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Tunable protein synthesis by transcript isoforms in human cells - PubMed

  • ️Fri Jan 01 2016

Tunable protein synthesis by transcript isoforms in human cells

Stephen N Floor et al. Elife. 2016.

Abstract

Eukaryotic genes generate multiple RNA transcript isoforms though alternative transcription, splicing, and polyadenylation. However, the relationship between human transcript diversity and protein production is complex as each isoform can be translated differently. We fractionated a polysome profile and reconstructed transcript isoforms from each fraction, which we term Transcript Isoforms in Polysomes sequencing (TrIP-seq). Analysis of these data revealed regulatory features that control ribosome occupancy and translational output of each transcript isoform. We extracted a panel of 5' and 3' untranslated regions that control protein production from an unrelated gene in cells over a 100-fold range. Select 5' untranslated regions exert robust translational control between cell lines, while 3' untranslated regions can confer cell type-specific expression. These results expose the large dynamic range of transcript-isoform-specific translational control, identify isoform-specific sequences that control protein output in human cells, and demonstrate that transcript isoform diversity must be considered when relating RNA and protein levels.

Keywords: RNA processing; RNA-seq; chromosomes; computational biology; deep sequencing; engineered translation; genes; human; systems biology; transcript isoforms; translational control.

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Conflict of interest statement

The authors declare that no competing interests exist.

Figures

Figure 1.
Figure 1.. Transcript Isoforms in Polysomes sequencing (TrIP-seq) measures transcript isoform specific translation.

(A) HEK 293T cells were treated with cycloheximide and the cytoplasmic fraction was extracted and applied to a sucrose gradient, which was further fractionated into individual polysomes that were converted into sequencing libraries. (B) Intersample clustering recapitulates the gradient order of polysomes, indicating the sequenced fractions are faithful to the gradient profile. (C,D) RT-PCR analysis and transcript-level quantification for ACTB and ATF4 (C) and three transcripts of EEF1B2 (D) demonstrating concordance of sequencing and transcript-specific RT-PCR. TPM – transcripts per million. *nonspecific amplicon. (E) Members of transcript classes with more than 100 reads in indicated fractions show that coding genes are represented across the polysome while noncoding genes are preferentially in low polysome fractions. (F) Spearman’s correlation (RS) between gene (left) or isoform (right) read counts from ribosome profiling or TrIP-seq. See also Figure 1—figure supplement 1 and Materials and methods for calculation of TrIP-seq polysome counts. DOI:

http://dx.doi.org/10.7554/eLife.10921.003
Figure 1—figure supplement 1.
Figure 1—figure supplement 1.. Extended TrIP-seq validation.

(A) A large version of a representative polysome profile showing fractions collected. (B) A diagram of the alternative transcript isoforms of the gene EEF1B2. Coding regions are wide boxes, noncoding regions are narrow boxes, and introns are lines. Upstream open reading frames (uORFs) are indicated by gray bars, with red octagons marking stop codons. The uORF for EEF1B2-201 is in-frame with the annotated start codon. Differentially included regions are shown in yellow, arrows show the direction of transcription, and select introns have been removed for clarity as indicated by gaps. (C) A replicate RT-PCR gel of EEF1B2 showing the presence of EEF1B2-003 in high polysomes. (D) The distribution of coding isoforms and noncoding genes across polysome fractions. (E) Replicate correlations between the weighted polysome sum of TrIP-seq read counts at the isoform level. RS: Spearman’s correlation. (F) Correlation between TrIP-seq polysome counts and HEK 293T cell iBAQ protein abundance. (G) The Jaccard distance between clusterings of resampled data from Figure 1B. DOI:

http://dx.doi.org/10.7554/eLife.10921.006
Figure 1—figure supplement 2.
Figure 1—figure supplement 2.. Extended validation of TrIP-seq isoform abundances across polysome fractions using qRT-PCR.

(A) qRT-PCR (top) and TrIP-seq abundances (bottom) for three EEF1B2 isoforms. (B) As in (A) except for the gene SRSF5. qRT-PCR abundance is calculated as a fold change versus cytoplasmic abundance. Note that systematic normalization differences exist between the TrIP-seq and qRT-PCR abundances since the TrIP-seq abundances are from repeat and rRNA subtracted data, while the input for qRT-PCR is total RNA. NTC: no template control. DOI:

http://dx.doi.org/10.7554/eLife.10921.007
Figure 1—figure supplement 3.
Figure 1—figure supplement 3.. Read Tracking Across the Sequencing Analysis Pipeline.

