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• W2946008612 abstract "Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called “integromics.” We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets. Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called “integromics.” We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets. Recent large-scale analysis of protein concentrations, modifications, and interactions has seen tremendous advances, pushing us to consider the next steps in multiomics studies. Some of the new work lies “outside the box” of standard parallel mining of individual data sets and attempts to model the relationships between proteomic variations and other molecular changes to gain insights at their interface (Fig. 1). A new field might be born: “integromics.” Integromics studies have included information on the dynamics of mRNA and protein concentration changes, but also other molecules, such as lipids and metabolites, or completely orthogonal information on genomic variation across a population of samples. Because several excellent reviews discuss the relationship between protein and mRNAs, as well as proteogenomic approaches, e.g. (1.Liu Y. Beyer A. Aebersold R. On the dependency of cellular protein levels on mRNA abundance.Cell. 2016; 165: 535-550Abstract Full Text Full Text PDF PubMed Scopus (1386) Google Scholar, 2.Vogel 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 (2487) Google Scholar, 3.Ruggles K.V. Krug K. Wang X. Clauser K.R. Wang J. Payne S.H. Fenyö D. Zhang B. Mani D.R. Methods, tools and current perspectives in proteogenomics.Mol. Cell. Proteomics. 2017; 16: 959-981Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar, 4.Rodriguez H. Pennington S.R. Revolutionizing precision oncology through collaborative proteogenomics and data sharing.Cell. 2018; 173: 535-539Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar), we will focus here on other new directions that have emerged in the last few years, e.g. with respect to combination of proteomics with other technologies or other data types. We will also discuss components of such integrative analysis. The first and long-debated question in integrative proteomic studies concerns the correlation between mRNA and protein concentrations in a steady-state system, i.e. in unperturbed cells that do not change over time. High correlation between mRNA and protein concentrations implies that transcription determines the cellular architecture; low correlation implies a dominant role for post-transcriptional regulation. For yeast and mammalian cells, estimates started to appear over ten years ago and differed considerably (5.de Sousa Abreu R. Penalva L.O. Marcotte E.M. Vogel C. Global signatures of protein and mRNA expression levels.Mol. Biosyst. 2009; 5: 1512-1526PubMed Google Scholar): although most studies agreed on substantial contribution of post-transcriptional regulation to the overall expression landscape (6.Vogel C. de Sousa Abreu R. Ko D. Le S.-Y. Shapiro B.A. Burns S.C. Sandhu D. Boutz D.R. Marcotte E.M. Penalva L.O. Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line.Mol. Syst. Biol. 2010; 6: 400Crossref PubMed Scopus (442) Google Scholar, 7.Schwanhäusser B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4059) Google Scholar), some argued for a dominant role of transcription (8.Li J.J. Bickel P.J. Biggin M.D. System wide analyses have underestimated protein abundances and the importance of transcription in mammals.PeerJ. 2014; 2: e270Crossref PubMed Scopus (108) Google Scholar). In 2012, we attempted to synthesize these findings into a common theme: transcription regulation might often act as an on-off switch, whereas translation and protein degradation fine-tune actual concentrations, like a rheostat (2.Vogel 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 (2487) Google Scholar). This two-step process attributes to the different response signals for different groups of genes (9.McManus 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). A 2015 study of bone-marrow derived dendritic cells exposed to lipopolysaccharide supported this view (10.Jovanovic M. Rooney M.S. Mertins P. Przybylski D. Chevrier N. Satija R. Rodriguez E.H. Fields A.P. Schwartz S. Raychowdhury R. Mumbach M.R. Eisenhaure T. Rabani M. Gennert D. Lu D. Delorey T. Weissman J.S. Carr S.A. Hacohen N. Regev A. Immunogenetics. Dynamic profiling of the protein life cycle in response to pathogens.Science. 2015; 347: 1259038Crossref PubMed Scopus (295) Google Scholar): indeed, most of the responses to the stimulus were initiated by RNA expression changes. In comparison, protein levels for housekeeping genes were also altered substantially when examining absolute molecule numbers. Because of the high protein concentrations, the resulting fold-changes remained comparatively small. We observed a similar trend in cancer cells responding to protein misfolding: protein concentration changes were much smaller in magnitude than mRNA expression changes (11.Cheng Z. Teo G. Krueger S. Rock T.M. Koh H.W.L. Choi H. Vogel C. Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress.Mol. Syst. Biol. 2016; 12: 855Crossref PubMed Scopus (113) Google Scholar). In addition, the transcriptome returned to pretreatment levels after ∼12 h whereas proteins did not reach steady state at that time. Our most recent study suggests that the cell might implement such discordance observed between the transcriptome and the proteome in different ways, e.g. through gene specific increase in translation via short regulatory elements despite no transcription change or an increase in transcription but delayed translation (12.Rendleman J. Cheng Z. Maity S. Kastelic N. Munschauer M. Allgoewer K. Teo G. Zhang Y.B.M. Lei A. Parker B. Landthaler M. Freeberg L. Kuersten S. Choi H. Vogel C. New insights into the cellular temporal response to proteostatic stress.Elife. 2018; 7: e39054Crossref PubMed Scopus (27) Google Scholar). Another possible explanation for the disparity between transcript and protein levels has been discussed for yeast undergoing meiosis (13.Cheng Z. Otto G.M. Powers E.N. Keskin A. Mertins P. Carr S.A. Jovanovic M. Brar G.A. Pervasive, coordinated protein-level changes driven by transcript isoform switching during meiosis.Cell. 2018; 172: 910-923.e16Abstract Full Text Full Text PDF PubMed Scopus (77) Google Scholar). When comparing protein expression levels with those of transcripts and parallel ribosome footprinting data, the authors noticed impaired translation of several genes through new isoforms which they named “long undecoded transcript isoforms” (LUTIs) 1The abbreviations used are: LUTI, long undecoded transcript isoforms; SILAC, stable isotope labeling of amino acids; QTL, quantitative tract loci; CETSA, cellular thermal shift assays; MKGI, meta-dimensional knowledge-driven genomic interactions; PECA, protein expression control analysis.. They proposed that a single transcription factor can active the canonical transcript for some genes and LUTI for others. Finally, several theoretical studies highlighted the unexpected conservation of protein-RNA ratios across tissues (14.Edfors F. Danielsson F. Hallström B.M. Käll L. Lundberg E. Pontén F. Forsström B. Uhlén M. Gene-specific correlation of RNA and protein levels in human cells and tissues.Mol. Syst. Biol. 2016; 12: 883Crossref PubMed Scopus (240) Google Scholar, 15.Wilhelm M. Schlegl J. Hahne H. Gholami A.M. Lieberenz M. Savitski M.M. Ziegler E. Butzmann L. Gessulat S. Marx H. Mathieson T. Lemeer S. Schnatbaum K. Reimer U. Wenschuh H. Mollenhauer M. Slotta-Huspenina J. Boese J.