlue was from Thermo Fisher Scientific (Waltham, MA, USA) and was used according to the manufacturer's instructions. Minimal Essential Medium (MEM), Opti-MEM, Dulbecco's PBS, 100X pyruvate (100 mM), 100X nonessential amino acids (11140-050), and 100X penicillin streptomycin (15140-122) were from Thermo Fisher Scientific. Protease inhibitor cocktail Complete EDTA-free was from Roche (Basel, Switzerland). 3XFlag peptide was from ApexBio Technology (Houston, TX, USA). Anti-Flag(M2) resin (A2220) was from MilliporeSigma. Talon (635503) resin was from Clontech Laboratories (Mountain View, CA, USA). Ni-NTA agarose (30210) was from Qiagen (Hilden, Germany). 2020 and LT1 transfection reagents were from Mirus Bio (Madison, WI, USA). DharmaFECT I and Duo were from Dharmacon (Lafeyette, CO, USA). Polyethylenimine was from Polysciences (Warrington, PA, USA). Venor GeM PCR-based mycoplasma detection kit was from MilliporeSigma. ECL substrate and ECL-Plus substrate were from Thermo Fisher Scientific and were used as directed. Acrylamide, ammonium persulfate, tetramethylethylenediamine, Precision Plus protein standard were from Bio-Rad (Hercules, CA, USA). All lysates were quantified using the Bio-Rad Protein Assay (Bio-Rad) relative to bovine serum albumin (BSA) as a standard (Bio-Rad). PCR was carried out using Phusion Hot start II (Thermo Fisher Scientific) as per the manufacturer's protocol. All plasmid inserts were validated by sequencing at Cornell Biotechnology sequencing core facility (Ithaca, NY, USA). All sterile cell culture plasticware was from CellTreat Scientific Products (Pepperell, MA, USA).

Generation of plasmids

Sequences of all primers used for cloning and site-directed mutagenesis are listed in Supplemental Table S9. All plasmids generated were fully validated by Sanger sequencing at the Cornell Genomics Core Facility.

pCS2+8 Flag3HuR was generated by PCR amplification of human HuR (plasmid provided from the Hla lab), extension of the resulting product, and cloning into linearized pCS2+8 vector (plasmid 34931 Addgene, Watertown, MA, USA). For recombinant expression, the same procedure was used to clone HuR into a pET28a vector.

pLJM60 AUF1 p42 was obtained from Addgene (plasmid 38242) and cloned with a Flag2 tag into pCS2+8 as described above. Using the resulting plasmid, AUF1 p37 and AUF1 p45 were generated by deletion of AUF1-exon 7 and addition of AUF1-exon 2, respectively. AUF1 p40 was generated by addition of AUF1-exon 2 to AUF1 p37 . For recombinant expression, these constructs were cloned into a pET28a vector.

The pSGG luciferase reporter plasmid ( 38 ) containing the Nrf2 3'-UTR was obtained from Prof. Qun Zhou (University of Maryland School of Medicine, Baltimore, MD, USA). This plasmid was modified for intron-reporter assays by cloning fragments of the Nrf2 transcript with or without introns (amplified from genomic DNA or cDNA prepared from HEK293T cells, respectively) upstream of the luciferase coding sequence.

Short hairpin (sh)-RNA-resistant expression plasmids (for rescue experiments) were produced by PCR amplification of the starting plasmid with forward and reverse mutagenesis primers containing the desired mutations (Supplemental Table S9) followed by DpnI (NEB) treatment. These plasmids code for the same protein but contain mismatches (highlighted in red in the primer sequences Supplemental Table S9) in the shRNA-targeting sequence, allowing expression of the ectopic protein in knockdown cells.

Cell culture

HEK293T [obtained from American Type Culture Collection (ATCC), Manassas, VA, USA] and mouse embryonic endothelial cells (MEECs generated in the Hla lab) were cultured in MEM (51090036 Thermo Fisher Scientific) supplemented with 10% v/v fetal bovine serum (FBS MilliporeSigma), penicillin/streptomycin (Thermo Fisher Scientific), sodium pyruvate (Thermo Fisher Scientific), and non-essential amino acids (Thermo Fisher Scientific) at 37°C in a humidified atmosphere of 5% CO2. Medium was changed every 2-3d.

Generation of shRNA-based knockdown cell lines

HEK293T packaging cells (5.5 X 10 5 cells) were seeded in 6-well plates in antibiotic-free medium and incubated for 24 h. The cells were transfected with a mixture of 500 ng packaging plasmid (pCMV-R8.74psPAX2), 50 ng of envelope plasmid (pCMV-VSV-G), and 500 ng of pLKO vector containing the hairpin sequence (MilliporeSigma Supplemental Table S2) using TransIT-LT1 (Mirus Bio) following the manufacturer's protocol. shControl plasmid was obtained from Prof. Andrew Grimson (Cornell University). Eighteen hours post-transfection, the medium was replaced with medium containing 20% FBS and incubated for a further 24 h. Medium containing virus particles was harvested, centrifuged (800 g, 10 min), filtered through a 0.45-μm filter, and used for infection or frozen at -80°C for later use.

HEK293T or MEEC cells (5.5 X 10 6 cells) in 6-well plates were treated with 1 ml of virus-containing medium in a total volume of 6 ml of medium containing 8 μg/ml polybrene (MilliporeSigma). After 24 h, the medium was changed and the cells were incubated for 24 h. Following this period, the medium was changed to medium containing 2 μg/ml puromycin (Santa Cruz Biotechnology). Following selection, cells were assayed by Western blotting.