"Not RMSK" refers to reads not mapping to repeatmasker sequences, and "not abundant" is reads not mapping to rRNA and the mitochondrial chromosome, etc (Materials and methods). DOI:

http://dx.doi.org/10.7554/eLife.10921.008
Figure 2.
Figure 2.. Clustering of transcript distributions yields eight major clusters with diverse behavior across the polysome profile.

(A) The average relative abundance of all isoforms in each cluster across polysomes is shown. Error is s.d. (B) Hierarchical clustering of 62,703 transcript isoform distributions across the polysome profile and cytoplasmic fraction. Yellow: above isoform average, cyan: below isoform average. (C) Transcript type distribution per cluster from Ensembl-annotated biotypes. Dotted lines mark the abundance of each transcript type in all isoforms that went in to the clustering. See also Figure 1—source data 1 and 2 and Figure 2—figure supplement 1. DOI:

http://dx.doi.org/10.7554/eLife.10921.009
Figure 2—figure supplement 1.
Figure 2—figure supplement 1.. Further information on TrIP-seq clusters.

(A) Clustered transcript distributions from each biological replicate show robust clustering for the well-translated clusters 1, 2 and 6. The Jaccard distance is plotted (the intersection divided by the union) of each replicate cluster versus the pooled replicate clusters. (B) GO terms significant at the Benjamini-corrected p-value of 0.05 are indicated; clusters 3 and 4 had no enriched terms. DOI:

http://dx.doi.org/10.7554/eLife.10921.011
Figure 3.
Figure 3.. Effect of transcript features on polysome association.

(A) Meta-transcript distributions for two high polysome clusters (1 and 2) and one low polysome cluster (6). Clusters 1 and 2 were pooled for the analysis in (B). (B) The distance between distributions for 24 different transcript features evaluated for transcripts strongly or weakly associated with polysomes. Distance is the nonparametric effect size, measured as the dimensionless quantity Cliff’s d (see Materials and methods) and error bars are bootstrapped 95% confidence intervals. All differences except 5′ UTR and transcript length are significant at the p = 0.001 level based on two-tailed Mann-Whitney U-tests (Figure 3—figure supplement 1A). See Materials and methods for a description of all features and how they were tabulated. UTR – untranslated region; CDS – coding sequence. (C) Enrichment of either cognate (ATG start codon) or non-cognate (non-ATG) uORFs in high polysome versus low polysome clusters. Density is uORFs per 100 isoforms. See also Figure 3—figure supplement 1. DOI:

http://dx.doi.org/10.7554/eLife.10921.012
Figure 3—figure supplement 1.
Figure 3—figure supplement 1.. Further details of high versus low polysome associated transcript isoform comparisons.

(A) Empirical cumulative distribution functions (eCDFs) for all features compared, with p-values calculated using the Mann-Whitney U test. High polysome transcripts are from the union of clusters 1 and 2, while low polysome transcripts are from cluster 6. (B) Meta-isoform distributions for transcripts with 5′ leaders of indicated length ranges. Error bars and shaded gray area are upper and lower quartiles. (C) Percent of each TrIP-seq cluster with the indicated 5′ leader length. Note the increase in 5′ leader length in the poorly translated cluster 7. DOI:

http://dx.doi.org/10.7554/eLife.10921.013
Figure 4.
Figure 4.. Predicted isoform-specific translational control changes during human embryogenesis.

(A) For each embryonic stage, transcripts are mapped onto all eight TrIP-seq clusters, as in Figure 2. The percentage of transcripts mapping to each cluster per stage is then calculated, and compared to the percentage for TrIP-seq data. hESC – human embryonic stem cell; p0, p10 – passage zero or passage ten. Error is s.d. between single cells at each embryonic stage or between TrIP-seq biological replicates. (B) Expression levels for two transcripts of CSDE1 across embryonic development and polysome fractions demonstrating a switch in translational status. Error is S.D. TPM – transcripts per million. (C) Diagram of two transcript isoforms of CSDE1. Regions different between the two isoforms are in yellow and select shared intronic regions have been shortened for clarity. See also Figure 4—figure supplement 1. DOI:

http://dx.doi.org/10.7554/eLife.10921.014
Figure 4—figure supplement 1.
Figure 4—figure supplement 1.. Clustering of human preimplantation embryo data

(A) Hierarchical clustering of 45,895 transcript isoforms across human preimplantation embryogenesis with the stages indicated below. Yellow: above isoform average, cyan: below isoform average. DOI:

http://dx.doi.org/10.7554/eLife.10921.015
Figure 4—figure supplement 2.
Figure 4—figure supplement 2.. Additional examples of transcript isoforms that exhibit differential expression in human embryos and differential translation in TrIP-seq data.

