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RNA G-quadruplexes cause eIF4A-dependent oncogene translation in cancer.

Wolfe AL, Singh K, Zhong Y, Drewe P, Rajasekhar VK, Sanghvi VR, Mavrakis KJ, Jiang M, Roderick JE, Van der Meulen J, Schatz JH, Rodrigo CM, Zhao C, Rondou P, de Stanchina E, Teruya-Feldstein J, Kelliher MA, Speleman F, Porco JA, Pelletier J, Rätsch G, Wendel HG - Nature (2014)

Bottom Line: Accordingly, inhibition of eIF4A with silvestrol has powerful therapeutic effects against murine and human leukaemic cells in vitro and in vivo.Notably, among the most eIF4A-dependent and silvestrol-sensitive transcripts are a number of oncogenes, superenhancer-associated transcription factors, and epigenetic regulators.Hence, the 5' UTRs of select cancer genes harbour a targetable requirement for the eIF4A RNA helicase.

View Article: PubMed Central - PubMed

Affiliation: 1] Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA [2] Weill Cornell Graduate School of Medical Sciences, New York, New York 10065, USA [3].

ABSTRACT
The translational control of oncoprotein expression is implicated in many cancers. Here we report an eIF4A RNA helicase-dependent mechanism of translational control that contributes to oncogenesis and underlies the anticancer effects of silvestrol and related compounds. For example, eIF4A promotes T-cell acute lymphoblastic leukaemia development in vivo and is required for leukaemia maintenance. Accordingly, inhibition of eIF4A with silvestrol has powerful therapeutic effects against murine and human leukaemic cells in vitro and in vivo. We use transcriptome-scale ribosome footprinting to identify the hallmarks of eIF4A-dependent transcripts. These include 5' untranslated region (UTR) sequences such as the 12-nucleotide guanine quartet (CGG)4 motif that can form RNA G-quadruplex structures. Notably, among the most eIF4A-dependent and silvestrol-sensitive transcripts are a number of oncogenes, superenhancer-associated transcription factors, and epigenetic regulators. Hence, the 5' UTRs of select cancer genes harbour a targetable requirement for the eIF4A RNA helicase.

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Ribosome profiling quality control data and effects on translationa and b) Read counts by length of mapped sequence before and after filtering rRNA, linker reads, non-coding RNAs, short mapped sequences (“noisy” reads; see text and method for details), n = 2 biological replicates; c and d) Read length frequency histograms and mapping analysis of ribosome footprint data after quality control filtering for vehicle treated cells (c) or Silvestrol treated cells (d), n = 2 biological replicates; e) Silvestrol induced changes in total RNA (log2 Fold change RPKM) and ribosome protected RNA (RF), n = 2 biological replicates; f) Histogram of all genes’ ribosome footprint intensity (measured as unique read number per million per gene, RPM) for Silvestrol and vehicle treated cells indicating Silvestrol affected mRNAs were broadly distributed (see text for details), n = 2 biological replicates; g) Mean fluorescence intensity of incorporated L-azidohomoalanine (AHA) in newly synthesized proteins in KOPT-K1 cells treated with vehicle (DMSO), Silvestrol (Silv. 25 nM), or Cycloheximide (CHX 100 nM) for the indicated time period, n = 3 biological replicates; h) Polyribosome profiles of Silvestrol (25 nM) or vehicle (DMSO) treated KOPT-K1 cells showing OD254 absorption across the ribosome containing fractions, n = 3 biological replicates; i) Ribosome density for transcripts across control and Silvestrol samples (ribosomal footprint (RF) reads per kilobases per million reads (RPKM)), n = 2 biological replicates. The correlation (R2 = 0.94) indicates a broad effect on translation and transcripts with significantly differential changes in ribosome density are indicated as red and blue dots; j) Length comparison of 5′UTRs of TE up genes and a background gene set; *: mean, n = 2 biological replicates; k) Percentage of TE up genes and background genes containing the indicated sequence motifs; *: p < 0.001, n = 2 biological replicates.
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Figure 9: Ribosome profiling quality control data and effects on translationa and b) Read counts by length of mapped sequence before and after filtering rRNA, linker reads, non-coding RNAs, short mapped sequences (“noisy” reads; see text and method for details), n = 2 biological replicates; c and d) Read length frequency histograms and mapping analysis of ribosome footprint data after quality control filtering for vehicle treated cells (c) or Silvestrol treated cells (d), n = 2 biological replicates; e) Silvestrol induced changes in total RNA (log2 Fold change RPKM) and ribosome protected RNA (RF), n = 2 biological replicates; f) Histogram of all genes’ ribosome footprint intensity (measured as unique read number per million per gene, RPM) for Silvestrol and vehicle treated cells indicating Silvestrol affected mRNAs were broadly distributed (see text for details), n = 2 biological replicates; g) Mean fluorescence intensity of incorporated L-azidohomoalanine (AHA) in newly synthesized proteins in KOPT-K1 cells treated with vehicle (DMSO), Silvestrol (Silv. 25 nM), or Cycloheximide (CHX 100 nM) for the indicated time period, n = 3 biological replicates; h) Polyribosome profiles of Silvestrol (25 nM) or vehicle (DMSO) treated KOPT-K1 cells showing OD254 absorption across the ribosome containing fractions, n = 3 biological replicates; i) Ribosome density for transcripts across control and Silvestrol samples (ribosomal footprint (RF) reads per kilobases per million reads (RPKM)), n = 2 biological replicates. The correlation (R2 = 0.94) indicates a broad effect on translation and transcripts with significantly differential changes in ribosome density are indicated as red and blue dots; j) Length comparison of 5′UTRs of TE up genes and a background gene set; *: mean, n = 2 biological replicates; k) Percentage of TE up genes and background genes containing the indicated sequence motifs; *: p < 0.001, n = 2 biological replicates.