RNA sequencing

Following treatment of shHuR/shControl cells with HNE (50 μM) or H2O2 (225 μM) for 18 h, cells were lysed by the addition of Trizol (Thermo Fisher Scientific), and RNA was isolated following the manufacturer's protocol. The quality of the RNA was assessed by Nanodrop spectrophotometry (A260/ A280 ratio

2) and fragment analysis using a Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA quality numbers for all samples were 10.0. The rest of the procedure, including data analysis, was performed by the RNA sequencing core service at Cornell University. Briefly, rRNA was subtracted by hybridization from total RNA samples (100 ng total RNA input) using the RiboZero Magnetic Gold H/M/R Kit (Illumina, San Diego, CA, USA). Following cleanup by precipitation, rRNA-subtracted samples were quantified with a Qubit 2.0 (RNA HS kit Thermo Fisher Scientific). TruSeq-barcoded RNA-seq libraries were generated from half of the rRNA-subtracted samples with the NEBNext Ultra II Directional RNA Library Prep Kit (New England Biolabs, Ipswich, MA, USA). Each library was quantified with a Qubit 2.0 (dsDNA HS kit Thermo Fisher Scientific), and the size distribution was determined with a Fragment Analyzer (Agilent Technologies) prior to pooling. Libraries were sequenced on a NextSeq500 instrument (Illumina). At least 60 M single-end 75 bp reads were generated per library. Reads were trimmed for low quality and adaptor sequences with cutadapt v.1.8 [parameters: -m50 -q20 -a 5‘-AGATCGGAAGAGCACACGTCTGAACT-CCAGTC-3‘ -match-read-wildcards ( 39 )]. Trimmed reads were mapped to rRNA sequences with bowtie2 v.2.2 to remove them [parameters: default mapping options, -al and -un to split matching and non-matching reads ( 40 )]. Reads were mapped to the reference genome/transcriptome (GRCh37/hg19) using tophat v.2.1 [parameters: -library-type = fr-firststrand -no-novel-juncs -G < ref_genes.gtf > ( 41 )]. Cufflinks v.2.2 (cuffnorm/cuffdiff) was used to generate fragments per kilobase of transcript per million mapped reads (FPKM) values and statistical analysis of differential gene expression ( 42 ). RNA-Sequencing data have been submitted to the Gene Expression Omnibus (GEO accession number GSE127444).

Growth inhibition assays

HEK293T cells (4000 cells/well) were seeded in 96-well plates. After 24 h, cells were treated with the indicated molecules at the indicated concentrations and incubated for 48 h. AlamarBlue (Thermo Fisher Scientific) was added to each well, and the cells were incubated for a further 3 h, after which fluorescence (excitation 560 nm emission 590 nm) was measured using a BioTek Cytation 3 microplate reader (BioTek, Winooski, VT, USA). To test whether reduced alamarBlue is oxidized by H2O2 over the time-scales used, HEK293T cells in a 96-well plate were incubated with the manufacturer's recommended concentration of alamarBlue for 3 h to reduce the dye. The growth medium containing reduced alamarBlue was collected, pooled, centrifuged to remove cells, and then incubated with the concentrations of H2O2 used in the viability experiment (Supplemental Fig. S2B), and fluorescence was measured over time as previously described. We observed

3% loss of fluorescence after 4 h in reduced alamarBlue treated with 1250 μM H2O2 the fluorescence of samples at all other concentrations was not significantly different from equivalent medium containing reduced alamarBlue but not treated with H2O2. Thus, this control experiment confirmed that our experimental conditions involving oxidants are compatible with redox-sensitive alamarBlue reagents employed in our growth inhibition assays.

Knockdown of HuR and Nrf2 with small interfering RNA

Small interfering (si)-RNAs targeting the open reading frame of human HuR or Nrf2 were obtained from Dharmacon (Supplemental Table S6), and nontargeting control siRNAs (Control siRNA-A or -E) were obtained from Santa Cruz Biotechnology. HEK293T (3.6 X 10 5 cells) in 6-well plates were transfected with siRNA using Dharmafect I (Dharmacon) for 48 h following the manufacturer's protocol, then assayed. For cotransfection of siRNA and plasmid(s) for reporter assays, cells were transfected with Dharmafect Duo (Dharmacon) for 48 h following the manufacturer's protocol, then assayed.

Western blotting

Cells were resuspended in RIPA buffer (Santa Cruz Biotechnology) supplemented with protease and phosphatase inhibitors and lysed by 3 cycles of rapid freeze-thaw. Lysates were cleared by centrifugation (20,000 g, 10 min, 4°C) and total protein concentration was determined by the Bradford assay. Typically, 20-40 μg of total protein was loaded per lane, separated by SDS-PAGE, transferred to PVDF, blocked, and incubated with the appropriate antibodies (Supplemental Table S10). Detection was carried out on a ChemiDoc-MP imaging system (Bio-Rad) using ECL Western blotting Substrate (Thermo Fisher Scientific) or SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific). Western blot data were quantitated using the Gel Analysis tool in ImageJ (National Institutes of Health, Bethesda, MD, USA). Bands of interest were integrated and normalized to the loading control.

Luciferase reporter assays

Luciferase reporter assays were carried out as previously described ( 43 ). Briefly, cells were cotransfected with firefly luciferase plasmid [pGL4.37 E364A for antioxidant response element:luciferase assays (Promega, Madison, WI, USA) pSGG containing the human Nrf2 3'-UTR sequence (from Prof. Qun Zhou, University of Maryland School of Medicine)] and Renilla luciferase plasmid [pGL4.75, E693A (Promega)] in a 40:1 ratio for 48 h using TransIT 2020 (Mirus Bio) or Dharmafect Duo (Dharmacon). For experiments involving ectopic protein expression, the 40:1 reporter plasmid mixture was cotransfected with ectopic protein expression plasmid in a 1:1 ratio. Cells were lysed for 15 min in passive lysis buffer (Promega) with gentle shaking, and homogenized lysate was transferred to the wells of an opaque white 96-well plate (Corning, Corning, NY, USA). Firefly and Renilla luciferase activity was measured on a BioTek Cytation3 microplate reader. For assays involving HNE treatment, medium was replaced with medium containing the indicated concentration of HNE at 30 h post-transfection, and cells were incubated for a further 18 h.

Real-time quantitative PCR

Real-time quantitative PCR (qPCR) was carried out as previously described ( 44 ). Total RNA was isolated from cells using Trizol reagent (Thermo Fisher Scientific) following the manufacturer's protocol. One microgram of total RNA (purity/integrity assessed by agarose gel electrophoresis and concentration determined by A260 nm using a BioTek Cytation3 microplate reader with a Take3 accessory) was treated with amplification-grade DNase I (Thermo Fisherr Scientific) and reverse transcribed using Oligo(dT)20 as a primer and Superscript III Reverse Transcriptase (Thermo Fisherr Scientific) following the manufacturer's protocol. PCR was performed for 2 technical replicates per sample using iQ SYBR Green Supermix (Bio-Rad) and primers specific to the gene of interest (Supplemental Table S11) following the manufacturer's protocol. Amplicons were chosen that were 150-200 bp in length and had no predicted off-target binding predicted by the Basic Local Alignment Search Tool (BLAST National Center for Biotechnology Information, Bethesda, MD, USA). For genes with multiple splice variants, primers that amplified conserved sequences across all splice variants were chosen. Primers were validated using standard curves generated by amplification of serially diluted cDNA primers with a standard curve slope between -0.8 and 1.2 and R 2 ≥ 0.97 were considered efficient. Single PCR products were confirmed by melting analysis following the PCR protocol. Data were collected using a LightCycler 480 (Roche). Threshold cycles were determined using the LightCycler 480 software. Samples with a threshold cycle >35 or without a single, correct melting point were not included in data analysis. Normalization was carried out using a single housekeeping gene as indicated in each dataset and the ΔΔCt method.