DOI:

http://dx.doi.org/10.7554/eLife.10921.016
Figure 5.
Figure 5.. Regulatory UTR sequences are sufficient to control translation of a heterologous reporter.

HEK 293T cells were transfected with indicated RNAs and Renilla luciferase units are plotted. (A–D) Renilla light units normalized to control UTR mRNA (A,C) and corresponding traces from TrIP-seq data (B,D) for 5′ leaders (A,B) or 3′ UTRs (C,D) exhibit differential protein production over two log-units in cells. Inset graphs show comparisons between mock-transfected cells and the lowest output mRNAs. Error bars are S.E.M. from at least four biological and three technical replicates (twelve total; A) or three biological and three technical replicates (nine total; B). (B,D) Error is S.D. Rluc – Renilla luciferase units; UTR – untranslated region. (E) Luciferase fold change versus the average number of ribosomes on each transcript, computed by averaging the plots in B and D. See also Figure 5—figure supplement 1. DOI:

http://dx.doi.org/10.7554/eLife.10921.017
Figure 5—figure supplement 1.
Figure 5—figure supplement 1.. Diagrams of isoforms differentially represented in gigh versus low polysomes.

(A) Isoform diagrams for the genes CCNE2, NAB1, NAE1, NDC1, RICTOR, and SRSF5. Coding regions are wide boxes, noncoding regions are narrow boxes, introns are lines, and upstream open reading frames (uORFs) are indicated by gray bars. Differentially included regions are shown in yellow, arrows show the direction of transcription, and select introns have been removed for clarity as indicated by gaps. (B) Secondary structures of two RICTOR 5′ leader sequences predicted by RNAfold. RNA bases are colored by base pairing probability. DOI:

http://dx.doi.org/10.7554/eLife.10921.018
Figure 6.
Figure 6.. Translational control by transcript 5′ leader sequences is robust across cell types.

(A,B) Five cell lines in six conditions were transfected with Renilla luciferase fused to the UTR indicated and luciferase units normalized to control UTR mRNA. Both 5′ leaders (A) and 3′ UTRs (B) were tested. Error is S.E.M. between three technical and three biological replicates (nine total) per condition. See also Figure 6—figure supplement 1. DOI:

http://dx.doi.org/10.7554/eLife.10921.019
Figure 6—figure supplement 1.
Figure 6—figure supplement 1.. Changes in protein production conferred by 5′ UTRs are more robust across cell lines than those conferred by 3′ UTRs.

Plotted is the coefficient of variation (the standard deviation divided by the mean) for the indicated UTR fused to luciferase across five cell lines and HEK 293T cells at 2 hr and 18 hr (Figure 6). DOI:

http://dx.doi.org/10.7554/eLife.10921.020
Figure 7.
Figure 7.. Transcript processing has widespread effects on protein production.

(A) The mapping between gene and protein is complex, and RNA processing has a strong influence on the translatability of individual transcript isoforms and therefore protein production. (B) Regulatory elements can be discovered by comparing transcript isoforms that are differentially translated, and subsequently fused to heterologous genes to control protein output in cells. ORF – open reading frame. DOI:

http://dx.doi.org/10.7554/eLife.10921.021
Author response image 1.
Author response image 1.. Polysome association of noncoding genes.

(A) Clustering of noncoding transcript isoforms as in Figure 2A. (B) The fraction of transcripts per noncoding gene type in high polysome clusters red and blue (gray) versus all annotated noncoding gene derived transcripts (black). (C) The gene MCM3AP and its antisense gene MCM3AP-AS1 are polysome associated and exhibit ribosome profiling reads from two datasets, one of which is stranded (red reads: sense strand; blue reads: antisense strand). DOI:

http://dx.doi.org/10.7554/eLife.10921.023
Author response image 2.
Author response image 2.. Retained intron transcripts found in polysomes are also observed by ribosome profiling.

(A) An example of a retained intron transcript on polysomes where the retained intron region is detected through the eight+ ribosome fraction. (bottom) Ribosome profiling reads are detected in the retained intron region in two separate 293T ribosome profiling datasets. (B) Shown are fragment length distributions for ribosome profiling reads derived from either retained intro regions (red) or coding sequence regions (black). The FLOSS score (Ingolia et al., 2014) between retained intron and CDS fragment length distributions is 0.023, indicating near perfect agreement. The FLOSS score is the integrated point wise difference between the two distributions. Ribosome profiling data from the Yoon-Jae Cho lab and from Sidrauski et al. (2015). DOI:

http://dx.doi.org/10.7554/eLife.10921.024

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