-H. Bantscheff M. Gerstmair A. Faerber F. Kuster B. Mass-spectrometry-based draft of the human proteome.Nature. 2014; 509: 582-587Crossref PubMed Scopus (1312) Google Scholar), and the fact that protein concentrations of orthologs appear to be more conserved across organisms than mRNA concentrations (16.Khan Z. Ford M.J. Cusanovich D.A. Mitrano A. Pritchard J.K. Gilad Y. Primate transcript and protein expression levels evolve under compensatory selection pressures.Science. 2013; 342: 1100-1104Crossref PubMed Scopus (155) Google Scholar, 17.Laurent J.M. Vogel C. Kwon T. Craig S.A. Boutz D.R. Huse H.K. Nozue K. Walia H. Whiteley M. Ronald P.C. Marcotte E.M. Protein abundances are more conserved than mRNA abundances across diverse taxa.Proteomics. 2010; 10: 4209-4212Crossref PubMed Scopus (101) Google Scholar, 18.Schrimpf S.P. Weiss M. Reiter L. Ahrens C.H. Jovanovic M. Malmström J. Brunner E. Mohanty S. Lercher M.J. Hunziker P.E. Aebersold R. von Mering C. Hengartner M.O. Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes.PLos Biol. 2009; 7: e48Crossref PubMed Scopus (184) Google Scholar). However, some of these observations are because of an effect like Simpson's paradox (19.Friendly M. Monette G. Fox J. Elliptical insights: understanding statistical methods through elliptical geometry.Stat. Sci. 2013; 28: 1-39Crossref Scopus (84) Google Scholar): some relationships may become reversed or masked by the opposing effects of other data types. Therefore, orthologous protein concentrations might be correlated across genes, but not as much as some studies suggest (20.Fortelny N. Overall C.M. Pavlidis P. Cohen Freue G.V. Can we predict protein from mRNA levels?.Nature. 2017; 547: E19-E20Crossref PubMed Scopus (109) Google Scholar, 21.Franks A. Airoldi E. Slavov N. Post-transcriptional regulation across human tissues.PLoS Comput. Biol. 2017; 13: e1005535Crossref PubMed Scopus (91) Google Scholar). In addition to these insights into the relationship between protein and mRNA concentrations, the future might bring more integration of these paired data sets with additional information, such as the temporal changes in response to a stimulus, physical interactions between proteins, or measurements of synthesis and turnover rates. Examples of such new directions include the time-resolved studies described above, or recent work involving Down Syndrome patients (22.Liu Y. Borel C. Li L. Müller T. Williams E.G. Germain P.-L. Buljan M. Sajic T. Boersema P.J. Shao W. Faini M. Testa G. Beyer A. Antonarakis S.E. Aebersold R. Systematic proteome and proteostasis profiling in human Trisomy 21 fibroblast cells.Nat. Commun. 2017; 8: 1212Crossref PubMed Scopus (63) Google Scholar). The study analyzed protein and mRNA concentrations in samples from identical twins where one twin is healthy and one has Down Syndrome. Thanks to careful integration of these data with protein stability measurements, the authors demonstrated the major role of degradation in maintaining stoichiometry in protein complexes, despite minor effects on overall mRNA or protein levels. Another factor lowering RNA and protein correlations arises from alternative splicing and the production of protein isoforms, and many efforts in integrative proteomics concern the complete mapping of the resulting proteome diversity (13.Cheng Z. Otto G.M. Powers E.N. Keskin A. Mertins P. Carr S.A. Jovanovic M. Brar G.A. Pervasive, coordinated protein-level changes driven by transcript isoform switching during meiosis.Cell. 2018; 172: 910-923.e16Abstract Full Text Full Text PDF PubMed Scopus (77) Google Scholar, 15.Wilhelm M. Schlegl J. Hahne H. Gholami A.M. Lieberenz M. Savitski M.M. Ziegler E. Butzmann L. Gessulat S. Marx H. Mathieson T. Lemeer S. Schnatbaum K. Reimer U. Wenschuh H. Mollenhauer M. Slotta-Huspenina J. Boese J.-H. Bantscheff M. Gerstmair A. Faerber F. Kuster B. Mass-spectrometry-based draft of the human proteome.Nature. 2014; 509: 582-587Crossref PubMed Scopus (1312) Google Scholar, 23.Uhlén M. Fagerberg L. Hallström B.M. Lindskog C. Oksvold P. Mardinoglu A. Sivertsson Å. Kampf C. Sjöstedt E. Asplund A. Olsson I. Edlund K. Lundberg E. Navani S. Szigyarto C.A.-K. Odeberg J. Djureinovic D. Takanen J.O. Hober S. Alm T. Edqvist P.-H. Berling H. Tegel H. Mulder J. Rockberg J. Nilsson P. Schwenk J.M. Hamsten M. von Feilitzen K. Forsberg M. Persson L. Johansson F. Zwahlen M. von Heijne G. Nielsen J. Pontén F. Proteomics. Tissue-based map of the human proteome.Science. 2015; 347: 1260419Crossref PubMed Scopus (7243) Google Scholar, 24.Omenn G.S. Lane L. Lundberg E.K. Overall C.M. Deutsch E.W. Progress on the HUPO Draft Human Proteome: 2017 Metrics of the Human Proteome Project.J. Proteome Res. 2017; 16: 4281-4287Crossref PubMed Scopus (48) Google Scholar, 25.Cifani P. Dhabaria A. Chen Z. Yoshimi A. Kawaler E. Abdel-Wahab O. Poirier J.T. Kentsis A. ProteomeGenerator: A framework for comprehensive proteomics based on de novo transcriptome assembly and high-accuracy peptide mass spectral matching.J. Proteome Res. 2018; 17: 3681-3692Crossref PubMed Scopus (19) Google Scholar). Integrating data from mRNA sequencing and proteomics, Liu et al. attempted to map the entire human proteome with respect to its variants (26.Liu Y. Gonzàlez-Porta M. Santos S. Brazma A. Marioni J.C. Aebersold R. Venkitaraman A.R. Wickramasinghe V.O. Impact of alternative splicing on the human proteome.Cell Rep. 2017; 20: 1229-1241Abstract Full Text Full Text PDF PubMed Scopus (87) Google Scholar). The authors identified a significant contribution of alternative splicing to proteome composition and diversity, with respect to alternative translation initiation, alternative splicing, and post-translational modifications. However, these estimates are not uncontended - other studies suggest that the number of functional variants per protein might be very small (27.Tress M.L. Abascal F. Valencia A. Alternative splicing may not be the key to proteome complexity.Trends Biochem. Sci. 2017; 42: 98-110Abstract Full Text Full Text PDF PubMed Scopus (174) Google Scholar, 28.Blencowe B.J. The Relationship between alternative splicing and proteomic complexity.Trends Biochem. Sci. 2017; 42: 407-408Abstract Full Text Full Text PDF PubMed Scopus (82) Google Scholar, 29.Tay A.P. Pang C.N.I. Twine N.A. Hart-Smith G. Harkness L. Kassem M. Wilkins M.R. Proteomic validation of transcript isoforms, including those assembled from RNA-Seq data.J. Proteome Res. 2015; 14: 3541-3554Crossref PubMed Scopus (13) Google Scholar). The reason for these discrepancies may lie in technical challenges to identify critical peptides that mark variants and isoforms (29.Tay A.P. Pang C.N.I. Twine N.A. Hart-Smith G. Harkness L. Kassem M. Wilkins M.R. Proteomic validation of transcript isoforms, including those assembled from RNA-Seq data.J. Proteome Res. 2015; 14: 3541-3554Crossref PubMed Scopus (13) Google Scholar) or in the fact that most proteins get expressed only one isoform per tissue (23.Uhlén M. Fagerberg L. Hallström B.M. Lindskog C. Oksvold P. Mardinoglu A. Sivertsson Å. Kampf C. Sjöstedt E. Asplund A. Olsson I. Edlund K. Lundberg E. Navani S. Szigyarto C.A.-K. Odeberg J. Djureinovic D. Takanen J.O. Hober S. Alm T. Edqvist P.-H. Berling H. Tegel H. Mulder J. Rockberg J. Nilsson P. Schwenk J.M. Hamsten M. von Feilitzen K. Forsberg M. Persson L. Johansson F. Zwahlen M. von Heijne G. Nielsen J. Pontén F. Proteomics. Tissue-based map of the human proteome.Science. 2015; 347: 1260419Crossref PubMed Scopus (7243) Google Scholar). Therefore, identification of functional proteoforms and alternative splice variants remains a daunting task. Several recent studies have moved beyond simple assessment of the relationships between concentrations and toward identification of the underlying processes that determine concentrations and concentration changes. One example is the twin study mentioned above that included examination of protein degradation through use of dynamic proteomics (22.Liu Y. Borel C. Li L. Müller T. Williams E.G. Germain P.-L. Buljan M. Sajic T. Boersema P.J. Shao W. Faini M. Testa G. Beyer A. Antonarakis S.E. Aebersold R. Systematic proteome and proteostasis profiling in human Trisomy 21 fibroblast cells.Nat. Commun. 2017; 8: 1212Crossref PubMed Scopus (63) Google Scholar). Other examples arise from the inclusion of sequencing data that identifies ribosome footprint positions along mRNAs, estimating translation efficiency and regulatory elements (30.Ingolia N.T. Ghaemmaghami S. Newman J.R.S. Weissman J.S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling.Science. 2009; 324: 218-223Crossref PubMed Scopus (2395) Google Scholar). Several such studies exist and examined meiosis or the response to environmental stress (13.Cheng Z. Otto G.M. Powers E.N. Keskin A. Mertins P. Carr S.A. Jovanovic M. Brar G.A. Pervasive, coordinated protein-level changes driven by transcript isoform switching during meiosis.Cell. 2018; 172: 910-923.e16Abstract Full Text Full Text PDF PubMed Scopus (77) Google Scholar, 15.Wilhelm M. Schlegl J. Hahne H. Gholami A.M. Lieberenz M. Savitski M.M. Ziegler E. Butzmann L. Gessulat S. Marx H. Mathieson T. Lemeer S. Schnatbaum K. Reimer U. Wenschuh H. Mollenhauer M. Slotta-Huspenina J. Boese J.-H. Bantscheff M. Gerstmair A. Faerber F. Kuster B. Mass-spectrometry-based draft of the human proteome.Nature. 2014; 509: 582-587Crossref PubMed Scopus (1312) Google Scholar, 23.Uhlén M. Fagerberg L. Hallström B.M. Lindskog C. Oksvold P. Mardinoglu A. Sivertsson Å. Kampf C. Sjöstedt E. Asplund A. Olsson I. Edlund K. Lundberg E. Navani S. Szigyarto C.A.-K. Odeberg J. Djureinovic D. Takanen J.O. Hober S. Alm T. Edqvist P.-H. Berling H. Tegel H. Mulder J. Rockberg J. Nilsson P. Schwenk J.M. Hamsten M. von Feilitzen K. Forsberg M. Persson L. Johansson F. Zwahlen M. von Heijne G. Nielsen J. Pontén F. Proteomics. Tissue-based map of the human proteome.Science. 2015; 347: 1260419Crossref PubMed Scopus (7243) Google Scholar, 24.Omenn G.S. Lane L. Lundberg E.K. Overall C.M. Deutsch E.W. Progress on the HUPO Draft Human Proteome: 2017 Metrics of the Human Proteome Project.J. Proteome Res. 2017; 16: 4281-4287Crossref PubMed Scopus (48) Google Scholar, 25.Cifani P. Dhabaria A. Chen Z. Yoshimi A. Kawaler E. Abdel-Wahab O. Poirier J.T. Kentsis A. ProteomeGenerator: A framework for comprehensive proteomics based on de novo transcriptome assembly and high-accuracy peptide mass spectral matching.J. Proteome Res. 2018; 17: 3681-3692Crossref PubMed Scopus (19) Google Scholar, 31.Van Dalfsen K.M. Hodapp S. Keskin A. Otto G.M. Berdan C.A. Higdon A. Cheunkarndee T. Nomura D.K. Jovanovic M. Brar G.A. Global proteome remodeling during ER stress involves Hac1-driven expression of long undecoded transcript isoforms.Dev. Cell. 2018; 46: 219-235.e8Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar, 32.Ho Y.-H. Shishkova E. Hose J. Coon J.J. Gasch A.P. Decoupling yeast cell division and stress defense implicates mRNA repression in translational reallocation during stress.Curr. Biol. 2018; 28: 2673-2680.e4Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar). For example, when comparing genome-wide transcriptome, proteome, and ribosome profiles across diverse stresses, Ho et al. found that ribosomes appeared to dissociate from some transcripts, delaying their translation into the corresponding proteins (32.Ho Y.-H. Shishkova E. Hose J. Coon J.J. Gasch A.P. Decoupling yeast cell division and stress defense implicates mRNA repression in translational reallocation during stress.Curr. Biol. 2018; 28: 2673-2680.e4Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar). The authors suggested that this process frees ribosomes which could then be used toward the synthesis of stress response proteins. However, importantly, the association of ribosomes with mRNAs may not always reflect actual translation output, as can be measured by proteomics methods such as pulsed SILAC (stable isotope labeling of amino acids) (33.Schwanhäusser B. Gossen M. Dittmar G. Selbach M. Global analysis of cellular protein translation by pulsed SILAC.Proteomics. 2009; 9: 205-209Crossref PubMed Scopus (245) Google Scholar). Ribosomes might attach to mRNAs leading to reported footprints, but not actively translate and produce protein. Such discrepancy was observed for data from multiple myeloma cells (34.Liu T.-Y. Huang H.H. Wheeler D. Xu Y. Wells J.A. Song Y.S. Wiita A.P. Time-resolved proteomics extends ribosome profiling-based measurements of protein synthesis dynamics.Cell Syst. 2017; 4: 636-644.e9Abstract Full Text Full Text PDF PubMed Scopus (31) Google Scholar). Under unperturbed conditions, ribosome footprinting and pulsed-SILAC translation measurements largely correlated across genes. However, when the cells were perturbed through inhibition of protein degradation, the correlation vanished: pulsed-SILAC was able to detect global alterations in translation rates across genes, whereas ribosome footprinting failed to do so. Therefore, albeit proteomics methods often pose technical challenges and result in smaller coverage than sequencing methods, some biological questions might demand the use of proteomics for translation measurements over assessment of ribosome footprints. An entirely orthogonal area of proteomic data integration lies in their use as molecular phenotypes that are then associated with specific genomic regions to discover Quantitative Trait Loci (QTL). Associations with mRNA expression phenotypes are typically called eQTLs, whereas associations with protein expression render pQTLs. The relationship between eQTLs and pQTLs is complex and still only incompletely understood (35.Albert F.W. Treusch S. Shockley A.H. Bloom J.S. Kruglyak L. Genetics of single-cell protein abundance variation in large yeast populations.Nature. 2014; 506: 494-497Crossref PubMed Scopus (85) Google Scholar, 36.Albert F.W. Bloom J.S. Siegel J. Day L. Kruglyak L. Genetics of -regulatory variation in gene expression.Elife. 2018; 7: e35471Crossref PubMed Scopus (62) Google Scholar). For example, in yeast, the genomic position of eQTLs and pQTLs seems to overlap only little (37.Foss E.J. Radulovic D. Shaffer S.A. Ruderfer D.M. Bedalov A. Goodlett D.R. Kruglyak L. Genetic basis of proteome variation in yeast.Nat. Genet. 2007; 39: 1369-1375Crossref PubMed Scopus (185) Google Scholar), and cis regulation is common for eQTLs but not at the level of the proteome (38.Foss E.J. Radulovic D. Shaffer S.A. Goodlett D.R. Kruglyak L. Bedalov A. Genetic variation shapes protein networks mainly through non-transcriptional mechanisms.PLos Biol. 2011; 9: e1001144Crossref PubMed Scopus (80) Google Scholar). One important role of pQTLs appear to be maintenance of the stoichiometry of protein complexes and pathways (39.Picotti P. Clément-Ziza M. Lam H. Campbell D.S. Schmidt A. Deutsch E.W. Röst H. Sun Z. Rinner O. Reiter L. Shen Q. Michaelson J.J. Frei A. Alberti S. Kusebauch U. Wollscheid B. Moritz R.L. Beyer A. Aebersold R. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis.Nature. 2013; 494: 266-270Crossref PubMed Scopus (225) Google Scholar). More recent studies in blood plasma cells confirmed the complexity of the relationship between pQTLs and eQTLs. For example, they detected several pQTLs that affected protein levels in trans, illustrating how pQTLs can identify effects hidden at the mRNA level (40.Suhre K. Arnold M. Bhagwat A.M. Cotton R.J. Engelke R. Raffler J. Sarwath H. Thareja G. Wahl A. DeLisle R.K. Gold L. Pezer M. Lauc G. El-Din Selim M.A. Mook-Kanamori D.O. Al-Dous E.K. Mohamoud Y.A. Malek J. Strauch K. Grallert H. Peters A. Kastenmüller G. Gieger C. Graumann J. Connecting genetic risk to disease end points through the human blood plasma proteome.Nat. Commun. 2017; 8: 14357Crossref PubMed Scopus (253) Google Scholar). Other studies found several pQTLs acting in cis and reported substantial overlap between pQTLs and cis-eQTLs—contrasting what had been observed before (41.Sun W. Kechris K. Jacobson S. Drummond M.B. Hawkins G.A. Yang J. Chen T.-H. Quibrera P.M. Anderson W. Barr R.G. Basta P.V. Bleecker E.R. Beaty T. Casaburi R. Castaldi P. Cho M.H. Comellas A. Crapo J.D. Criner G. Demeo D. Christenson S.A. Couper D.J. Curtis J.L. Doerschuk C.M. Freeman C.M. Gouskova N.A. Han M.K. Hanania N.A. Hansel N.N. Hersh C.P. Hoffman E.A. Kaner R.J. Kanner R.E. Kleerup E.C. Lutz S. Martinez F.J. Meyers D.A. Peters S.P. Regan E.A. Rennard S.I. Scholand M.B. Silverman E.K. Woodruff P.G. O'Neal W.K. Bowler R.P. SPIROMICS Research Group, and COPDGene Investigators Common genetic polymorphisms influence blood biomarker measurements in COPD.PLoS Genet. 2016; 12: e1006011Crossref PubMed Scopus (61) Google Scholar, 42.Jiang L.-G. Li B. Liu S.-X. Wang H.-W. Li C.-P. Song S.-H. Beatty M. Zastrow-Hayes G. Yang X.-H. Qin F. He Y. Characterization of proteome variation during modern maize breeding.Mol. Cell. Proteomics. 2018; 18: 263-276Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar, 43.Di Narzo A.F. Telesco S.E. Brodmerkel C. Argmann C. Peters L.A. Li K. Kidd B. Dudley J. Cho J. Schadt E.E. Kasarskis A. Dobrin R. Hao K. High-throughput characterization of blood serum proteomics of IBD patients with respect to aging and genetic factors.PLoS Genet. 2017; 13: e1006565Crossref PubMed Scopus (30) Google Scholar). Protocols to measure post-translational modifications such as phosphorylation, ubiquitination, and SUMOylation are now readily available for routine use. Recent work integrated such measurements with other 'omics data, i.e. protein concentrations. For example, a study in mice showed substantial overlap between protein abundance and phosphorylation levels but revealed differences in the temporal patterns upon induction of a high-fat diet (44.Krahmer N. Najafi B. Schueder F. Quagliarini F. Steger M. Seitz S. Kasper R. Salinas F. Cox J. Uhlenhaut N.H. Walther T.C. Jungmann R. Zeigerer A. Borner G.H.H. Mann M. Organellar proteomics and phospho-proteomics reveal subcellular reorganization in diet-induced hepatic steatosis.Dev. Cell. 2018; 47: 205-221.e7Abstract Full Text Full Text PDF PubMed Scopus (75) Google Scholar). A similar observation was made in samples from breast cancer patients: the phosphoproteome grouped into clusters that were undetectable at the mRNA or protein level, illustrating the need for collecting multiple data types (45.Mertins P. Mani D.R. Ruggles K.V. Gillette M.A. Clauser K.R. Wang P. Wang X. Qiao J.W. Cao S. Petralia F. Kawaler E. Mundt F. Krug K. Tu Z. Lei J.T. Gatza M.L. Wilkerson M. Perou C.M. Yellapantula V. Huang K.-L. Lin C. McLellan M.D. Yan P. Davies S.R. Townsend R.R. Skates S.J. Wang J. Zhang B. Kinsinger C.R. Mesri M. Rodriguez H. Ding L. Paulovich A.G. Fenyö D. Ellis M.J. Carr S.A. NCI CPTAC Proteogenomics connects somatic mutations to signalling in breast cancer.Nature. 2016; 534: 55-62Crossref PubMed Scopus (978) Google Scholar). 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• W2946008612 date "2019-08-01" @default.
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• W2946008612 title "Exploiting Interdata Relationships in Next-generation Proteomics Analysis" @default.
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