Amino Acid Animals Computational Biology Databases de novo gene Evolution Genetic Genome Humans lncRNA Mice Molecular Molecular Sequence Data Nucleic Acid Proteins Proteins: chemistry Proteins: genetics Repetitive Sequences ribosome profiling RNA-Seq Selection Sequence Analysis Sequence Homology transcriptomics yeast
2018 |
William R Blevins, Teresa Tavella, Simone G Moro, Bernat Blasco-Moreno, Adrià Closa-Mosquera, Juana Díez, Lucas B Carey, M. Mar Albà bioRxiv, Dec 19, 2018. (Abstract | Links | BibTeX | Tags: Ribo-Seq) @article{Blevins2018, title = {Using ribosome profiling to quantify differences in protein expression: a case study in Saccharomyces cerevisiae oxidative stress conditions}, author = {William R Blevins, Teresa Tavella, Simone G Moro, Bernat Blasco-Moreno, Adrià Closa-Mosquera, Juana Díez, Lucas B Carey, M. Mar Albà}, url = {https://doi.org/10.1101/501478 }, year = {2018}, date = {2018-12-19}, journal = {bioRxiv, Dec 19}, volume = { }, abstract = {Cells respond to changes in the environment by modifying the concentration of specific proteins. Paradoxically, the cellular response is usually examined by measuring variations in transcript abundance by high throughput RNA sequencing (RNA-Seq), instead of directly measuring protein concentrations. This happens because RNA-Seq-based methods provide better quantitative estimates, and more extensive gene coverage, than proteomics-based ones. However, variations in transcript abundance do not necessarily reflect changes in the corresponding protein abundance. How can we close this gap? Here we explore the use of ribosome profiling (Ribo-Seq) to perform differentially gene expression analysis in a relatively well-characterized system, oxidative stress in baker yeast. Ribo-Seq is an RNA sequencing method that specifically targets ribosome-protected RNA fragments, and thus is expected to provide a more accurate view of changes at the protein level than classical RNA-Seq. We show that gene quantification by Ribo-Seq is indeed more highly correlated with protein abundance, as measured from mass spectrometry data, than quantification by RNA-Seq. The analysis indicates that, whereas a subset of genes involved in oxidation-reduction processes is detected by both types of data, the majority of the genes that happen to be significant in the RNA-Seq-based analysis are not significant in the Ribo-Seq analysis, suggesting that they do not result in protein level changes. The results illustrate the advantages of Ribo-Seq to make inferences about changes in protein abundance in comparison with RNA-Seq.}, keywords = {Ribo-Seq} } Cells respond to changes in the environment by modifying the concentration of specific proteins. Paradoxically, the cellular response is usually examined by measuring variations in transcript abundance by high throughput RNA sequencing (RNA-Seq), instead of directly measuring protein concentrations. This happens because RNA-Seq-based methods provide better quantitative estimates, and more extensive gene coverage, than proteomics-based ones. However, variations in transcript abundance do not necessarily reflect changes in the corresponding protein abundance. How can we close this gap? Here we explore the use of ribosome profiling (Ribo-Seq) to perform differentially gene expression analysis in a relatively well-characterized system, oxidative stress in baker yeast. Ribo-Seq is an RNA sequencing method that specifically targets ribosome-protected RNA fragments, and thus is expected to provide a more accurate view of changes at the protein level than classical RNA-Seq. We show that gene quantification by Ribo-Seq is indeed more highly correlated with protein abundance, as measured from mass spectrometry data, than quantification by RNA-Seq. The analysis indicates that, whereas a subset of genes involved in oxidation-reduction processes is detected by both types of data, the majority of the genes that happen to be significant in the RNA-Seq-based analysis are not significant in the Ribo-Seq analysis, suggesting that they do not result in protein level changes. The results illustrate the advantages of Ribo-Seq to make inferences about changes in protein abundance in comparison with RNA-Seq. |
2017 |
Jorge Ruiz-Orera, José Luis Villanueva-Cañas, William Blevins, M.Mar Albà De novo gene evolution: How do we transition from non-coding to coding? (Conference) PeerJ preprints 5 (e3031v2), 2017, (The SMBE 2017 Collection). (Abstract | Links | BibTeX | Tags: de novo gene, long non-coding RNA, Ribo-Seq, ribosome profiling) @conference{Ruiz-Orera2017, title = {De novo gene evolution: How do we transition from non-coding to coding?}, author = {Jorge Ruiz-Orera, José Luis Villanueva-Cañas, William Blevins, M.Mar Albà}, url = {https://doi.org/10.7287/peerj.preprints.3031v2}, year = {2017}, date = {2017-06-28}, journal = {PeerJ Preprints}, volume = {PeerJ preprints 5}, number = {e3031v2}, abstract = {Recent years have witnessed the discovery of protein–coding genes which appear to have evolved de novo from previously non-coding sequences. This has changed the long-standing view that coding sequences can only evolve from other coding sequences. However, there are still many open questions regarding how new protein-coding sequences can arise from non-genic DNA. Two prerequisites for the birth of a new functional protein-coding gene are that the corresponding DNA fragment is transcribed and that it is also translated. Transcription is known to be pervasive in the genome, producing a large number of transcripts that do not correspond to conserved protein-coding genes, and which are usually annotated as long non-coding RNAs (lncRNA). Recently, sequencing of ribosome protected fragments (Ribo-Seq) has provided evidence that many of these transcripts actually translate small proteins. We have used mouse non-synonymous and synonymous variation data to estimate the strength of purifying selection acting on the translated open reading frames (ORFs). Whereas a subset of the lncRNAs are likely to actually be true protein-coding genes (and thus previously misclassified), the bulk of lncRNAs code for proteins which show variation patterns consistent with neutral evolution. We also show that the ORFs that have a more favorable, coding-like, sequence composition are more likely to be translated than other ORFs in lncRNAs. This study provides strong evidence that there is a large and ever-changing reservoir of lowly abundant proteins; some of these peptides may become useful and act as seeds for de novo gene evolution.}, note = {The SMBE 2017 Collection}, keywords = {de novo gene, long non-coding RNA, Ribo-Seq, ribosome profiling} } Recent years have witnessed the discovery of protein–coding genes which appear to have evolved de novo from previously non-coding sequences. This has changed the long-standing view that coding sequences can only evolve from other coding sequences. However, there are still many open questions regarding how new protein-coding sequences can arise from non-genic DNA. Two prerequisites for the birth of a new functional protein-coding gene are that the corresponding DNA fragment is transcribed and that it is also translated. Transcription is known to be pervasive in the genome, producing a large number of transcripts that do not correspond to conserved protein-coding genes, and which are usually annotated as long non-coding RNAs (lncRNA). Recently, sequencing of ribosome protected fragments (Ribo-Seq) has provided evidence that many of these transcripts actually translate small proteins. We have used mouse non-synonymous and synonymous variation data to estimate the strength of purifying selection acting on the translated open reading frames (ORFs). Whereas a subset of the lncRNAs are likely to actually be true protein-coding genes (and thus previously misclassified), the bulk of lncRNAs code for proteins which show variation patterns consistent with neutral evolution. We also show that the ORFs that have a more favorable, coding-like, sequence composition are more likely to be translated than other ORFs in lncRNAs. This study provides strong evidence that there is a large and ever-changing reservoir of lowly abundant proteins; some of these peptides may become useful and act as seeds for de novo gene evolution. |