Analysis of reporter mRNA levels

HEK293T cells (1.6 X 10 5 ) in 12-well plates were transfected with luciferase reporters as previously described for 48 h. Cells were then lysed in 250 μl of passive lysis buffer for 15 min with shaking, and 50 μl of lysate was taken to measure luciferase activity as described above. Trizol LS was added to the remaining lysate, and RNA was isolated as described above. Prior to RT, total RNA (1 mg) was treated with amplification-grade DNase I (Thermo Fisher Scientific) to remove any residual plasmid. RT and real-time qPCR was then carried out as above.

RNA immunoprecipitation-PCR

RNA immunoprecipitation (RIP)-PCR was carried out following previously described methods ( 45 ). HEK293T cells (4.5 X 10 6 )in 10-cm-diameter plates were transfected with the indicated constructs (mixed 1:3 with empty plasmid) or empty plasmid alone (8 μg total DNA per plate) with polyethylenimine (21 g/plate). Medium was changed 24 h post-transfection, and the cells were incubated for 48 h total. Cells were washed once with PBS (Thermo Fisher Scientific), harvested by trypsinization, and washed thoroughly with PBS.

Cell pellets were resuspended in polysome lysis buffer [10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (Chem-Impex International) pH 7.0, 100 mM KCl (Thermo Fisher Scientific), 5 mM MgCl2 (Thermo Fisher Scientific), 0.5% Nonidet-P40, Complete EDTA-free Protease Inhibitor Cocktail (Roche), 0.2% vanadyl ribonuceloside complex (New England Biolabs)] and frozen at -80°C for at least 30 min. The lysate was thawed, centrifuged twice (20,000 g, 10 min each), and the protein concentration was determined using Bradford Assay. Three milligrams of total protein was diluted to 0.5 mg/ml in NT2 buffer [50 mM Tris pH 7.4, 150 mM NaCl (Thermo Fisher Scientific), 1 mM MgCl2 (Thermo Fisher Scientific), 0.05% Nonidet-P40, 0.2% vanadyl ribonuceloside complex] and incubated with 50 μl of Flag M2 agarose (MilliporeSigma) for 3 h at 4°C with end-over-end rotation. The resin was washed at least 4 times (5 min/wash) with NT2 buffer containing 300 mM NaCl, and a portion of the resin was retained for Western blot analysis. The resin was resuspended in 100 μl of NT2, supplemented with 0.1% SDS and 3 mg/ml proteinase K (Santa Cruz Biotechnology), and incubated at 55°C for 30 min. RNA was isolated using Trizol reagent following the manufacturer's protocol and analyzed by real-time qPCR as previously described.

Analysis of mRNA stability

HEK293T shHuR/shAUF1 and shControl cells (9.6 X 10 5 cells) in 6-well plates were treated with 5 μg/ml actinomycin D (MilliporeSigma) and harvested in Trizol as described above at the indicated time points. The level of Nrf2-mRNA remaining was assessed by real-time qPCR as previously described.

Nuclear-cytosolic fractionation for RNA isolation

This procedure was adapted from a reported protocol ( 46 ). HEK293T cells (9.6 X 10 5 cells) in 6-well plates were harvested by tryspinization and washed twice with cold PBS. Cell pellets were then resuspended in RSB buffer [10 mM Tris (Chem-Impex International) pH 7.4, 10 mM NaCl (Thermo Fisher Scientific), 3 mM MgCl2 (Thermo Fisher Scientific)], incubated on ice for 3 min, and centrifuged (1500 g, 3 min, 4°C). Cells were resuspended in RSBG40 buffer [10 mM Tris pH 7.4, 10 mM NaCl, 3mM MgCl2, 10% glycerol (MilliporeSigma), 0.5% Nonidet P-40, 0.5 mM DTT (VWR International, Radnor, PA, USA), and 100 U/ml RNaseOUT (Thermo Fisher Scientific)] and lysed with gentle pipetting up and down. The suspension was centrifuged (4500 g, 3 min, 4°C), and the supernatant was saved as the cytosolic fraction. The nuclear fraction (pellet) was again resuspended in RSBG40 and 10X detergent solution [final concentrations: 3.3% sodium deoxycholate (Chem-Impex International), 6.6% Tween 20 (Thermo Fisher Scientific)] was added. The nuclei were pipetted up and down several times and the suspension was incubated on ice for 5 min and then centrifuged (4500 g, 3 min, 4°C). The supernatant was pooled with the previous supernatant, and the combined sample was used as the cytosolic fraction. The nuclei were washed once more with RSBG40, centrifuged (9500 g, 3 min, 4°C), and resuspended thoroughly in RSBG40. Trizol LS was then added to each sample and RNA was isolated, treated with amplification-grade DNase I, and subjected to qPCR as described above.

Zebrafish husbandry, microinjection, and imaging

All zebrafish procedures were performed in accordance with the guidelines of U.S. National Institutes of Health and were approved by Cornell University's Institutional Animal Care and Use Committee (IACUC). Tg(-3.4gstp1:GFP)it416b fish were obtained from Riken Brain Science Institute, (Wako, Japan National BioResource Project, Zebrafish). Transgenic fish were crossed with wild-type fish to generate a mixture of heterozygous and wild-type progeny for experiments. Fertilized eggs at the single-cell stage were injected with 8 ng of morpholino oligonucleotides (MOs) targeting zHuR (elavl1a) or zAUF1 (hnrnpd) or a random control MO (Gene Tools, Philomath, OR, USA Supplemental Table S7). Fish were screened for green fluorescence protein (GFP) expression by imaging with a Leica M205-FA fluorescence stereoscope (Leica Microsystems, Wetzlar, Germany)

24 h postfertilization. Following imaging, fish were euthanized, washed twice in PBS with 0.1% Tween-20, and transferred to 4% formaldehyde for further immunofluoresence analysis (see below). GFP expression was detected using red fluorescent antibody staining because of high background signal in the GFP (ex: 488 nm em: 520-550 nm) channel at this zebrafish developmental stage, which prevents accurate quantitation.

Whole-mount immunofluorescence

Whole-mount immunostaining of zebrafish larvae was carried out as previously described ( 47 ). All incubation steps were performed with gentle rocking. Euthanized fish were washed twice with PBST (PBS + 0.1% Tween-20) and fixed in 4% formaldehyde in PBS at 4°C overnight or up to 7 d. Fish were then washed twice for 30 min with PDT (PBST, 0.3% Triton X-100, 1% DMSO), blocked for 1 h at room temperature in blocking buffer (PBST, 10% FBS, 2% BSA), and incubated with primary antibody (Supplemental Table S10) in blocking buffer for 2 h at room temperature or overnight at 4°C. Fish were washed twice with PDT, reblocked for 1 h at room temperature, and incubated with secondary antibody (Supplemental Table S10) in blocking buffer for 1.5 h at room temperature. Following secondary antibody incubation, fish were washed twice with PDT and then imaged. Fluorescence was quantified using the “Measure” tool in ImageJ (National Institutes of Health, Bethesda, MD, USA).

Expression and purification of His6-HuR

BL21-CodonPlus cells (Agilent Technologies) were transformed with pET28a plasmid encoding His6-TEV-HuR. A single colony was divided into several starter cultures (5 ml) and grown in lysogeny broth medium containing chloramphenicol and kanamycin overnight at 37°C with shaking. Cultures were then each diluted into 1 L of lysogeny broth containing kanamycin and grown at 37°C with shaking to optical density (OD)600 nm = 0.5, at which point isopropyl-β-D-thiogalactoside (1 mM final concentration GoldBio, St. Louis, MO, USA) was added to induce expression. The temperature was then reduced to 18°C, and cultures were grown overnight with shaking. Further steps were carried out at 4°C. Cells were harvested by centrifugation (4000 g, 20 min, 4°C), resuspended in lysis buffer [20 mM Tris pH 7.5, 500 mM KCl, 2 mM MgCl2, 10 mM imidazole (Thermo Fisher Scientific), 5% glycerol, 5 mM β-mercaptoethanol (βME), and 0.5 mM phenylmethylsulfonyl fluoride (Enzo Life Sciences, Farmingdale, NY, USA)] and lysed by 2 passages through an Emlusiflex cell disruptor (Avestin Europe, Mannheim, Germany). Debris was cleared by centrifugation (20,000 g, 30 min, 4°C). Streptomycin sulfate (1% wt/vol) was added dropwise over 20 min with gentle stirring, and precipitated material was cleared by centrifugation (20,000 g, 30 min, 4°C). The lysate then was incubated with nickel resin (Qiagen) for 1 h with gentle agitation. The resin was washed progressively with wash buffers (20 mM Tris pH7.5 150 mM KCl 2 mM MgCl2 50, 100, 200 mM imidazole and 5 mM βME). Protein was eluted with elution buffer [20 mM Tris pH 7.5, 150 mM KCl, 2 mM MgCl2, 300 mM imidazole (Thermo Fisher Scientific), 5 mM βME], concentrated, and loaded onto a size-exclusion chromatography column (ÄKTA purification system, Hiload 26/600 Superdex 75 prep grade GE Healthcare, Chicago, IL, USA) and run in storage buffer(20 mM Tris pH 7.5, 200 mM KCl, 2 mM MgCl2, 5% glycerol, 5 mM TCEP). The protein was collected, concentrated, divided into aliquots, flash frozen in liquid nitrogen, and stored at -80°C in single-use aliquots to avoid freeze/thaw.

Expression and purification of His6-AUF1

BL21-CodonPlus cells (Agilent Technologies) were transformed with pET28a His6-AUF1 (p37) and grown as described above. The purification procedure was the same as for His6HuR except for the buffers: lysis buffer [50 mM NaH2PO4 pH 7.6, 300 mM NaCl, 5 mM imidazole, 5 mM βME, and 1 mM phenylmethylsulfonyl fluoride, 0.5% Nonidet P40, 0.15 mg/ml lysozyme, 0.1 mg/ml RNase A (MilliporeSigma)] wash buffers (50 mM NaH2PO4 pH 7.6 150 mM NaCl 20, 50, 100, mM imidazole and 5 mM βME) elution buffer (50 mM NaH2PO4 pH 7.6, 150 mM NaCl, 250 mM imidazole, 5 mM βME). The eluate was supplemented with 0.1 mg/ml RNase A and rotated end over end for 1 h at room temperature. The resulting mixture was then dialyzed against storage buffer [50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid pH 7.6, 150 mM KCl, 2 mM MgCl2, 2 mM TCEP] for 2 h at 4°C, then dialysis buffer was changed and dialysis was allowed to proceed overnight at 4°C. The protein was then concentrated and stored (with 5% glycerol added to storage buffer) at -80°C in single-use aliquots to avoid freeze/thaw.

[ 32 P]-End-labeling of RNA oligos

RNA oligos (IDT, Coralville, IA, USA Supplemental Table S8) were resuspended in RNase-free water to 200 μM. RNA was labeled with γ-[ 32 P] ATP (PerkinElmer, Waltham, MA, USA) in a reaction containing (final concentrations) 20 pmol RNA, 20 pmol γ-[ 32 P] ATP, 1X polynucleotide kinase buffer (New England Biolabs), and 0.4 U/μl polynucleotide kinase (New England Biolabs) in a total volume of 50 μl. The reaction was incubated at 37°C for 1 h, after which 50 μl of RNase-free water and 300 μl of Trizol LS were added, and the mixture was left at room temperature for 5 min. CHCl3 (200 μl) was added, the mixture was shaken for 15 s, left at room temperature for 2 min, and then spun down briefly to collect the contents. The aqueous layer was collected and mixed with an equal volume of CHCl3, shaken, and spun down briefly. The aqueous layer was again collected, supplemented with 15 μg of GlycoBlue (Ambion) and ammonium acetate to a final concentration of 500 mM, and 3 volumes of 100% EtOH were added. The mixture was vortexed and allowed to precipitate overnight at -20°C, then centrifuged (20,000 g, 15 min, 4°C). The labeled RNA pellet was washed twice with 75% EtOH in RNase-free water, dried briefly, and resuspended in RNase-free water. Labeled RNA was stored at -80°C in single-use aliquots to avoid freeze/thaw.


RBPs exert feedback regulation at different post-transcriptional levels

RBPs can control gene expression at different steps from RNA synthesis to its decay. Often the mechanism by which RBPs control their own levels is the same by which they regulate their target RNAs. For example, splicing factors such as SR proteins or heterogeneous nuclear RNPs (hnRNPs) mediate unproductive alternative splicing (AS) of their mRNAs upon overexpression. This generates transcripts that contain premature termination codons (PTCs), which trigger rapid RNA degradation ( Wollerton et al., 2004 Lareau et al., 2007 Ni et al., 2007). Other splicing factors, such as the Fox proteins, use AS to produce dominant negative isoforms that compete with the full-length proteins ( Damianov and Black, 2010). Translational regulators, such as SRSF1, instead inhibit the translation of their own mRNAs ( Sun et al., 2010), while the export factor NXF1 binds and promotes the export of its own transcript that contains a retained intron, which leads to either to rapid degradation of this RNA or to synthesis of a truncated, inactive NXF1 protein isoform ( Li et al., 2006).

In some cases, RBPs exert homeotic feedback at multiple levels. The yeast ribosomal protein L32 for example can control both splicing and translation of its own mRNA, matching its production to the synthesis rate of ribosome precursors ( Dabeva and Warner, 1993). The Drosophila protein Sex-lethal (Sxl) even employs both positive and negative feedback mechanisms that operate at the level of splicing and translation. This results in binary switch-like gene expression (mediated by positive feedback), while simultaneously preventing deleterious overproduction of the protein by negative feedback ( Moschall et al., 2017).


How many RNA-binding proteins can simultaneously bind on a single mRNA? - Biology

Protein droplets sediment without matrix support. Spin labelling at specific positions of FUS and EPR experiments revealed protein is further compacted inside droplets while diffusion NMR quantified FUS proportions in the different phases.

A recent "Nature Chemical Biology" paper by the Allain group (IMBB) in collaboration with the Jeschke (DCHAB-PC) and Klotzsch (HEST & Humboldt) labs demonstrates a novel method to simultaneously quantify FUS in the droplets and characterize FUS structurally both in the dispersed and condensed phases.

Many RNA-binding proteins undergo liquid-liquid phase separation, which underlies the formation of membraneless organelles, such as stress granules and P-bodies. Studies of the molecular mechanism of phase separation in vitro is hampered by coalescence and sedimentation of organelle-sized droplets that interact with glass surfaces. Here we demonstrate that liquid droplets of FUS, which is a protein found in cytoplasmic aggregates of ALS and FTD patients, can be stabilized in vitro using an agarose hydrogel that acts as cytoskeleton mimic. This allows their spectroscopic characterization by liquid phase NMR and electron paramagnetic resonance (EPR) spectroscopy. Protein signals from both dispersed and condensed phases can be observed simultaneously and their respective proportions be quantified precisely. Furthermore, the agarose hydrogel acts as a cryoprotectant during shock freezing which facilitates pulsed EPR measurements at cryogenic temperatures. Surprisingly, Double Electron-Electron Resonance (DEER) measurements revealed a compaction of FUS in the condensed phase.

Figure and text kindly provided by the autors.
Both were published as well as News on the website of ETH's D-BIOL.


Biology Chapter 17 Quizzes

C) Different organisms have different types of amino acids.

B) post-transcriptional splicing

C) translation in the absence of a ribosome

B) It changes an amino acid in the encoded protein.

C)It alters the reading frame of the mRNA.

B) Transfer RNA takes information from DNA directly to a ribosome, where protein synthesis takes place.

C) Messenger RNA is transcribed from a single gene and transfers information from the DNA in the nucleus to the cytoplasm, where protein synthesis takes place.

B) The translation of codons is mediated by tRNAs in eukaryotes, but translation requires no intermediate molecules such as tRNAs in prokaryotes.

C) Prokaryotic codons usually contain different bases than those of eukaryotes.

B) The excess nucleotides (ACCA) will be cleaved off at the ribosome.

C) The 5′ cap of the mRNA will become covalently bound.

B) It is a mechanism for increasing the rate of translation.

C) It increases the rate of transcription.

B) codon that specifies the same amino acid as the original codon

C) an amino acid substitution that alters the tertiary structure of the protein

B) The genetic code is universal (the same for all organisms).

C) The genetic code is different for different domains of organisms.

B) Prokaryotic cells have complicated mechanisms for targeting proteins to the appropriate cellular organelles.

C) Extensive RNA processing is required before prokaryotic transcripts can be translated.

B) proteins are synthesized

B) reading of the next codon of mRNA

B) Genes dictate the production of specific enzymes, and affected individuals have genetic defects that cause them to lack certain enzymes.

C) Enzymes are made of DNA, and affected individuals lack DNA polymerase.

B) It supplies a source of energy for termination of translation.

C) It releases the ribosome from the ER to allow polypeptides into the cytosol.

B) binding of the anticodon to the codon and the attachment of amino acids to tRNAs

C) binding of ribosomes to mRNA

B) a polypeptide missing an amino acid

B) the ribosome reaches a stop codon

C) base pairing of activated methionine-tRNA to AUG of the messenger RNA

B) RNA polymerase transcribes through the polyadenylation signal, causing proteins to associate with the transcript and cut it free from the polymerase.

C) RNA polymerase transcribes through the terminator sequence, causing the polymerase to separate from the DNA and release the transcript.


83 Eukaryotic Post-transcriptional Gene Regulation

By the end of this section, you will be able to do the following:

  • Understand RNA splicing and explain its role in regulating gene expression
  • Describe the importance of RNA stability in gene regulation

RNA is transcribed, but must be processed into a mature form before translation can begin. This processing that takes place after an RNA molecule has been transcribed, but before it is translated into a protein, is called post-transcriptional modification. As with the epigenetic and transcriptional stages of processing, this post-transcriptional step can also be regulated to control gene expression in the cell. If the RNA is not processed, shuttled, or translated, then no protein will be synthesized.

RNA Splicing, the First Stage of Post-transcriptional Control

In eukaryotic cells, the RNA transcript often contains regions, called introns, that are removed prior to translation. The regions of RNA that code for protein are called exons . ((Figure)). After an RNA molecule has been transcribed, but prior to its departure from the nucleus to be translated, the RNA is processed and the introns are removed by splicing. Splicing is done by spliceosomes, ribonucleoprotein complexes that can recognize the two ends of the intron, cut the transcript at those two points, and bring the exons together for ligation.


Alternative RNA Splicing In the 1970s, genes were first observed that exhibited alternative RNA splicing. Alternative RNA splicing is a mechanism that allows different protein products to be produced from one gene when different combinations of exons are combined to form the mRNA ((Figure)). This alternative splicing can be haphazard, but more often it is controlled and acts as a mechanism of gene regulation, with the frequency of different splicing alternatives controlled by the cell as a way to control the production of different protein products in different cells or at different stages of development. Alternative splicing is now understood to be a common mechanism of gene regulation in eukaryotes according to one estimate, 70 percent of genes in humans are expressed as multiple proteins through alternative splicing. Although there are multiple ways to alternatively splice RNA transcripts, the original 5′-3′ order of the exons is always conserved. That is, a transcript with exons 1 2 3 4 5 6 7 might be spliced 1 2 4 5 6 7 or 1 2 3 6 7, but never 1 2 5 4 3 6 7.


How could alternative splicing evolve? Introns have a beginning- and ending-recognition sequence it is easy to imagine the failure of the splicing mechanism to identify the end of an intron and instead find the end of the next intron, thus removing two introns and the intervening exon. In fact, there are mechanisms in place to prevent such intron skipping, but mutations are likely to lead to their failure. Such “mistakes” would more than likely produce a nonfunctional protein. Indeed, the cause of many genetic diseases is abnormal splicing rather than mutations in a coding sequence. However, alternative splicing could possibly create a protein variant without the loss of the original protein, opening up possibilities for adaptation of the new variant to new functions. Gene duplication has played an important role in the evolution of new functions in a similar way by providing genes that may evolve without eliminating the original, functional protein.

Question: In the corn snake Pantherophis guttatus, there are several different color variants, including amelanistic snakes whose skin patterns display only red and yellow pigments. The cause of amelanism in these snakes was recently identified as the insertion of a transposable element into an intron in the OCA2 (oculocutaneous albinism) gene. How might the insertion of extra genetic material into an intron lead to a nonfunctional protein?

Visualize how mRNA splicing happens by watching the process in action in this video.

Control of RNA Stability

Before the mRNA leaves the nucleus, it is given two protective “caps” that prevent the ends of the strand from degrading during its journey. 5′ and 3′ exonucleases can degrade unprotected RNAs. The 5′ cap , which is placed on the 5′ end of the mRNA, is usually composed of a methylated guanosine triphosphate molecule (GTP). The GTP is placed “backward” on the 5′ end of the mRNA, so that the 5′ carbons of the GTP and the terminal nucleotide are linked through three phosphates. The poly-A tail , which is attached to the 3′ end, is usually composed of a long chain of adenine nucleotides. These changes protect the two ends of the RNA from exonuclease attack.

Once the RNA is transported to the cytoplasm, the length of time that the RNA resides there can be controlled. Each RNA molecule has a defined lifespan and decays at a specific rate. This rate of decay can influence how much protein is in the cell. If the decay rate is increased, the RNA will not exist in the cytoplasm as long, shortening the time available for translation of the mRNA to occur. Conversely, if the rate of decay is decreased, the mRNA molecule will reside in the cytoplasm longer and more protein can be translated. This rate of decay is referred to as the RNA stability. If the RNA is stable, it will be detected for longer periods of time in the cytoplasm.

Binding of proteins to the RNA can also influence its stability. Proteins called RNA-binding proteins , or RBPs, can bind to the regions of the mRNA just upstream or downstream of the protein-coding region. These regions in the RNA that are not translated into protein are called the untranslated regions , or UTRs. They are not introns (those have been removed in the nucleus). Rather, these are regions that regulate mRNA localization, stability, and protein translation. The region just before the protein-coding region is called the 5′ UTR , whereas the region after the coding region is called the 3′ UTR ((Figure)). The binding of RBPs to these regions can increase or decrease the stability of an RNA molecule, depending on the specific RBP that binds.


RNA Stability and microRNAs

In addition to RBPs that bind to and control (increase or decrease) RNA stability, other elements called microRNAs can bind to the RNA molecule. These microRNAs , or miRNAs, are short RNA molecules that are only 21 to 24 nucleotides in length. The miRNAs are made in the nucleus as longer pre-miRNAs. These pre-miRNAs are chopped into mature miRNAs by a protein called Dicer . Like transcription factors and RBPs, mature miRNAs recognize a specific sequence and bind to the RNA however, miRNAs also associate with a ribonucleoprotein complex called the RNA-induced silencing complex (RISC) . The RNA component of the RISC base-pairs with complementary sequences on an mRNA and either impede translation of the message or lead to the degradation of the mRNA.

Section Summary

Post-transcriptional control can occur at any stage after transcription, including RNA splicing and RNA stability. Once RNA is transcribed, it must be processed to create a mature RNA that is ready to be translated. This involves the removal of introns that do not code for protein. Spliceosomes bind to the signals that mark the exon/intron border to remove the introns and ligate the exons together. Once this occurs, the RNA is mature and can be translated. Alternative splicing can produce more than one mRNA from a given transcript. Different splicing variants may be produced under different conditions.

RNA is created and spliced in the nucleus, but needs to be transported to the cytoplasm to be translated. RNA is transported to the cytoplasm through the nuclear pore complex. Once the RNA is in the cytoplasm, the length of time it resides there before being degraded, called RNA stability, can also be altered to control the overall amount of protein that is synthesized. The RNA stability can be increased, leading to longer residency time in the cytoplasm, or decreased, leading to shortened time and less protein synthesis. RNA stability is controlled by RNA-binding proteins (RPBs) and microRNAs (miRNAs). These RPBs and miRNAs bind to the 5′ UTR or the 3′ UTR of the RNA to increase or decrease RNA stability. MicroRNAs associated with RISC complexes may repress translation or lead to mRNA breakdown.


Summary

Knowledge of the role of RNA networks associated with RNA modifications and RNA-RBP interactions in inflammatory and metabolic regulation in obesity is limited. However, accumulating evidence has shed light on how RNA networks dynamically affect metabolic, developmental, and inflammatory mechanisms in the pathogenesis of obesity-associated disorders. Indeed, functional changes of additional RBPs, including but not limited to LIN28A, IGF2BP2/IMP2, and HuR, are linked to the development of metabolic diseases (213�). RNA methylation influences and fate maps RNA toward translation, degradation, or even sequestration inside cellular compartments. Therefore, reversible RNA modification, particularly RNA methylation, represents a new epigenetic marker, similar to reversible DNA modifications, that provides clues to adaptive cellular responses associated with metabolic changes in response to distinct exogenous or endogenous stress stimuli including excessive nutrients, toxins, and microbial infections (216, 217). RNA modifications could affect RNA-RBP networks and emerging biological events, including RNA export and transport, RNA cleavage, maturation, and stability, as well as functional changes in RBPs (218). Unveiling these epigenetic regulatory roles of the RNA networks is important to clarify the pathogenesis of chronic diseases including, but not limited to, obesity, cardiovascular, aging, and autoimmune diseases, where the exact molecular mechanisms are still ill defined.

While RNA modifications are generally considered a fine-tuning process of cellular homeostasis, prolonged external cues, including chronic HFD-feeding, may gradually accumulate erroneous RNA modifications that drive the cell fate toward chronic and low-grade inflammatory conditions which are well-observed cellular features in obesity. This concept is supported by evidence that several inflammatory RBPs, including TLR3, TLR7, and PKR, are involved in the induction of obesity-associated chronic inflammation. As mRNA methylation modification could influence post-transcriptional gene silencing and control targeted gene expression, e.g., through affecting interactions with mRNA/miRNA/Ago proteins complex, there may be specific types and modifications of RNAs that activate metabolic and inflammatory programs in the pathogenesis of obesity.

Current technical advances have allowed us to investigate quantitative changes in many types of RNA species either coding or non-coding RNA levels, and methylation status (219�). However, the challenge is to precisely analyze the type of modification, the degree of modifications, and to predict their biological significance, particularly the effect of these changes on the RNA's secondary structure, localization, and interactions with specific RBPs. These important insights would then lead to the development of novel therapeutics to change RNA-RBP interactions in obesity and T2D. Nonetheless, to investigate the components that manage RNA specificity, regulatory mechanisms, and functions of RBPs, it is important to prioritize methods that characterize direct endogenous protein-RNA interactions. In the past decade, several RIP methods, including cross-linking and immunoprecipitation (CLIP) and methylation individual-nucleotide-resolution CLIP (miCLIP), have been developed to determine the in vivo RNA targets of RBPs (221�). Utilization of RBPs known to be involved in inflammatory and metabolic diseases to identify the “pathological” RNAs by RIP would provide unique approaches for better understanding the molecular mechanisms of chronic inflammatory diseases. Such efforts might pave the way for novel therapeutic and pharmacological targets and/or interventions for combating obesity-induced sequelae.


Results

Differentially Expressed RBPs in STAD

The study design is illustrated in Figure 1. It was revealed by GEPIA analysis that DEGs in STAD included 896 downregulated genes and 3475 upregulated genes (Figure 2A). Among the 1542 RBPs, 362 were differentially expressed with 331 upregulated and 31 downregulated (Figure 2B, Table S1).

Figure 1 Framework for analyzing RBPs in STAD.

Figure 2 Volcano plot of related DEGs in STAD. (A) Volcano plot of all DEGs in STAD. (B) Volcano plot of differentially expressed RBPs in STAD.

GO and KEGG Pathway Enrichment Analysis of the Differentially Expressed RBPs

To explore the functions and mechanisms of these differentially expressed RBPs, we performed the functional analysis for downregulated and upregulated RBPs via WebGestalt.

As shown in Table 1, significant differences were observed in functional enrichment of downregulated and upregulated RBPs. As for localization within the cell, downregulated RBPs were enriched in U2-type spliceosomal complex, spliceosomal snRNP complex, U2-type prespliceosome, prespliceosome and U1 snRNP, and upregulated RBPs in U1 snRNP, U4 snRNP, U12-type spliceosomal complex, box H/ACA snoRNP complex and histone pre-mRNA 3⮞nd processing complex. Differences in localization within the cell meant different molecular function, which was further confirmed by molecular functional analysis. Molecular functional analysis demonstrated that downregulated RBPs participated in RNA binding, mRNA binding and snRNA binding and upregulated RBPs in RNA 7-methylguanosine cap binding, exoribonuclease activity, transforming growth factor-beta receptor, box H/ACA snoRNA binding and snRNP binding (Table 1). Biological process analysis showed that downregulated RBPs were related to positive regulation of mRNA processing, mRNA splice site selection, positive regulation of RNA splicing, positive regulation of mRNA splicing and regulation of chaperone-mediated autophagy while upregulated RBPs to ribosomal subunit export from the nucleus, termination of RNA polymerase II transcription, positive regulation of cytoplasmic mRNA processing body assembly, regulation of ribonuclease activity and mRNA cleavage involved in gene silencing (Table 1).

Table 1 GO Enrichment and KEGG Pathway Analysis of Differently Expressed RBPs

Moreover, downregulated RBPs were only enriched in the spliceosome pathway, while upregulated RBPs significantly in aminoacyl-tRNA biosynthesis, ribosome biogenesis in eukaryotes, RNA transport, mRNA surveillance pathway, RNA degradation and spliceosome (Table 1).

Prognosis-Related Hub RBPs

To further analyze the effects of RBPs on the prognosis of STAD patients, we assessed the relationship between the differentially expressed RBPs and OS through the univariate Cox regression analysis and Kaplan–Meier method in the TCGA training cohort, the results of which suggested that 25 candidate hub RBPs were significantly associated with OS (Table 2). Afterwards, the impacts of these 25 candidate hub RBPs on OS were evaluated by multivariate analysis, the results of which demonstrated that seven hub RBPs were independent prognostic predictors for STAD patients (Table 2).

Table 2 Cox Regression Analysis for Identification of Hub RBPs in the Training Dataset

Construction of Prognostic Model

A predictive model based on the aforementioned seven hub RBPs was then established. According to the formula: RS= (0.040* Exp PTBP1) + (𢄠.052* Exp PPIH) + (0.172* Exp SMAD5) + (𢄠.208* Exp MSI2) + (𢄠.474* Exp RBM15) + (0.072* Exp MRPS17) + (𢄠.248* Exp ADAT3), RS of each individual patient was assessed. The predictive capability of RS was evaluated by survival analysis. According to the median RS, the 187 patients from TCGA training cohort were assigned into low-risk group and high-risk group. Results of survival analysis demonstrated that compared with patients of the low-risk group, those of high-risk group had significantly poorer OS (pπ.001, Figure 3A). In order to further evaluate the prognostic capability of the seven identified hub RBPs, we subsequently performed a time-dependent ROC analysis, the results of which demonstrated that the area under the ROC curve (AUC) of this RBPs RS model was 0.804 (Figure 3B), indicating the moderate diagnostic performance of this model. The survival status of patients, RS and expression heat map of the signature consisting of seven hub RBPs in the low- and high-risk subgroups are displayed in Figure 3C𠄾.

Figure 3 Risk score analysis of seven-genes prognostic model in the TCGA training cohort. (A) Survival curve for low- and high-risk subgroups. (B) ROC curves for forecasting OS based on risk score. (C) Survival status. (D) Risk score. (E) Expression heat map.

Validation of Prognostic Model

To further verify the validity of the seven RBPs-based predictive model, we analyzed the TCGA testing cohort included 184 STAD patients and GEO cohort (GSE84437) included 433 STAD patients, the results of which showed that compared with patients with low-risk score, those with high-risk score had significantly worse OS (pπ.05, Figure 4A pπ.05, Figure S1A). AUC of the TCGA testing cohort and GEO cohort were 0.644 (Figure 4B) and 0.581 (Figure S1B), which suggested good sensitivity and specificity of the predictive model. The survival status of patients, RS and expression heat map of seven hub RBPs in the TCGA testing cohorts and GEO cohort are shown in Figure 4C𠄾 and Figure S1C𠄾. Additionally, the prognostic significance of different variables was assessed among patients of TCGA training cohort, TCGA testing cohort and GEO cohort by Cox regression analysis, the results of which demonstrated that for three cohorts, RS were independent prognostic factors of OS (pπ.01, Figure 5A pπ.05, Figure 5B pπ.01, Figure S1F). The prognostic values of the seven hub RBPs were also further investigated by Kaplan-Meier plotter online tool, the results of which revealed that all the seven hub RBPs were not only significantly associated with OS but also with relapse free survival (RFS) (Figure S2). In summary, all the aforementioned results suggested that the seven RBPs-based prognostic model was reliable in predicting outcomes of STAD patients.

Figure 4 Risk score analysis of seven-genes prognostic model in the TCGA testing cohort. (A) Survival curve for low- and high-risk subgroups. (B) ROC curves for forecasting OS based on risk score. (C) Survival status. (D) Risk score. (E) Expression heat map.

Figure 5 The prognostic value of different clinical parameters. (A, B) Univariate and multivariate COX regression analysis of different clinical parameters in TCGA training and TCGA testing cohort.

Building a Predictive Nomogram

A nomogram was constructed to generate a clinically practical model that would enable physicians to evaluate the prognosis of STAD patients using the seven hub RBPs (Figure 6). Based on the results of multivariate Cox analysis, the corresponding points were assigned to each individual variable according to the point scale in the nomogram. A horizontal line was drawn to determine the point of each variable. The total point for each patient was calculated by adding up the points of all variables, based on which we estimated the survival rate of each patient at 1 year, 2 years, and 3 years.

Figure 6 Nomogram for predicting 1, 2, and 3 year OS of STAD patients in the TCGA training cohort.

Biology Network and Functions of the Seven Hub RBPs

To further investigate the functions of the seven hub RBPs in STAD, we created TF-gene, micro-RNA and tissue-specific PPI network of the seven hub RBPs. TF-gene-specific network included 68 nodes, 152 edges and 7 seeds (PTBP1, PPIH, SMAD5, MSI2, RBM15, MRPS17, and ADAT3) (Figure 7A), miRNA-gene-specific network 36 nodes, 64 edges and 4 seeds (MRPS17, MSI2, SMAD5 and PTBP1) (Figure 7B), and tissue-specific PPI network 14 nodes, 19 edges and 5 seeds (PPIH, RBM15, PTBP1, SMAD5 and MSI2) (Figure 7C). Finally, function enrichment analysis revealed that they were enriched in tRNA wobble base modification, thrombopoietin-mediated signaling pathway, Mullerian duct regression, poly-pyrimidine tract binding and U4/U6 snRNP (Figure 7D). All these results suggested that the seven hub RBPs were widely involved in many biological processes.

Figure 7 Biology network and functions of the seven hub RBPs. (A) TF-gene network. (B) miRNA-gene network. (C) Tissue-specific PPI network. (D) Functional enrichment network.

Assessment of Tumor Immune Infiltration

The effects of the seven RBPs on tumor immune infiltration were also explored, the results of which demonstrated that expression of MRPS17 and PTBP1 was negatively significantly correlated with the abundance of TILs. It was also revealed that expression of MRPS17 was negatively correlated with Th1 cell, Tem-CD8 cell, NKT cell and NK cell abundance (Figure 8A). The expression of PTBP1 was also revealed to be negatively correlated with Th1 cell, Tem-CD4 cell, NKT cell and eosinophil abundance (Figure 8B). All these results indicated that MRPS17 and PTBP1 may reduce the infiltration of immune cells.

Figure 8 The relationship between the hub RBPs expression and TILs abundance. (A) The relationship between MRPS17 expression and TILs abundance. (B) The relationship between PTBP1 expression and TILs abundance.

The Downregulation of PTBP1 Weakened the Migration and Invasion Capability of STAD Cells

Since the role of PTBP1 in STAD is still unclear, we further explored the effect of PTBP1 on the migration and invasion capability of STAD cells. PTBP1 mRNA expression in multiple STAD cell lines was detected by RT-PCR (Figure 9A). Subsequently, siRNAs (si-NC, si-1, and si-2) were transfected into AGS and HGC27 cells. RT-PCR and Western Blotting assays showed that PTBP1 expression was significantly down-regulated in STAD cells (AGS and HGC27) transfected with si-1 or si-2 compared with those transfected with si-NC (Figure 9B). Then, it was proven by transwell assays that both migration and invasion of AGS (pπ.01, Figure 9C) and HGC27 (pπ.01, Figure 9D) were significantly reduced by knockdown of PTBP1. Therefore, we could infer that PTBP1 played important roles in promoting metastasis of STAD.

Figure 9 The downregulation of PTBP1 weakened the migration and invasion capability of STAD cells. (A) PTBP1 mRNA expression level in STAD cells. (B) PTBP1 knockdown AGS and HGC27 cells were constructed and then confirmed by RT-PCR and Western blotting. (C, D) Migration and invasion capability of AGS and HGC27 cells were significantly weakened by downregulation of PTBP1. (**pπ 0.01, ***p π.001).


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* Edited by Thomas Blumenthal. Last revised February 8, 2005. Published April 18, 2006. This chapter should be cited as: Lee, M.-H. and Schedl, T. RNA-binding proteins (April 18, 2006), WormBook , ed. The C. elegans Research Community, WormBook, doi/10.1895/wormbook.1.79.1, http://www.wormbook.org.

Copyright: © 2006 Min-Ho Lee and Tim Schedl. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

§ To whom correspondence should be addressed. E-mail: [email protected] or [email protected]

† Current address: Department of Biological Sciences, University at Albany, SUNY, Albany, NY 12222 USA

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