Mentions: For footprinting studies, we treated KOPT-K1 cells with 25 nM of Silvestrol or vehicle for 45 minutes, then deep-sequenced total RNA and ribosome protected RNA (ribosome footprints = RFs) (Figure 3a)14. We removed reads mapping to ribosomal RNAs, non-coding RNAs, library linkers, and incomplete alignments (Extended Data Fig. 4a/b). The majority of the remaining reads between 25–35 nucleotides in length mapped to protein coding genes (Extended Data Fig. 4c/d). The total number of RF reads that mapped to exons was 3.2 million in control and 3.4 million Silvestrol samples and this corresponded to ~11,128 protein coding genes. RF reads showed a wider variation between control and Silvestrol than total RNA sequences indicating minimal transcriptional variation (Extended Data Fig. 4e). The number of ribosomes occupying any transcript is given as gene specific RF reads per one million total reads (RPM). The RPM frequency distribution in control and Silvestrol samples was overlapping, indicating that Silvestrol equally affected mRNAs with high and low ribosome occupancy (Extended Data Fig. 4f). Polysome analysis and metabolic labelling with L-azidohomoalanine (AHA) labelling confirmed an inhibitory effect on translation (AHA: Silvestrol ~ 60%; p(Silv. vs. Veh.) = 3.6 × 10−3; Cycloheximide 80%, p(CHX vs. Veh.) = 2 × 10−4) (Extended Data Fig. 4g/h). The translational efficiency (TE) for each mRNA is calculated by normalizing the RF frequency to transcript length and total transcript abundance (RPKM: reads per kilobase per million reads). RPKM values for RF from vehicle and Silvestrol samples were correlated (R2 = 0.94) indicating an overall inhibitory effect (Extended Data Fig. 4i).


RNA G-quadruplexes cause eIF4A-dependent oncogene translation in cancer.

Wolfe AL, Singh K, Zhong Y, Drewe P, Rajasekhar VK, Sanghvi VR, Mavrakis KJ, Jiang M, Roderick JE, Van der Meulen J, Schatz JH, Rodrigo CM, Zhao C, Rondou P, de Stanchina E, Teruya-Feldstein J, Kelliher MA, Speleman F, Porco JA, Pelletier J, Rätsch G, Wendel HG - Nature (2014)

Ribosome profiling quality control data and effects on translationa and b) Read counts by length of mapped sequence before and after filtering rRNA, linker reads, non-coding RNAs, short mapped sequences (“noisy” reads; see text and method for details), n = 2 biological replicates; c and d) Read length frequency histograms and mapping analysis of ribosome footprint data after quality control filtering for vehicle treated cells (c) or Silvestrol treated cells (d), n = 2 biological replicates; e) Silvestrol induced changes in total RNA (log2 Fold change RPKM) and ribosome protected RNA (RF), n = 2 biological replicates; f) Histogram of all genes’ ribosome footprint intensity (measured as unique read number per million per gene, RPM) for Silvestrol and vehicle treated cells indicating Silvestrol affected mRNAs were broadly distributed (see text for details), n = 2 biological replicates; g) Mean fluorescence intensity of incorporated L-azidohomoalanine (AHA) in newly synthesized proteins in KOPT-K1 cells treated with vehicle (DMSO), Silvestrol (Silv. 25 nM), or Cycloheximide (CHX 100 nM) for the indicated time period, n = 3 biological replicates; h) Polyribosome profiles of Silvestrol (25 nM) or vehicle (DMSO) treated KOPT-K1 cells showing OD254 absorption across the ribosome containing fractions, n = 3 biological replicates; i) Ribosome density for transcripts across control and Silvestrol samples (ribosomal footprint (RF) reads per kilobases per million reads (RPKM)), n = 2 biological replicates. The correlation (R2 = 0.94) indicates a broad effect on translation and transcripts with significantly differential changes in ribosome density are indicated as red and blue dots; j) Length comparison of 5′UTRs of TE up genes and a background gene set; *: mean, n = 2 biological replicates; k) Percentage of TE up genes and background genes containing the indicated sequence motifs; *: p < 0.001, n = 2 biological replicates.
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Related In: Results  -  Collection

