1
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Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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2
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Khan S, Conover R, Asthagiri AR, Slavov N. Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition. J Proteome Res 2024. [PMID: 38663020 DOI: 10.1021/acs.jproteome.4c00277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Physiological processes, such as the epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within a cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in the cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism, and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and, thus, reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offers a window into protein regulation during physiological transitions.
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Affiliation(s)
- Saad Khan
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Rachel Conover
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Anand R Asthagiri
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel Squared Technology Institute, Watertown, Massachusetts 02472, United States
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3
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O'Neill JR, Yébenes Mayordomo M, Mitulović G, Al Shboul S, Bedran G, Faktor J, Hernychova L, Uhrik L, Gómez-Herranz M, Kocikowski M, Save V, Vojtěšek B, Arends MJ, Hupp T, Alfaro JA. Multi-Omic Analysis of Esophageal Adenocarcinoma Uncovers Candidate Therapeutic Targets and Cancer-Selective Posttranscriptional Regulation. Mol Cell Proteomics 2024; 23:100764. [PMID: 38604503 DOI: 10.1016/j.mcpro.2024.100764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 03/08/2024] [Accepted: 04/08/2024] [Indexed: 04/13/2024] Open
Abstract
Efforts to address the poor prognosis associated with esophageal adenocarcinoma (EAC) have been hampered by a lack of biomarkers to identify early disease and therapeutic targets. Despite extensive efforts to understand the somatic mutations associated with EAC over the past decade, a gap remains in understanding how the atlas of genomic aberrations in this cancer impacts the proteome and which somatic variants are of importance for the disease phenotype. We performed a quantitative proteomic analysis of 23 EACs and matched adjacent normal esophageal and gastric tissues. We explored the correlation of transcript and protein abundance using tissue-matched RNA-seq and proteomic data from seven patients and further integrated these data with a cohort of EAC RNA-seq data (n = 264 patients), EAC whole-genome sequencing (n = 454 patients), and external published datasets. We quantified protein expression from 5879 genes in EAC and patient-matched normal tissues. Several biomarker candidates with EAC-selective expression were identified, including the transmembrane protein GPA33. We further verified the EAC-enriched expression of GPA33 in an external cohort of 115 patients and confirm this as an attractive diagnostic and therapeutic target. To further extend the insights gained from our proteomic data, an integrated analysis of protein and RNA expression in EAC and normal tissues revealed several genes with poorly correlated protein and RNA abundance, suggesting posttranscriptional regulation of protein expression. These outlier genes, including SLC25A30, TAOK2, and AGMAT, only rarely demonstrated somatic mutation, suggesting post-transcriptional drivers for this EAC-specific phenotype. AGMAT was demonstrated to be overexpressed at the protein level in EAC compared to adjacent normal tissues with an EAC-selective, post-transcriptional mechanism of regulation of protein abundance proposed. Integrated analysis of proteome, transcriptome, and genome in EAC has revealed several genes with tumor-selective, posttranscriptional regulation of protein expression, which may be an exploitable vulnerability.
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Affiliation(s)
- J Robert O'Neill
- Cambridge Oesophagogastric Centre, Addenbrooke's Hospital, Cambridge, United Kingdom; Institute of Genetics and Cancer (IGC), University of Edinburgh, Edinburgh, Scotland.
| | - Marcos Yébenes Mayordomo
- Institute of Genetics and Cancer (IGC), University of Edinburgh, Edinburgh, Scotland; International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland.
| | - Goran Mitulović
- Clinical Department of Laboratory Medicine Proteomics Core Facility, Medical University Vienna, Vienna, Austria; Bruker Austria, Wien, Austria
| | - Sofian Al Shboul
- Department of Pharmacology and Public Health, Faculty of Medicine, The Hashemite University, Zarqa, Jordan
| | - Georges Bedran
- International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland
| | - Jakub Faktor
- International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland
| | - Lenka Hernychova
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Lukas Uhrik
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Maria Gómez-Herranz
- International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland
| | - Mikołaj Kocikowski
- International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland
| | - Vicki Save
- Department of Pathology, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - Bořivoj Vojtěšek
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Mark J Arends
- Edinburgh Pathology, Institute of Genetics and Cancer (IGC), University of Edinburgh, Edinburgh, Scotland
| | - Ted Hupp
- Institute of Genetics and Cancer (IGC), University of Edinburgh, Edinburgh, Scotland; International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland
| | - Javier Antonio Alfaro
- International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland; Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK; International Centre for Cancer Vaccine Science, University of Gdańsk, Gdańsk, Poland; Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada; The Canadian Association for Responsible AI in Medicine, Victoria, BC, Canada.
