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• W2340761255 abstract "The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been studied by transcriptomic and proteomic profiling. Here, we review the relationship between protein and mRNA levels under various scenarios, such as steady state, long-term state changes, and short-term adaptation, demonstrating the complexity of gene expression regulation, especially during dynamic transitions. The spatial and temporal variations of mRNAs, as well as the local availability of resources for protein biosynthesis, strongly influence the relationship between protein levels and their coding transcripts. We further discuss the buffering of mRNA fluctuations at the level of protein concentrations. We conclude that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes. The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been studied by transcriptomic and proteomic profiling. Here, we review the relationship between protein and mRNA levels under various scenarios, such as steady state, long-term state changes, and short-term adaptation, demonstrating the complexity of gene expression regulation, especially during dynamic transitions. The spatial and temporal variations of mRNAs, as well as the local availability of resources for protein biosynthesis, strongly influence the relationship between protein levels and their coding transcripts. We further discuss the buffering of mRNA fluctuations at the level of protein concentrations. We conclude that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes. The central dogma of biology tightly links the molecular species DNA, RNA, and protein. Whereas the nucleotide sequence of a gene determines the sequence of its mRNA product, and whereas an mRNA’s sequence determines the amino acid sequence of the resulting polypeptide, there is no trivial relationship between the concentration of a transcript and the concentration(s) of the protein(s) derived from a particular locus. Systematic studies quantifying transcripts and proteins at genomic scales revealed the importance of multiple processes beyond transcript concentration that contribute to establishing the expression level of a protein (McManus et al., 2015McManus J. Cheng Z. Vogel C. Next-generation analysis of gene expression regulation--comparing the roles of synthesis and degradation.Mol. Biosyst. 2015; 11: 2680-2689Crossref PubMed Google Scholar). These include (1) translation rates: they are significantly influenced by the mRNA sequence—e.g., through the gene’s codon composition, upstream open reading frames (uORFs) (Wethmar et al., 2010Wethmar K. Smink J.J. Leutz A. Upstream open reading frames: Molecular switches in (patho)physiology.BioEssays. 2010; 32: 885-893Crossref PubMed Scopus (71) Google Scholar), or internal ribosome entry sites (IRES); (2) translation rate modulation: translation rates can be modulated through the binding of proteins to regulatory elements on the transcript, through the binding of non-coding RNAs such as micro-RNAs (as reviewed in Barrett et al., 2012Barrett L.W. Fletcher S. Wilton S.D. Regulation of eukaryotic gene expression by the untranslated gene regions and other non-coding elements.Cell. Mol. Life Sci. 2012; 69: 3613-3634Crossref PubMed Scopus (109) Google Scholar), or through the relative availability of transcript and (charged) ribosomes; (3) modulation of a protein’s half-life: the complex ubiquitin-proteasome pathway (Tang and Amon, 2013Tang Y.C. Amon A. Gene copy-number alterations: a cost-benefit analysis.Cell. 2013; 152: 394-405Abstract Full Text Full Text PDF PubMed Scopus (70) Google Scholar), or autophagy may influence protein concentrations independent of transcript concentrations; (4) protein synthesis delay: protein synthesis takes time, and transcript changes will therefore affect protein levels only with a certain temporal delay; (5) protein transport: protein export spatially disconnects proteins from the transcripts they were synthesized from. Thus, the direct comparison between protein and mRNA abundances from the same location or from the same cell type may not be appropriate. Decades of research have investigated each of these mechanisms on a single-gene-basis. Relatively recent technological advances now also support studies into the role of these processes at a proteome-wide scale. Over the last two decades, high-throughput technologies have been developed that support the large-scale and quantitative analysis of genomes, transcriptomes, and proteomes. Datasets generated by these techniques have been used to systematically explore quantitative relationships between transcripts and proteins in a wide range of systems and conditions. The results from these studies have led to somewhat conflicting conclusions, especially with respect to the question to what extent mRNA concentrations dictate cellular protein concentrations. As already noted in previous reviews (Vogel and Marcotte, 2012Vogel C. Marcotte E.M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses.Nat. Rev. Genet. 2012; 13: 227-232Crossref PubMed Scopus (607) Google Scholar), it is important to clearly distinguish different types of protein-mRNA correlations (Figure 1). First, one can correlate the variation of protein and mRNA concentrations originating from the same gene(s) across different individuals, conditions, or time points. Such analyses explore to what extent the variation of mRNA levels is resulting in respective protein concentration changes. Second, one can correlate the concentrations of many different proteins with their respective coding transcripts. This type of analysis asks to what extent differences between mRNAs are reflected at the level of proteins. Analyses of the latter type can further be distinguished based on whether they compare relative differences in molecular concentrations between different chemical entities (defined as “relative abundance”) or whether they compare absolute cellular concentrations—i.e., essentially asking how many protein molecules per transcript are present in a cell (defined as “absolute abundance”) (Marguerat et al., 2012Marguerat S. Schmidt A. Codlin S. Chen W. Aebersold R. Bähler J. Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells.Cell. 2012; 151: 671-683Abstract Full Text Full Text PDF PubMed Scopus (147) Google Scholar). We emphasize this aspect at the beginning of this Review because a lot of confusion can arise by not clearly distinguishing these different types of correlation. In this Review, we discuss how advanced technologies support the dissection of observed protein level variance into contributions from transcriptional, translational, and post-translational processes. Second, we summarize the available information that describes under which circumstances protein levels are primarily determined by the levels of their coding mRNAs and under what conditions (and why) more complex relationships are observed. Third, we discuss global processes such as resource allocation and “buffering” mechanisms at the protein level that often attenuate the propagation of mRNA level variation to the protein level; and fourth, we address the need to account for the spatial and temporal scales at which molecules are being measured. Our improved understanding of the dichotomy between mRNA and protein levels is intimately dependent on significant recent technological advances to quantify transcripts and proteins, respectively, and the quality of the data the respective techniques generate (Figure 2). Systematic, genome-scale screens require technologies with high sensitivity, accuracy, and precision. In the following paragraphs, we briefly describe these techniques, with particular attention to their quantitative performance and data quality. Currently, the state-of-the-art technology for genome-scale transcript quantification is RNA sequencing (RNA-seq). It comprises the quantification of RNA via sequencing and then counting millions of short transcript fragments (Mortazavi et al., 2008Mortazavi A. Williams B.A. McCue K. Schaeffer L. Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq.Nat. Methods. 2008; 5: 621-628Crossref PubMed Scopus (4419) Google Scholar) and is comprehensively described elsewhere (Wang et al., 2009Wang Z. Gerstein M. Snyder M. RNA-Seq: a revolutionary tool for transcriptomics.Nat. Rev. Genet. 2009; 10: 57-63Crossref PubMed Scopus (3488) Google Scholar). Although the precision of RNA-seq is not always better than that of DNA microarrays (Black et al., 2014Black M.B. Parks B.B. Pluta L. Chu T.M. Allen B.C. Wolfinger R.D. Thomas R.S. Comparison of microarrays and RNA-seq for gene expression analyses of dose-response experiments.Toxicol. Sci. 2014; 137: 385-403Crossref PubMed Scopus (10) Google Scholar, Wang et al., 2014Wang C. Gong B. Bushel P.R. Thierry-Mieg J. Thierry-Mieg D. Xu J. Fang H. Hong H. Shen J. Su Z. et al.The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.Nat. Biotechnol. 2014; 32: 926-932Crossref PubMed Scopus (69) Google Scholar), the use of high-throughput sequencing provides a number of advantages, including the possibility to account for sequence variation, allele-specific expression, and the detection and quantification of “new” transcripts or isoforms. Further, the technique can be applied to less frequently studied species, where DNA microarrays are often not available. For a long time, it remained difficult to accurately quantify absolute RNA concentrations in cells at a large scale. The previous hybridization-based microarray analyses perform poorly when it comes to absolute transcript quantification. RNA-seq allows for actual counting of RNA molecules—e.g., by counting the sequenced fragments per kilobase of exon model per million mapped reads (FPKM). However, only a fraction of all transcripts in a sample are being sequenced, and the observed read-counts such as FPKM are biased by the sequence of the transcript. Thus, absolute quantification requires additional calibration of the data. Despite some limitations such as sequence bias, RNA-seq data can be calibrated using a number of mRNA standards with concentrations assayed by nCounter (Geiss et al., 2008Geiss G.K. Bumgarner R.E. Birditt B. Dahl T. Dowidar N. Dunaway D.L. Fell H.P. Ferree S. George R.D. Grogan T. et al.Direct multiplexed measurement of gene expression with color-coded probe pairs.Nat. Biotechnol. 2008; 26: 317-325Crossref PubMed Scopus (642) Google Scholar) and thereby provides an accurate and absolute quantitation for all transcripts (Marguerat et al., 2012Marguerat S. Schmidt A. Codlin S. Chen W. Aebersold R. Bähler J. Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells.Cell. 2012; 151: 671-683Abstract Full Text Full Text PDF PubMed Scopus (147) Google Scholar). The potentially large number of transcript isoforms that can be generated from the same gene via alternative splicing presents an important complication for the comparison of protein and mRNA levels. The possibility that peptide levels may be compared to splice isoforms that do not contain the respective peptide sequence may distort the protein/mRNA correlation. The emergence of RNA-seq has greatly improved our ability to account for splicing effects compared to earlier transcript profiling methods. This is particularly relevant when evaluating protein/mRNA correlation for a single gene (Figure 1A), as the transcript isoforms expressed may change across conditions (Brar et al., 2012Brar G.A. Yassour M. Friedman N. Regev A. Ingolia N.T. Weissman J.S. High-resolution view of the yeast meiotic program revealed by ribosome profiling.Science. 2012; 335: 552-557Crossref PubMed Scopus (146) Google Scholar). Thus, the advent of RNA-seq constituted an advance in the comparison of protein and mRNA concentrations primarily for three reasons: (1) isoforms and differential exon usage can be much better quantified, yielding better models of the actual transcript underlying a given protein; (2) precision can be higher compared to microarrays, provided samples were sequenced at sufficient depth; and (3) with appropriate standards absolute quantification is possible. Conventionally, the measurement of protein abundances in biological samples has primarily relied on antibody-based techniques, such as quantitative western blots or ELISA assays. The first limitation of the antibody-based methods is the availability of antibodies. The Human Protein Atlas project (Uhlen et al., 2010Uhlen M. Oksvold P. Fagerberg L. Lundberg E. Jonasson K. Forsberg M. Zwahlen M. Kampf C. Wester K. Hober S. et al.Towards a knowledge-based Human Protein Atlas.Nat. Biotechnol. 2010; 28: 1248-1250Crossref PubMed Scopus (742) Google Scholar), which aims to generate antibodies for every protein of the human proteome, has changed this situation (Uhlén et al., 2015Uhlén M. Fagerberg L. Hallström B.M. Lindskog C. Oksvold P. Mardinoglu A. Sivertsson Å. Kampf C. Sjöstedt E. Asplund A. et al.Proteomics. Tissue-based map of the human proteome.Science. 2015; 347: 1260419Crossref PubMed Scopus (223) Google Scholar). However, despite the advantages of measurements based on affinity reagents such as high resolution of protein localization, these methods cannot generate large-scale, quantitative protein profiles between different samples due to the lack of a universal, external standard and highly parallel methods. Over the last 20 years, mass spectrometry (MS)-based proteomics has emerged as an important analytical method for protein research, specifically for protein identification and quantification. As a versatile quantification tool, MS supports both relative and absolute protein measurement at a large scale, without need of generating antibodies (Aebersold and Mann, 2003Aebersold R. Mann M. Mass spectrometry-based proteomics.Nature. 2003; 422: 198-207Crossref PubMed Scopus (4101) Google Scholar, Domon and Aebersold, 2006Domon B. Aebersold R. Mass spectrometry and protein analysis.Science. 2006; 312: 212-217Crossref PubMed Scopus (1112) Google Scholar) (Table 1 and Figure 2). Both relative and absolute protein quantification strategies using MS have been applied for illustrating mRNA-protein correlations. Table 1 lists techniques and example datasets. Both relative and absolute quantification methods can yield high quality data. Absolute quantification (i.e., the quantification of average protein and mRNA molecules per cell) can also be used to derive actual synthesis and degradation rates, thereby providing additional insight into cellular processes that cannot be obtained through relative quantification alone. At a small-scale, protein absolute concentration (termed AQUA) can be measured by spiking synthetic isotopically labeled peptides (Gerber et al., 2003Gerber S.A. Rush J. Stemman O. Kirschner M.W. Gygi S.P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS.Proc. Natl. Acad. Sci. USA. 2003; 100: 6940-6945Crossref PubMed Scopus (1194) Google Scholar). The spike-in methods are useful to generate absolute concentration data for relatively small numbers of analytes, those for which reference samples are available. In contrast, proteome-wide, or large-scale absolute concentrations (copies per cell information) are still based on label-free quantification (LFQ), which, however, needs spike-ins to quantify “anchor proteins” for data calibration (Ludwig and Aebersold, 2014Ludwig C. Aebersold R. Chapter 4: Getting absolute: determining absolute protein quantities via selected reaction monitoring mass spectrometry.in: Quantitative Proteomics. The Royal Society of Chemistry, 2014: 80-109Crossref Google Scholar) (Table 1).Table 1Mass Spectrometric Techniques for Large-Scale Relative and Absolute Proteome Quantification and Representative Literature Citations, Indicating Their Use for mRNA-Protein Relationship AnalysisAbbreviationFull Name of the TechniqueRef. of TechniqueQuantification LevelaNote that the progress in label-free quantification (LFQ) techniques provides an example of how technical advances have led to improved data quality. The initial semi-quantitative method, spectral counting, was superseded by more accurate quantification methods based on MS1 peak intensities, and recently, a further increase in accuracy was achieved by using MS2 peak intensities as the basis for quantification (Bruderer et al., 2015; Gillet et al., 2012; Ludwig et al., 2012; Schubert et al., 2013). Spectral counts, the method simply counts the number of MS2 spectra identified for a given protein in different biological samples for protein quantification. MS1 peak intensity, the signal intensity trace of a peptide ion digested from a given protein without peptide fragmentation; MS2 peak intensity.AccuracyReproducibilityRef. Analyzing mRNA-Protein correlationRelative Quantification2DEtwo-dimensional gel electrophoresis(O’Farrell, 1975O’Farrell P.H. High resolution two-dimensional electrophoresis of proteins.J. Biol. 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In-vivo quantitative proteomics reveals a key contribution of post-transcriptional mechanisms to the circadian regulation of liver metabolism.PLoS Genet. 2014; 10: e1004047Crossref PubMed Scopus (57) Google Scholar)LFQ-spectral countinglabel-free quantification/ spectral counting(Liu et al., 2004Liu H. Sadygov R.G. Yates 3rd, J.R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics.Anal. Chem. 2004; 76: 4193-4201Crossref PubMed Scopus (1538) Google Scholar)MS2 (spectral counts)lowmiddle(Zhang et al., 2014Zhang B. Wang J. Wang X. Zhu J. Liu Q. Shi Z. Chambers M.C. Zimmerman L.J. Shaddox K.F. Kim S. et al.NCI CPTACProteogenomic characterization of human colon and rectal cancer.Nature. 2014; 513: 382-387Crossref PubMed Scopus (142) Google Scholar)LFQ-MS1label-free quantification/MS1-based(Old et al., 2005Old W.M. Meyer-Arendt K. Aveline-Wolf L. Pierce K.G. Mendoza A. Sevinsky J.R. Resing K.A. Ahn N.G. 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Proteomics. 2012; 11 (O111.016717)Crossref PubMed Scopus" @default.
• W2340761255 created "2016-06-24" @default.
• W2340761255 creator A5022041947 @default.
• W2340761255 creator A5025063698 @default.
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• W2340761255 date "2016-04-01" @default.
• W2340761255 modified "2023-10-17" @default.
• W2340761255 title "On the Dependency of Cellular Protein Levels on mRNA Abundance" @default.
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