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Figure 9: Ribosome profiling quality control data and effects on translationa and b) Read counts by length of mapped sequence before and after filtering rRNA, linker reads, non-coding RNAs, short mapped sequences (“noisy” reads; see text and method for details), n = 2 biological replicates; c and d) Read length frequency histograms and mapping analysis of ribosome footprint data after quality control filtering for vehicle treated cells (c) or Silvestrol treated cells (d), n = 2 biological replicates; e) Silvestrol induced changes in total RNA (log2 Fold change RPKM) and ribosome protected RNA (RF), n = 2 biological replicates; f) Histogram of all genes’ ribosome footprint intensity (measured as unique read number per million per gene, RPM) for Silvestrol and vehicle treated cells indicating Silvestrol affected mRNAs were broadly distributed (see text for details), n = 2 biological replicates; g) Mean fluorescence intensity of incorporated L-azidohomoalanine (AHA) in newly synthesized proteins in KOPT-K1 cells treated with vehicle (DMSO), Silvestrol (Silv. 25 nM), or Cycloheximide (CHX 100 nM) for the indicated time period, n = 3 biological replicates; h) Polyribosome profiles of Silvestrol (25 nM) or vehicle (DMSO) treated KOPT-K1 cells showing OD254 absorption across the ribosome containing fractions, n = 3 biological replicates; i) Ribosome density for transcripts across control and Silvestrol samples (ribosomal footprint (RF) reads per kilobases per million reads (RPKM)), n = 2 biological replicates. The correlation (R2 = 0.94) indicates a broad effect on translation and transcripts with significantly differential changes in ribosome density are indicated as red and blue dots; j) Length comparison of 5′UTRs of TE up genes and a background gene set; *: mean, n = 2 biological replicates; k) Percentage of TE up genes and background genes containing the indicated sequence motifs; *: p < 0.001, n = 2 biological replicates.
Mentions: For footprinting studies, we treated KOPT-K1 cells with 25 nM of Silvestrol or vehicle for 45 minutes, then deep-sequenced total RNA and ribosome protected RNA (ribosome footprints = RFs) (Figure 3a)14. We removed reads mapping to ribosomal RNAs, non-coding RNAs, library linkers, and incomplete alignments (Extended Data Fig. 4a/b). The majority of the remaining reads between 25–35 nucleotides in length mapped to protein coding genes (Extended Data Fig. 4c/d). The total number of RF reads that mapped to exons was 3.2 million in control and 3.4 million Silvestrol samples and this corresponded to ~11,128 protein coding genes. RF reads showed a wider variation between control and Silvestrol than total RNA sequences indicating minimal transcriptional variation (Extended Data Fig. 4e). The number of ribosomes occupying any transcript is given as gene specific RF reads per one million total reads (RPM). The RPM frequency distribution in control and Silvestrol samples was overlapping, indicating that Silvestrol equally affected mRNAs with high and low ribosome occupancy (Extended Data Fig. 4f). Polysome analysis and metabolic labelling with L-azidohomoalanine (AHA) labelling confirmed an inhibitory effect on translation (AHA: Silvestrol ~ 60%; p(Silv. vs. Veh.) = 3.6 × 10−3; Cycloheximide 80%, p(CHX vs. Veh.) = 2 × 10−4) (Extended Data Fig. 4g/h). The translational efficiency (TE) for each mRNA is calculated by normalizing the RF frequency to transcript length and total transcript abundance (RPKM: reads per kilobase per million reads). RPKM values for RF from vehicle and Silvestrol samples were correlated (R2 = 0.94) indicating an overall inhibitory effect (Extended Data Fig. 4i).

Bottom Line: Accordingly, inhibition of eIF4A with silvestrol has powerful therapeutic effects against murine and human leukaemic cells in vitro and in vivo.Notably, among the most eIF4A-dependent and silvestrol-sensitive transcripts are a number of oncogenes, superenhancer-associated transcription factors, and epigenetic regulators.Hence, the 5' UTRs of select cancer genes harbour a targetable requirement for the eIF4A RNA helicase.

View Article: PubMed Central - PubMed

Affiliation: 1] Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA [2] Weill Cornell Graduate School of Medical Sciences, New York, New York 10065, USA [3].

ABSTRACT
The translational control of oncoprotein expression is implicated in many cancers. Here we report an eIF4A RNA helicase-dependent mechanism of translational control that contributes to oncogenesis and underlies the anticancer effects of silvestrol and related compounds. For example, eIF4A promotes T-cell acute lymphoblastic leukaemia development in vivo and is required for leukaemia maintenance. Accordingly, inhibition of eIF4A with silvestrol has powerful therapeutic effects against murine and human leukaemic cells in vitro and in vivo. We use transcriptome-scale ribosome footprinting to identify the hallmarks of eIF4A-dependent transcripts. These include 5' untranslated region (UTR) sequences such as the 12-nucleotide guanine quartet (CGG)4 motif that can form RNA G-quadruplex structures. Notably, among the most eIF4A-dependent and silvestrol-sensitive transcripts are a number of oncogenes, superenhancer-associated transcription factors, and epigenetic regulators. Hence, the 5' UTRs of select cancer genes harbour a targetable requirement for the eIF4A RNA helicase.

Show MeSH
Related in: MedlinePlus