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4
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Khan S, Conover R, Asthagiri AR, Slavov N. Dynamics of single-cell protein covariation during epithelial-mesenchymal transition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.21.572913. [PMID: 38187715 PMCID: PMC10769332 DOI: 10.1101/2023.12.21.572913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Physiological processes, such as epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and thus reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offer a window into protein regulation during physiological transitions.
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Affiliation(s)
- Saad Khan
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
| | - Rachel Conover
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Anand R. Asthagiri
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
- Department of Chemical Engineering, Northeastern University, Boston, MA, USA
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
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5
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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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6
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Leduc A, Harens H, Slavov N. Modeling and interpretation of single-cell proteogenomic data. ARXIV 2023:arXiv:2308.07465v2. [PMID: 37645043 PMCID: PMC10462161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Biological functions stem from coordinated interactions among proteins, nucleic acids and small molecules. Mass spectrometry technologies for reliable, high throughput single-cell proteomics will add a new modality to genomics and enable data-driven modeling of the molecular mechanisms coordinating proteins and nucleic acids at single-cell resolution. This promising potential requires estimating the reliability of measurements and computational analysis so that models can distinguish biological regulation from technical artifacts. We highlight different measurement modes that can support single-cell proteogenomic analysis and how to estimate their reliability. We then discuss approaches for developing both abstract and mechanistic models that aim to biologically interpret the measured differences across modalities, including specific applications to directed stem cell differentiation and to inferring protein interactions in cancer cells from the buffing of DNA copy-number variations. Single-cell proteogenomic data will support mechanistic models of direct molecular interactions that will provide generalizable and predictive representations of biological systems.
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Affiliation(s)
- Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115, USA
| | - Hannah Harens
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
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7
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Moura Ferreira MAD, Wendering P, Arend M, Batista da Silveira W, Nikoloski Z. Accurate prediction of in vivo protein abundances by coupling constraint-based modelling and machine learning. Metab Eng 2023; 80:184-192. [PMID: 37802292 DOI: 10.1016/j.ymben.2023.09.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/10/2023] [Accepted: 09/25/2023] [Indexed: 10/08/2023]
Abstract
Quantification of how different environmental cues affect protein allocation can provide important insights for understanding cell physiology. While absolute quantification of proteins can be obtained by resource-intensive mass-spectrometry-based technologies, prediction of protein abundances offers another way to obtain insights into protein allocation. Here we present CAMEL, a framework that couples constraint-based modelling with machine learning to predict protein abundance for any environmental condition. This is achieved by building machine learning models that leverage static features, derived from protein sequences, and condition-dependent features predicted from protein-constrained metabolic models. Our findings demonstrate that CAMEL results in excellent prediction of protein allocation in E. coli (average Pearson correlation of at least 0.9), and moderate performance in S. cerevisiae (average Pearson correlation of at least 0.5). Therefore, CAMEL outperformed contending approaches without using molecular read-outs from unseen conditions and provides a valuable tool for using protein allocation in biotechnological applications.
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Affiliation(s)
| | - Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, 14476, Germany; Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
| | - Marius Arend
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, 14476, Germany; Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
| | | | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, 14476, Germany; Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany.
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8
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Sweatt AJ, Griffiths CD, Paudel BB, Janes KA. Proteome-wide copy-number estimation from transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.548432. [PMID: 37503057 PMCID: PMC10369941 DOI: 10.1101/2023.07.10.548432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Protein copy numbers constrain systems-level properties of regulatory networks, but absolute proteomic data remain scarce compared to transcriptomics obtained by RNA sequencing. We addressed this persistent gap by relating mRNA to protein statistically using best-available data from quantitative proteomics-transcriptomics for 4366 genes in 369 cell lines. The approach starts with a central estimate of protein copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model that links mRNAs to protein. For dozens of independent cell lines and primary prostate samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, and empirical protein-to-mRNA ratios. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein interaction complexes, suggesting mechanistic relationships are embedded. We use the method to estimate viral-receptor abundances of CD55-CXADR from human heart transcriptomes and build 1489 systems-biology models of coxsackievirus B3 infection susceptibility. When applied to 796 RNA sequencing profiles of breast cancer from The Cancer Genome Atlas, inferred copy-number estimates collectively reclassify 26% of Luminal A and 29% of Luminal B tumors. Protein-based reassignments strongly involve a pharmacologic target for luminal breast cancer (CDK4) and an α-catenin that is often undetectable at the mRNA level (CTTNA2). Thus, by adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility limits of contemporary proteomics. The collection of gene-specific models is assembled as a web tool for users seeking mRNA-guided predictions of absolute protein abundance (http://janeslab.shinyapps.io/Pinferna).
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Affiliation(s)
- Andrew J. Sweatt
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908
| | - Cameron D. Griffiths
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908
| | - B. Bishal Paudel
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908
| | - Kevin A. Janes
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, 22908
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Filippenkov IB, Remizova JA, Stavchansky VV, Denisova AE, Gubsky LV, Myasoedov NF, Limborska SA, Dergunova LV. Synthetic Adrenocorticotropic Peptides Modulate the Expression Pattern of Immune Genes in Rat Brain following the Early Post-Stroke Period. Genes (Basel) 2023; 14:1382. [PMID: 37510287 PMCID: PMC10379992 DOI: 10.3390/genes14071382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 06/25/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
Ischemic stroke is an acute local decrease in cerebral blood flow due to a thrombus or embolus. Of particular importance is the study of the genetic systems that determine the mechanisms underlying the formation and maintenance of a therapeutic window (a time interval of up to 6 h after a stroke) when effective treatment can be provided. Here, we used a transient middle cerebral artery occlusion (tMCAO) model in rats to study two synthetic derivatives of adrenocorticotropic hormone (ACTH). The first was ACTH(4-7)PGP, which is known as Semax. It is actively used as a neuroprotective drug. The second was the ACTH(6-9)PGP peptide, which is elucidated as a prospective agent only. Using RNA-Seq analysis, we revealed hundreds of ischemia-related differentially expressed genes (DEGs), as well as 131 and 322 DEGs related to the first and second peptide at 4.5 h after tMCAO, respectively, in dorsolateral areas of the frontal cortex of rats. Furthermore, we showed that both Semax and ACTH(6-9)PGP can partially prevent changes in the immune- and neurosignaling-related gene expression profiles disturbed by the action of ischemia at 4.5 h after tMCAO. However, their different actions with regard to predominantly immune-related genes were also revealed. This study gives insight into how the transcriptome depends on the variation in the structure of the related peptides, and it is valuable from the standpoint of the development of measures for early post-stroke therapy.
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Affiliation(s)
- Ivan B Filippenkov
- Institute of Molecular Genetics of National Research Center "Kurchatov Institute", Kurchatov Sq. 2, Moscow 123182, Russia
| | - Julia A Remizova
- Institute of Molecular Genetics of National Research Center "Kurchatov Institute", Kurchatov Sq. 2, Moscow 123182, Russia
| | - Vasily V Stavchansky
- Institute of Molecular Genetics of National Research Center "Kurchatov Institute", Kurchatov Sq. 2, Moscow 123182, Russia
| | - Alina E Denisova
- Department of Neurology, Neurosurgery and Medical Genetics, Pirogov Russian National Research Medical University, Ostrovitianov Str. 1, Moscow 117997, Russia
| | - Leonid V Gubsky
- Department of Neurology, Neurosurgery and Medical Genetics, Pirogov Russian National Research Medical University, Ostrovitianov Str. 1, Moscow 117997, Russia
- Federal Center for the Brain and Neurotechnologies, Federal Biomedical Agency, Ostrovitianov Str. 1, Building 10, Moscow 117997, Russia
| | - Nikolay F Myasoedov
- Institute of Molecular Genetics of National Research Center "Kurchatov Institute", Kurchatov Sq. 2, Moscow 123182, Russia
| | - Svetlana A Limborska
- Institute of Molecular Genetics of National Research Center "Kurchatov Institute", Kurchatov Sq. 2, Moscow 123182, Russia
| | - Lyudmila V Dergunova
- Institute of Molecular Genetics of National Research Center "Kurchatov Institute", Kurchatov Sq. 2, Moscow 123182, Russia
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10
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Qian SH, Xiong YL, Chen L, Geng YJ, Tang XM, Chen ZX. Dynamic Spatial-temporal Expression Ratio of X Chromosome to Autosomes but Stable Dosage Compensation in Mammals. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:589-600. [PMID: 36031057 PMCID: PMC10787176 DOI: 10.1016/j.gpb.2022.08.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 08/03/2022] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
In the evolutionary model of dosage compensation, per-allele expression level of the X chromosome has been proposed to have twofold up-regulation to compensate its dose reduction in males (XY) compared to females (XX). However, the expression regulation of X-linked genes is still controversial, and comprehensive evaluations are still lacking. By integrating multi-omics datasets in mammals, we investigated the expression ratios including X to autosomes (X:AA ratio) and X to orthologs (X:XX ratio) at the transcriptome, translatome, and proteome levels. We revealed a dynamic spatial-temporal X:AA ratio during development in humans and mice. Meanwhile, by tracing the evolution of orthologous gene expression in chickens, platypuses, and opossums, we found a stable expression ratio of X-linked genes in humans to their autosomal orthologs in other species (X:XX ≈ 1) across tissues and developmental stages, demonstrating stable dosage compensation in mammals. We also found that different epigenetic regulations contributed to the high tissue specificity and stage specificity of X-linked gene expression, thus affecting X:AA ratios. It could be concluded that the dynamics of X:AA ratios were attributed to the different gene contents and expression preferences of the X chromosome, rather than the stable dosage compensation.
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Affiliation(s)
- Sheng Hu Qian
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yu-Li Xiong
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Lu Chen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ying-Jie Geng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiao-Man Tang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Zhen-Xia Chen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China; Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan 430070, China.
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11
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Karollus A, Mauermeier T, Gagneur J. Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers. Genome Biol 2023; 24:56. [PMID: 36973806 PMCID: PMC10045630 DOI: 10.1186/s13059-023-02899-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 03/16/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND The largest sequence-based models of transcription control to date are obtained by predicting genome-wide gene regulatory assays across the human genome. This setting is fundamentally correlative, as those models are exposed during training solely to the sequence variation between human genes that arose through evolution, questioning the extent to which those models capture genuine causal signals. RESULTS Here we confront predictions of state-of-the-art models of transcription regulation against data from two large-scale observational studies and five deep perturbation assays. The most advanced of these sequence-based models, Enformer, by and large, captures causal determinants of human promoters. However, models fail to capture the causal effects of enhancers on expression, notably in medium to long distances and particularly for highly expressed promoters. More generally, the predicted impact of distal elements on gene expression predictions is small and the ability to correctly integrate long-range information is significantly more limited than the receptive fields of the models suggest. This is likely caused by the escalating class imbalance between actual and candidate regulatory elements as distance increases. CONCLUSIONS Our results suggest that sequence-based models have advanced to the point that in silico study of promoter regions and promoter variants can provide meaningful insights and we provide practical guidance on how to use them. Moreover, we foresee that it will require significantly more and particularly new kinds of data to train models accurately accounting for distal elements.
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Affiliation(s)
- Alexander Karollus
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
| | - Thomas Mauermeier
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany.
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- Munich Data Science Institute, Technical University of Munich, Garching, Germany.
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12
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Blaser MC, Kraler S, Lüscher TF, Aikawa E. Network-Guided Multiomic Mapping of Aortic Valve Calcification. Arterioscler Thromb Vasc Biol 2023; 43:417-426. [PMID: 36727519 PMCID: PMC9975082 DOI: 10.1161/atvbaha.122.318334] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/18/2023] [Indexed: 02/03/2023]
Abstract
Despite devastating clinical sequelae of calcific aortic valve disease that range from left ventricular remodeling to arrhythmias, heart failure, and early death, the molecular insights into disease initiation and progression are limited and pharmacotherapies remain unavailable. The pathobiology of calcific aortic valve disease is complex and comprehensive studies are challenging valvular calcification is heterogeneous and occurs preferentially on the aortic surface, along a fibrocalcific spectrum. Here, we review efforts to study (epi-)genomic, transcriptomic, proteomic, and metabolomic aspects of aortic valve calcification in combination with network medicine-/systems biology-based strategies to integrate multilayered omics datasets and prioritize druggable targets for experimental validation studies. Ultimately, such holistic approach efforts may open therapeutic avenues that go beyond invasive and costly valve replacement therapy.
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Affiliation(s)
- Mark C. Blaser
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon Kraler
- Center for Molecular Cardiology, University of Zurich, Schlieren, CH
| | - Thomas F. Lüscher
- Center for Molecular Cardiology, University of Zurich, Schlieren, CH
- Heart Division, Royal Brompton & Harefield Hospitals, London, UK
- National Heart and Lung Institute, Imperial College, London, UK
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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13
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Hernandez-Alias X, Benisty H, Radusky LG, Serrano L, Schaefer MH. Using protein-per-mRNA differences among human tissues in codon optimization. Genome Biol 2023; 24:34. [PMID: 36829202 PMCID: PMC9951436 DOI: 10.1186/s13059-023-02868-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 02/07/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Codon usage and nucleotide composition of coding sequences have profound effects on protein expression. However, while it is recognized that different tissues have distinct tRNA profiles and codon usages in their transcriptomes, the effect of tissue-specific codon optimality on protein synthesis remains elusive. RESULTS We leverage existing state-of-the-art transcriptomics and proteomics datasets from the GTEx project and the Human Protein Atlas to compute the protein-to-mRNA ratios of 36 human tissues. Using this as a proxy of translational efficiency, we build a machine learning model that identifies codons enriched or depleted in specific tissues. We detect two clusters of tissues with an opposite pattern of codon preferences. We then use these identified patterns for the development of CUSTOM, a codon optimizer algorithm which suggests a synonymous codon design in order to optimize protein production in a tissue-specific manner. In human cell-line models, we provide evidence that codon optimization should take into account particularities of the translational machinery of the tissues in which the target proteins are expressed and that our approach can design genes with tissue-optimized expression profiles. CONCLUSIONS We provide proof-of-concept evidence that codon preferences exist in tissue-specific protein synthesis and demonstrate its application to synthetic gene design. We show that CUSTOM can be of benefit in biological and biotechnological applications, such as in the design of tissue-targeted therapies and vaccines.
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Affiliation(s)
- Xavier Hernandez-Alias
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003, Barcelona, Spain.
| | - Hannah Benisty
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Leandro G Radusky
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Luis Serrano
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003, Barcelona, Spain. .,Universitat Pompeu Fabra (UPF), 08002, Barcelona, Spain. .,ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain.
| | - Martin H Schaefer
- IEO European Institute of Oncology IRCCS, Department of Experimental Oncology, Via Adamello 16, 20139, Milan, Italy.
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14
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Dave P, Roth G, Griesbach E, Mateju D, Hochstoeger T, Chao JA. Single-molecule imaging reveals translation-dependent destabilization of mRNAs. Mol Cell 2023; 83:589-606.e6. [PMID: 36731471 PMCID: PMC9957601 DOI: 10.1016/j.molcel.2023.01.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/07/2022] [Accepted: 01/06/2023] [Indexed: 02/04/2023]
Abstract
The relationship between mRNA translation and decay is incompletely understood, with conflicting reports suggesting that translation can either promote decay or stabilize mRNAs. The effect of translation on mRNA decay has mainly been studied using ensemble measurements and global transcription and translation inhibitors, which can have pleiotropic effects. We developed a single-molecule imaging approach to control the translation of a specific transcript that enabled simultaneous measurement of translation and mRNA decay. Our results demonstrate that mRNA translation reduces mRNA stability, and mathematical modeling suggests that this process is dependent on ribosome flux. Furthermore, our results indicate that miRNAs mediate efficient degradation of both translating and non-translating target mRNAs and reveal a predominant role for mRNA degradation in miRNA-mediated regulation. Simultaneous observation of translation and decay of single mRNAs provides a framework to directly study how these processes are interconnected in cells.
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Affiliation(s)
- Pratik Dave
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
| | - Gregory Roth
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
| | - Esther Griesbach
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
| | - Daniel Mateju
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
| | - Tobias Hochstoeger
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland; University of Basel, 4003 Basel, Switzerland
| | - Jeffrey A Chao
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland.
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15
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Benchmarking commonly used software suites and analysis workflows for DIA proteomics and phosphoproteomics. Nat Commun 2023; 14:94. [PMID: 36609502 PMCID: PMC9822986 DOI: 10.1038/s41467-022-35740-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 12/20/2022] [Indexed: 01/07/2023] Open
Abstract
A plethora of software suites and multiple classes of spectral libraries have been developed to enhance the depth and robustness of data-independent acquisition (DIA) data processing. However, how the combination of a DIA software tool and a spectral library impacts the outcome of DIA proteomics and phosphoproteomics data analysis has been rarely investigated using benchmark data that mimics biological complexity. In this study, we create DIA benchmark data sets simulating the regulation of thousands of proteins in a complex background, which are collected on both an Orbitrap and a timsTOF instruments. We evaluate four commonly used software suites (DIA-NN, Spectronaut, MaxDIA and Skyline) combined with seven different spectral libraries in global proteome analysis. Moreover, we assess their performances in analyzing phosphopeptide standards and TNF-α-induced phosphoproteome regulation. Our study provides a practical guidance on how to construct a robust data analysis pipeline for different proteomics studies implementing the DIA technique.
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16
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Comparative Proteomic and Transcriptomic Analysis of the Impact of Androgen Stimulation and Darolutamide Inhibition. Cancers (Basel) 2022; 15:cancers15010002. [PMID: 36611998 PMCID: PMC9817687 DOI: 10.3390/cancers15010002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 11/22/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Several inhibitors of androgen receptor (AR) function are approved for prostate cancer treatment, and their impact on gene transcription has been described. However, the ensuing effects at the protein level are far less well understood. We focused on the AR signaling inhibitor darolutamide and confirmed its strong AR binding and antagonistic activity using the high throughput cellular thermal shift assay (CETSA HT). Then, we generated comprehensive, quantitative proteomic data from the androgen-sensitive prostate cancer cell line VCaP and compared them to transcriptomic data. Following treatment with the synthetic androgen R1881 and darolutamide, global mass spectrometry-based proteomics and label-free quantification were performed. We found a generally good agreement between proteomic and transcriptomic data upon androgen stimulation and darolutamide inhibition. Similar effects were found both for the detected expressed genes and their protein products as well as for the corresponding biological programs. However, in a few instances there was a discrepancy in the magnitude of changes induced on gene expression levels compared to the corresponding protein levels, indicating post-transcriptional regulation of protein abundance. Chromatin immunoprecipitation DNA sequencing (ChIP-seq) and Hi-C chromatin immunoprecipitation (HiChIP) revealed the presence of androgen-activated AR-binding regions and long-distance AR-mediated loops at these genes.
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17
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Srivastava H, Lippincott MJ, Currie J, Canfield R, Lam MPY, Lau E. Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners. PLoS Comput Biol 2022; 18:e1010702. [PMID: 36356032 PMCID: PMC9681107 DOI: 10.1371/journal.pcbi.1010702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 11/22/2022] [Accepted: 11/01/2022] [Indexed: 11/12/2022] Open
Abstract
Protein and mRNA levels correlate only moderately. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. Here we examined the contributions of input features in protein abundance prediction models. Using large proteogenomics data from 8 cancer types within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set, we trained models to predict the abundance of over 13,000 proteins using matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and compared predictive performances across algorithms, data set sizes, and input features. Over one-third of proteins (4,648) showed relatively poor predictability (elastic net r ≤ 0.3) from their cognate transcripts. Moreover, we found widespread occurrences where the abundance of a protein is considerably less well explained by its own cognate transcript level than that of one or more trans locus transcripts. The incorporation of additional trans-locus transcript abundance data as input features increasingly improved the ability to predict sample protein abundance. Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known or predicted interaction partners of the protein of interest, including not only large multi-protein complexes as previously shown, but also small stable complexes in the proteome with only one or few stable interacting partners. Network analysis further shows a complex proteome-wide interdependency of protein abundance on the transcript levels of multiple interacting partners. The predictive model analysis here therefore supports that protein-protein interaction including in small protein complexes exert post-transcriptional influence on proteome compositions more broadly than previously recognized. Moreover, the results suggest mRNA and protein co-expression analysis may have utility for finding gene interactions and predicting expression changes in biological systems.
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Affiliation(s)
- Himangi Srivastava
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Michael J. Lippincott
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Jordan Currie
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Robert Canfield
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Maggie P. Y. Lam
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Edward Lau
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, United States of America
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18
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Ciuffa R, Uliana F, Mannion J, Mehnert M, Tenev T, Marulli C, Satanowski A, Keller LML, Rodilla Ramírez PN, Ori A, Gstaiger M, Meier P, Aebersold R. Novel biochemical, structural, and systems insights into inflammatory signaling revealed by contextual interaction proteomics. Proc Natl Acad Sci U S A 2022; 119:e2117175119. [PMID: 36179048 PMCID: PMC9546619 DOI: 10.1073/pnas.2117175119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 07/28/2022] [Indexed: 12/03/2022] Open
Abstract
Protein-protein interactions (PPIs) represent the main mode of the proteome organization in the cell. In the last decade, several large-scale representations of PPI networks have captured generic aspects of the functional organization of network components but mostly lack the context of cellular states. However, the generation of context-dependent PPI networks is essential for structural and systems-level modeling of biological processes-a goal that remains an unsolved challenge. Here we describe an experimental/computational strategy to achieve a modeling of PPIs that considers contextual information. This strategy defines the composition, stoichiometry, temporal organization, and cellular requirements for the formation of target assemblies. We used this approach to generate an integrated model of the formation principles and architecture of a large signalosome, the TNF-receptor signaling complex (TNF-RSC). Overall, we show that the integration of systems- and structure-level information provides a generic, largely unexplored link between the modular proteome and cellular function.
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Affiliation(s)
- Rodolfo Ciuffa
- Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Federico Uliana
- Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Jonathan Mannion
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, SW3 6JB London, United Kingdom
| | - Martin Mehnert
- Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Tencho Tenev
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, SW3 6JB London, United Kingdom
| | - Cathy Marulli
- Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Ari Satanowski
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | | | | | - Alessandro Ori
- Leibniz Institute on Aging, Fritz Lipmann Institute, 07745 Jena, Germany
| | - Matthias Gstaiger
- Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Pascal Meier
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, SW3 6JB London, United Kingdom
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
- Faculty of Science, University of Zurich, 8093 Zurich, Switzerland
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19
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Magnusson R, Rundquist O, Kim MJ, Hellberg S, Na CH, Benson M, Gomez-Cabrero D, Kockum I, Tegnér JN, Piehl F, Jagodic M, Mellergård J, Altafini C, Ernerudh J, Jenmalm MC, Nestor CE, Kim MS, Gustafsson M. RNA-sequencing and mass-spectrometry proteomic time-series analysis of T-cell differentiation identified multiple splice variants models that predicted validated protein biomarkers in inflammatory diseases. Front Mol Biosci 2022; 9:916128. [PMID: 36106020 PMCID: PMC9465313 DOI: 10.3389/fmolb.2022.916128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/25/2022] [Indexed: 12/18/2022] Open
Abstract
Profiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. We characterised time-series of primary human naïve CD4+ T cells during early T helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-seq datasets to evaluate biomarker discovery in five T-cell associated diseases, further validating the findings for multiple sclerosis (MS) and asthma. The new models significantly out-performing models not including the usage of multiple splice variants and time delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was validated in MS using two independent cohorts for evaluating response to treatment and disease prognosis. In summary, our splice variant and time delay models substantially improved the prediction of protein abundance from mRNA expression in three different immune cell types. The models provided valuable biomarker candidates, which were further validated in MS and asthma.
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Affiliation(s)
- Rasmus Magnusson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Olof Rundquist
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Min Jung Kim
- Department of Applied Chemistry, College of Applied Sciences, Kyung Hee University, Yong-in, South Korea
| | - Sandra Hellberg
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Chan Hyun Na
- Department of Neurology, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Mikael Benson
- Centre for Personalised Medicine, Linköping University, Linköping, Sweden
| | - David Gomez-Cabrero
- Navarrabiomed, Complejo Hospitalario de Navarra, Universidad Pública de Navarra, IdiSNA, Pamplona, Spain
| | - Ingrid Kockum
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
| | - Jesper N. Tegnér
- Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden
- Science for Life Laboratory, Solna, Sweden
| | - Fredrik Piehl
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
| | - Maja Jagodic
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
| | - Johan Mellergård
- Department of Neurology, Linköping University, Linköping, Sweden
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Claudio Altafini
- Department of Automatic Control, Linköping University, Linköping, Sweden
| | - Jan Ernerudh
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Department of Clinical Immunology and Transfusion Medicine, Linköping University, Linköping, Sweden
| | - Maria C. Jenmalm
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- *Correspondence: Maria C. Jenmalm, ; Mika Gustafsson,
| | - Colm E. Nestor
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Min-Sik Kim
- Department of New Biology, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Mika Gustafsson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- *Correspondence: Maria C. Jenmalm, ; Mika Gustafsson,
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20
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Filippenkov IB, Remizova JA, Denisova AE, Stavchansky VV, Golovina KD, Gubsky LV, Limborska SA, Dergunova LV. Comparative Use of Contralateral and Sham-Operated Controls Reveals Traces of a Bilateral Genetic Response in the Rat Brain after Focal Stroke. Int J Mol Sci 2022; 23:ijms23137308. [PMID: 35806305 PMCID: PMC9266805 DOI: 10.3390/ijms23137308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/27/2022] [Accepted: 06/29/2022] [Indexed: 02/04/2023] Open
Abstract
Ischemic stroke is a multifactorial disease with a complex etiology and global consequences. Model animals are widely used in stroke studies. Various controls, either brain samples from sham-operated (SO) animals or symmetrically located brain samples from the opposite (contralateral) hemisphere (CH), are often used to analyze the processes in the damaged (ipsilateral) hemisphere (IH) after focal stroke. However, previously, it was shown that focal ischemia can lead to metabolic and transcriptomic changes not only in the IH but also in the CH. Here, using a transient middle cerebral artery occlusion (tMCAO) model and genome-wide RNA sequencing, we identified 1941 overlapping differentially expressed genes (DEGs) with a cutoff value >1.5 and Padj < 0.05 that reflected the general transcriptome response of IH subcortical cells at 24 h after tMCAO using both SO and CH controls. Concomitantly, 861 genes were differentially expressed in IH vs. SO, whereas they were not vs. the CH control. Furthermore, they were associated with apoptosis, the cell cycle, and neurotransmitter responses. In turn, we identified 221 DEGs in IH vs. CH, which were non-DEGs vs. the SO control. Moreover, they were predominantly associated with immune-related response. We believe that both sets of non-overlapping genes recorded transcriptome changes in IH cells associated with transhemispheric differences after focal cerebral ischemia. Thus, the specific response of the CH transcriptome should be considered when using it as a control in studies of target brain regions in diseases that induce a global bilateral genetic response, such as stroke.
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Affiliation(s)
- Ivan B. Filippenkov
- Department of Molecular Bases of Human Genetics, Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (J.A.R.); (V.V.S.); (K.D.G.); (S.A.L.); (L.V.D.)
- Correspondence: ; Tel.: +7-499-196-1858
| | - Julia A. Remizova
- Department of Molecular Bases of Human Genetics, Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (J.A.R.); (V.V.S.); (K.D.G.); (S.A.L.); (L.V.D.)
| | - Alina E. Denisova
- Department of Neurology, Neurosurgery and Medical Genetics, Pirogov Russian National Research Medical University, Ostrovitianov Str. 1, 117997 Moscow, Russia; (A.E.D.); (L.V.G.)
| | - Vasily V. Stavchansky
- Department of Molecular Bases of Human Genetics, Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (J.A.R.); (V.V.S.); (K.D.G.); (S.A.L.); (L.V.D.)
| | - Ksenia D. Golovina
- Department of Molecular Bases of Human Genetics, Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (J.A.R.); (V.V.S.); (K.D.G.); (S.A.L.); (L.V.D.)
| | - Leonid V. Gubsky
- Department of Neurology, Neurosurgery and Medical Genetics, Pirogov Russian National Research Medical University, Ostrovitianov Str. 1, 117997 Moscow, Russia; (A.E.D.); (L.V.G.)
- Federal Center for the Brain and Neurotechnologies, Federal Biomedical Agency, Ostrovitianov Str. 1, Building 10, 117997 Moscow, Russia
| | - Svetlana A. Limborska
- Department of Molecular Bases of Human Genetics, Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (J.A.R.); (V.V.S.); (K.D.G.); (S.A.L.); (L.V.D.)
| | - Lyudmila V. