1
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Ponomarenko EA, Krasnov GS, Kiseleva OI, Kryukova PA, Arzumanian VA, Dolgalev GV, Ilgisonis EV, Lisitsa AV, Poverennaya EV. Workability of mRNA Sequencing for Predicting Protein Abundance. Genes (Basel) 2023; 14:2065. [PMID: 38003008 PMCID: PMC10671741 DOI: 10.3390/genes14112065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome-proteome levels for different tissues does not exceed 0.3-0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction.
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Affiliation(s)
| | - George S. Krasnov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia;
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2
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van den Berg PR, Bérenger-Currias NMLP, Budnik B, Slavov N, Semrau S. Integration of a multi-omics stem cell differentiation dataset using a dynamical model. PLoS Genet 2023; 19:e1010744. [PMID: 37167320 DOI: 10.1371/journal.pgen.1010744] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 05/23/2023] [Accepted: 04/14/2023] [Indexed: 05/13/2023] Open
Abstract
Stem cell differentiation is a highly dynamic process involving pervasive changes in gene expression. The large majority of existing studies has characterized differentiation at the level of individual molecular profiles, such as the transcriptome or the proteome. To obtain a more comprehensive view, we measured protein, mRNA and microRNA abundance during retinoic acid-driven differentiation of mouse embryonic stem cells. We found that mRNA and protein abundance are typically only weakly correlated across time. To understand this finding, we developed a hierarchical dynamical model that allowed us to integrate all data sets. This model was able to explain mRNA-protein discordance for most genes and identified instances of potential microRNA-mediated regulation. Overexpression or depletion of microRNAs identified by the model, followed by RNA sequencing and protein quantification, were used to follow up on the predictions of the model. Overall, our study shows how multi-omics integration by a dynamical model could be used to nominate candidate regulators.
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Affiliation(s)
| | | | - Bogdan Budnik
- Mass Spectrometry and Proteomics Resource Laboratory, Harvard University, Cambridge, Massachusetts, United States of America
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America
| | - Stefan Semrau
- Leiden Institute of Physics, Leiden University, Leiden, Zuid-Holland, The Netherlands
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3
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Shrestha R, Llaurado Fernandez M, Dawson A, Hoenisch J, Volik S, Lin YY, Anderson S, Kim H, Haegert AM, Colborne S, Wong NKY, McConeghy B, Bell RH, Brahmbhatt S, Lee CH, DiMattia GE, Le Bihan S, Morin GB, Collins CC, Carey MS. Multiomics Characterization of Low-Grade Serous Ovarian Carcinoma Identifies Potential Biomarkers of MEK Inhibitor Sensitivity and Therapeutic Vulnerability. Cancer Res 2021; 81:1681-1694. [PMID: 33441310 DOI: 10.1158/0008-5472.can-20-2222] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 11/12/2020] [Accepted: 01/11/2021] [Indexed: 11/16/2022]
Abstract
Low-grade serous ovarian carcinoma (LGSOC) is a rare tumor subtype with high case fatality rates in patients with metastatic disease. There is a pressing need to develop effective treatments using newly available preclinical models for therapeutic discovery and drug evaluation. Here, we use multiomics integration of whole-exome sequencing, RNA sequencing, and mass spectrometry-based proteomics on 14 LGSOC cell lines to elucidate novel biomarkers and therapeutic vulnerabilities. Comparison of LGSOC cell line data with LGSOC tumor data enabled predictive biomarker identification of MEK inhibitor (MEKi) efficacy, with KRAS mutations found exclusively in MEKi-sensitive cell lines and NRAS mutations found mostly in MEKi-resistant cell lines. Distinct patterns of Catalogue of Somatic Mutations in Cancer mutational signatures were identified in MEKi-sensitive and MEKi-resistant cell lines. Deletions of CDKN2A/B and MTAP genes were more frequent in cell lines than tumor samples and possibly represent key driver events in the absence of KRAS/NRAS/BRAF mutations. These LGSOC cell lines were representative models of the molecular aberrations found in LGSOC tumors. For prediction of in vitro MEKi efficacy, proteomic data provided better discrimination than gene expression data. Condensin, minichromosome maintenance, and replication factor C protein complexes were identified as potential treatment targets in MEKi-resistant cell lines. This study suggests that CDKN2A/B or MTAP deficiency may be exploited using synthetically lethal treatment strategies, highlighting the importance of using proteomic data as a tool for molecular drug prediction. Multiomics approaches are crucial to improving our understanding of the molecular underpinnings of LGSOC and applying this information to develop new therapies. SIGNIFICANCE: These findings highlight the utility of global multiomics to characterize LGSOC cell lines as research models, to determine biomarkers of MEKi resistance, and to identify potential novel therapeutic targets.
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Affiliation(s)
- Raunak Shrestha
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada.,Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Radiation Oncology, University of California San Francisco, San Francisco, California
| | - Marta Llaurado Fernandez
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Amy Dawson
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Joshua Hoenisch
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stanislav Volik
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada
| | - Yen-Yi Lin
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada.,Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Shawn Anderson
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada
| | - Hannah Kim
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Anne M Haegert
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada
| | - Shane Colborne
- Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada
| | - Nelson K Y Wong
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Pathology & Laboratory Medicine, BC Cancer Agency, Vancouver, British Columbia, Canada
| | - Brian McConeghy
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada
| | - Robert H Bell
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada
| | | | - Cheng-Han Lee
- Department of Pathology & Laboratory Medicine, BC Cancer Agency, Vancouver, British Columbia, Canada
| | - Gabriel E DiMattia
- Translational Ovarian Cancer Research Program, London Health Science Centre, London, Ontario, Canada
| | | | - Gregg B Morin
- Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Colin C Collins
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada. .,Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Mark S Carey
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, British Columbia, Canada.
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4
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Ross AB, Langer JD, Jovanovic M. Proteome Turnover in the Spotlight: Approaches, Applications, and Perspectives. Mol Cell Proteomics 2020; 20:100016. [PMID: 33556866 PMCID: PMC7950106 DOI: 10.1074/mcp.r120.002190] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 11/25/2020] [Accepted: 11/30/2020] [Indexed: 01/17/2023] Open
Abstract
In all cells, proteins are continuously synthesized and degraded to maintain protein homeostasis and modify gene expression levels in response to stimuli. Collectively, the processes of protein synthesis and degradation are referred to as protein turnover. At a steady state, protein turnover is constant to maintain protein homeostasis, but in dynamic responses, proteins change their rates of synthesis and degradation to adjust their proteomes to internal or external stimuli. Thus, probing the kinetics and dynamics of protein turnover lends insight into how cells regulate essential processes such as growth, differentiation, and stress response. Here, we outline historical and current approaches to measuring the kinetics of protein turnover on a proteome-wide scale in both steady-state and dynamic systems, with an emphasis on metabolic tracing using stable isotope-labeled amino acids. We highlight important considerations for designing proteome turnover experiments, key biological findings regarding the conserved principles of proteome turnover regulation, and future perspectives for both technological and biological investigation.
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Affiliation(s)
- Alison Barbara Ross
- Department of Biological Sciences, Columbia University, New York, New York, USA
| | - Julian David Langer
- Proteomics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany; Proteomics, Max Planck Institute for Brain Research, Frankfurt am Main, Germany.
| | - Marko Jovanovic
- Department of Biological Sciences, Columbia University, New York, New York, USA.
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5
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Integration of absolute multi-omics reveals dynamic protein-to-RNA ratios and metabolic interplay within mixed-domain microbiomes. Nat Commun 2020; 11:4708. [PMID: 32948758 PMCID: PMC7501288 DOI: 10.1038/s41467-020-18543-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 08/28/2020] [Indexed: 11/08/2022] Open
Abstract
While the field of microbiology has adapted to the study of complex microbiomes via modern meta-omics techniques, we have not updated our basic knowledge regarding the quantitative levels of DNA, RNA and protein molecules within a microbial cell, which ultimately control cellular function. Here we report the temporal measurements of absolute RNA and protein levels per gene within a mixed bacterial-archaeal consortium. Our analysis of this data reveals an absolute protein-to-RNA ratio of 102–104 for bacterial populations and 103–105 for an archaeon, which is more comparable to Eukaryotic representatives’ humans and yeast. Furthermore, we use the linearity between the metaproteome and metatranscriptome over time to identify core functional guilds, hence using a fundamental biological feature (i.e., RNA/protein levels) to highlight phenotypical complementarity. Our findings show that upgrading multi-omic toolkits with traditional absolute measurements unlocks the scaling of core biological questions to dynamic and complex microbiomes, creating a deeper insight into inter-organismal relationships that drive the greater community function. Here, the authors perform a temporal multi-omic analysis of a minimalistic cellulose-degrading and methane-producing consortium at the strain level and estimate protein-to-RNA ratios and RNA-protein dynamics of the community simultaneously over time.
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6
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Ho JJD, Balukoff NC, Theodoridis PR, Wang M, Krieger JR, Schatz JH, Lee S. A network of RNA-binding proteins controls translation efficiency to activate anaerobic metabolism. Nat Commun 2020; 11:2677. [PMID: 32472050 PMCID: PMC7260222 DOI: 10.1038/s41467-020-16504-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 04/30/2020] [Indexed: 01/30/2023] Open
Abstract
Protein expression evolves under greater evolutionary constraint than mRNA levels, and translation efficiency represents a primary determinant of protein levels during stimuli adaptation. This raises the question as to the translatome remodelers that titrate protein output from mRNA populations. Here, we uncover a network of RNA-binding proteins (RBPs) that enhances the translation efficiency of glycolytic proteins in cells responding to oxygen deprivation. A system-wide proteomic survey of translational engagement identifies a family of oxygen-regulated RBPs that functions as a switch of glycolytic intensity. Tandem mass tag-pulse SILAC (TMT-pSILAC) and RNA sequencing reveals that each RBP controls a unique but overlapping portfolio of hypoxic responsive proteins. These RBPs collaborate with the hypoxic protein synthesis apparatus, operating as a translation efficiency checkpoint that integrates upstream mRNA signals to activate anaerobic metabolism. This system allows anoxia-resistant animals and mammalian cells to initiate anaerobic glycolysis and survive hypoxia. We suggest that an oxygen-sensitive RBP cluster controls anaerobic metabolism to confer hypoxia tolerance. mRNA translation efficiency is regulated in response to stimuli. Here the authors employ mass spectrometry analysis of ribosome fractions and show that under hypoxia, oxygen-sensitive RNA binding proteins enhance the translation efficiency of glycolysis pathway transcripts.
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Affiliation(s)
- J J David Ho
- Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.,Division of Hematology, Department of Medicine, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA
| | - Nathan C Balukoff
- Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA
| | - Phaedra R Theodoridis
- Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA
| | - Miling Wang
- Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA
| | - Jonathan R Krieger
- The SickKids Proteomics, Analytics, Robotics & Chemical Biology Centre (SPARC Biocentre), The Hospital for Sick Children, Toronto, ON, M5G 1X8, Canada.,Bioinformatics Solutions Inc., Waterloo, ON, N2L 6J2, Canada
| | - Jonathan H Schatz
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.,Division of Hematology, Department of Medicine, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA
| | - Stephen Lee
- Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA. .,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA. .,Department of Urology, Miller School of Medicine, University of Miami, Miami, 33136, USA.
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7
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Becker K, Bluhm A, Casas-Vila N, Dinges N, Dejung M, Sayols S, Kreutz C, Roignant JY, Butter F, Legewie S. Quantifying post-transcriptional regulation in the development of Drosophila melanogaster. Nat Commun 2018; 9:4970. [PMID: 30478415 PMCID: PMC6255845 DOI: 10.1038/s41467-018-07455-9] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 10/30/2018] [Indexed: 11/10/2022] Open
Abstract
Even though proteins are produced from mRNA, the correlation between mRNA levels and protein abundances is moderate in most studies, occasionally attributed to complex post-transcriptional regulation. To address this, we generate a paired transcriptome/proteome time course dataset with 14 time points during Drosophila embryogenesis. Despite a limited mRNA-protein correlation (ρ = 0.54), mathematical models describing protein translation and degradation explain 84% of protein time-courses based on the measured mRNA dynamics without assuming complex post transcriptional regulation, and allow for classification of most proteins into four distinct regulatory scenarios. By performing an in-depth characterization of the putatively post-transcriptionally regulated genes, we postulate that the RNA-binding protein Hrb98DE is involved in post-transcriptional control of sugar metabolism in early embryogenesis and partially validate this hypothesis using Hrb98DE knockdown. In summary, we present a systems biology framework for the identification of post-transcriptional gene regulation from large-scale, time-resolved transcriptome and proteome data.
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Affiliation(s)
- Kolja Becker
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany
| | - Alina Bluhm
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany
| | - Nuria Casas-Vila
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany
| | - Nadja Dinges
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany
| | - Mario Dejung
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany
| | - Sergi Sayols
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany
| | - Clemens Kreutz
- Center for Biosystems Analysis (ZBSA), University of Freiburg, Habsburger Str. 49, 79104, Freiburg, Germany
| | - Jean-Yves Roignant
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany
| | - Falk Butter
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany.
| | - Stefan Legewie
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128, Mainz, Germany.
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8
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Rendleman J, Cheng Z, Maity S, Kastelic N, Munschauer M, Allgoewer K, Teo G, Zhang YBM, 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; 7:39054. [PMID: 30272558 PMCID: PMC6185107 DOI: 10.7554/elife.39054] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 09/28/2018] [Indexed: 12/13/2022] Open
Abstract
Maintaining a healthy proteome involves all layers of gene expression regulation. By quantifying temporal changes of the transcriptome, translatome, proteome, and RNA-protein interactome in cervical cancer cells, we systematically characterize the molecular landscape in response to proteostatic challenges. We identify shared and specific responses to misfolded proteins and to oxidative stress, two conditions that are tightly linked. We reveal new aspects of the unfolded protein response, including many genes that escape global translation shutdown. A subset of these genes supports rerouting of energy production in the mitochondria. We also find that many genes change at multiple levels, in either the same or opposing directions, and at different time points. We highlight a variety of putative regulatory pathways, including the stress-dependent alternative splicing of aminoacyl-tRNA synthetases, and protein-RNA binding within the 3’ untranslated region of molecular chaperones. These results illustrate the potential of this information-rich resource.
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Affiliation(s)
- Justin Rendleman
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
| | - Zhe Cheng
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
| | - Shuvadeep Maity
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
| | - Nicolai Kastelic
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Mathias Munschauer
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Kristina Allgoewer
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
| | - Guoshou Teo
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
| | - Yun Bin Matteo Zhang
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
| | - Amy Lei
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
| | - Brian Parker
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
| | - Markus Landthaler
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.,Integrative Research Institute for the Life Sciences, Institute of Biology, Humboldt University, Berlin, Germany
| | | | | | - Hyungwon Choi
- National University of Singapore, Singapore.,Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore
| | - Christine Vogel
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
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9
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Sinitcyn P, Rudolph JD, Cox J. Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data. Annu Rev Biomed Data Sci 2018. [DOI: 10.1146/annurev-biodatasci-080917-013516] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry–based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.
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Affiliation(s)
- Pavel Sinitcyn
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Jan Daniel Rudolph
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Jürgen Cox
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
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10
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Morimoto S, Yahara K. Identification of stress responsive genes by studying specific relationships between mRNA and protein abundance. Heliyon 2018; 4:e00558. [PMID: 29560469 PMCID: PMC5857721 DOI: 10.1016/j.heliyon.2018.e00558] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 02/03/2018] [Accepted: 02/23/2018] [Indexed: 11/26/2022] Open
Abstract
Protein expression is regulated by the production and degradation of mRNAs and proteins but the specifics of their relationship are controversial. Although technological advances have enabled genome-wide and time-series surveys of mRNA and protein abundance, recent studies have shown paradoxical results, with most statistical analyses being limited to linear correlation, or analysis of variance applied separately to mRNA and protein datasets. Here, using recently analyzed genome-wide time-series data, we have developed a statistical analysis framework for identifying which types of genes or biological gene groups have significant correlation between mRNA and protein abundance after accounting for potential time delays. Our framework stratifies all genes in terms of the extent of time delay, conducts gene clustering in each stratum, and performs a non-parametric statistical test of the correlation between mRNA and protein abundance in a gene cluster. Consequently, we revealed stronger correlations than previously reported between mRNA and protein abundance in two metabolic pathways. Moreover, we identified a pair of stress responsive genes (ADC17 and KIN1) that showed a highly similar time series of mRNA and protein abundance. Furthermore, we confirmed robustness of the analysis framework by applying it to another genome-wide time-series data and identifying a cytoskeleton-related gene cluster (keratin 18, keratin 17, and mitotic spindle positioning) that shows similar correlation. The significant correlation and highly similar changes of mRNA and protein abundance suggests a concerted role of these genes in cellular stress response, which we consider provides an answer to the question of the specific relationships between mRNA and protein in a cell. In addition, our framework for studying the relationship between mRNAs and proteins in a cell will provide a basis for studying specific relationships between mRNA and protein abundance after accounting for potential time delays.
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Affiliation(s)
- Shimpei Morimoto
- Division of Biostatistics, Kurume University School of Medicine, Fukuoka, Japan
| | - Koji Yahara
- Antimicrobial Resistance Research Center, National Institute of Infectious Diseases, Tokyo, Japan
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11
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PECAplus: statistical analysis of time-dependent regulatory changes in dynamic single-omics and dual-omics experiments. NPJ Syst Biol Appl 2017; 4:3. [PMID: 29263799 PMCID: PMC5736550 DOI: 10.1038/s41540-017-0040-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 11/19/2017] [Accepted: 11/24/2017] [Indexed: 11/12/2022] Open
Abstract
Simultaneous dynamic profiling of mRNA and protein expression is increasingly popular, and there is a critical need for algorithms to identify regulatory layers and time dependency of gene expression. A group of scientists from United States and Singapore present PECAplus, a comprehensive set of statistical analysis tools to address this challenge. Protein expression control analysis (PECA) computes the probability scores for change in mRNA and protein-level regulatory parameters at each time point, deconvoluting gene expression regulation in the presence of measurement noise. PECAplus adapted PECA’s mass action model to a variety of proteomic data including pulsed SILAC and generic protein expression data. It also features analysis modules to fit smooth curves on rugged time series observations, and to facilitate time-dependent interpretation of the data for genes and biological functions. They demonstrate the core modules with two time course datasets of mammalian cells responding to unfolded proteins and pathogens.
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12
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El-Kassas HY, Ghobrial MG. Biosynthesis of metal nanoparticles using three marine plant species: anti-algal efficiencies against "Oscillatoria simplicissima". ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2017; 24:7837-7849. [PMID: 28132190 DOI: 10.1007/s11356-017-8362-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 01/02/2017] [Indexed: 06/06/2023]
Abstract
This study aims at controlling of the cyanobacteria Oscillatoria simplicissima, those that produce neurotoxins and have negative impacts on the aquatic organisms, using biosynthesized metal nanoparticles (NPs). Silver-NPs (Ag-NPs) have been successfully biosynthesized using Nannochloropsis oculata and Tetraselmis tetrathele cultures. Also, Ag-NPs and iron oxide-NPs (Fe3O4-NPs) were synthesized by Halophila stipulacea aqueous extract. The structural composition of the different biosynthesized NPs was studied. The algae cultures and the extract were used as reductants of AgNO3, and brown colors due to Ag-NP biosynthesis were observed. Silver signals were recorded in their corresponding EDX spectra. FTIR analyses showed that proteins in N. oculata and T. tetrathele cultures reduced AgNO3, and aromatic compounds stabilized the biogenic Ag-NPs. H. stipulacea extract contains proteins and polyphenols that could be in charge for the reduction of silver and iron ions into nanoparticles and polysaccharides which stabilized the biosynthesized Ag-NPs and Fe3O4-NPs. The Ag-NPs biosynthesized by T. tetrathele cultures and H. stipulacea aqueous extract exerted outstanding negative impacts on O. simplicissima (optical density and total chlorophyll) and the Ag-NPs biosynthesized using N. oculata culture exerted the moderate performance. The study results suggest that the bioactive compounds present in the FTIR profiles of the Ag-NPs and or ionic silver may be the main contributors in their anti-algal effects. A trial to use the biosynthesized Fe3O4-NPs using H. stipulacea aqueous extract to separate Ag-NPs was successfully carried out. Since the synthesis and applications of nanomaterials is a hot subject of research, the study outcomes not only provide a green approach for the synthesis of metal-NPs but also open the way for more nanoparticle applications.
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Affiliation(s)
- Hala Y El-Kassas
- Hydrobiology Department, Marine Environment Division, National Institute of Oceanography and Fisheries (NIOF), Alexandria, Egypt.
| | - Mary G Ghobrial
- Hydrobiology Department, Marine Environment Division, National Institute of Oceanography and Fisheries (NIOF), Alexandria, Egypt
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Liu Y, Beyer A, Aebersold R. On the Dependency of Cellular Protein Levels on mRNA Abundance. Cell 2016; 165:535-50. [PMID: 27104977 DOI: 10.1016/j.cell.2016.03.014] [Citation(s) in RCA: 1762] [Impact Index Per Article: 220.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Indexed: 12/30/2022]
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.
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Affiliation(s)
- Yansheng Liu
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Andreas Beyer
- Cellular Networks and Systems Biology, University of Cologne, CECAD, Joseph-Stelzmann-Strasse 26, Cologne 50931, Germany.
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; Faculty of Science, University of Zurich, 8057 Zurich, Switzerland.
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Cheng Z, Teo G, Krueger S, Rock TM, Koh HWL, Choi H, Vogel C. Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress. Mol Syst Biol 2016; 12:855. [PMID: 26792871 PMCID: PMC4731011 DOI: 10.15252/msb.20156423] [Citation(s) in RCA: 124] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The relative importance of regulation at the mRNA versus protein level is subject to ongoing debate. To address this question in a dynamic system, we mapped proteomic and transcriptomic changes in mammalian cells responding to stress induced by dithiothreitol over 30 h. Specifically, we estimated the kinetic parameters for the synthesis and degradation of RNA and proteins, and deconvoluted the response patterns into common and unique to each regulatory level using a new statistical tool. Overall, the two regulatory levels were equally important, but differed in their impact on molecule concentrations. Both mRNA and protein changes peaked between two and eight hours, but mRNA expression fold changes were much smaller than those of the proteins. mRNA concentrations shifted in a transient, pulse‐like pattern and returned to values close to pre‐treatment levels by the end of the experiment. In contrast, protein concentrations switched only once and established a new steady state, consistent with the dominant role of protein regulation during misfolding stress. Finally, we generated hypotheses on specific regulatory modes for some genes.
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Affiliation(s)
- Zhe Cheng
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Guoshou Teo
- Saw Swee Hock School of Public Health, National University Singapore, Singapore National University Health System, Singapore
| | - Sabrina Krueger
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Tara M Rock
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Hiromi W L Koh
- Saw Swee Hock School of Public Health, National University Singapore, Singapore National University Health System, Singapore
| | - Hyungwon Choi
- Saw Swee Hock School of Public Health, National University Singapore, Singapore National University Health System, Singapore
| | - Christine Vogel
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
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Liu Y, Aebersold R. The interdependence of transcript and protein abundance: new data--new complexities. Mol Syst Biol 2016; 12:856. [PMID: 26792872 PMCID: PMC4731012 DOI: 10.15252/msb.20156720] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The relative contribution of transcriptional and translational regulation in gene expression control has been intensely debated and remains a challenging question. Recent reports have suggested that protein abundance in mammalian cells is primarily controlled at the transcript‐level. In their recent work, Cheng et al (2016) determined the proteomic and transcriptomic changes in cells responding to endoplasmic reticulum (ER) stress. Their analyses indicate that the ER stress response is significantly controlled at both the transcript and protein levels.
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Affiliation(s)
- Yansheng Liu
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Faculty of Science, University of Zurich, Zurich, Switzerland
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McManus J, Cheng Z, Vogel C. Next-generation analysis of gene expression regulation--comparing the roles of synthesis and degradation. MOLECULAR BIOSYSTEMS 2015; 11:2680-9. [PMID: 26259698 PMCID: PMC4573910 DOI: 10.1039/c5mb00310e] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Technological advances now enable routine measurement of mRNA and protein abundances, and estimates of their rates of synthesis and degradation that inform on their values and the degree of change in response to stimuli. Importantly, more and more data on time-series experiments are emerging, e.g. of cells responding to stress, enabling first insights into a new dimension of gene expression regulation - its dynamics and how it allows for very different response signals across genes. This review discusses recently published methods and datasets, their impact on what we now know about the relationships between concentrations and synthesis rates of mRNAs and proteins in yeast and mammalian cells, their evolution, and new hypotheses on translation regulatory mechanisms generated by approaches that involve ribosome footprinting.
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Affiliation(s)
- Joel McManus
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
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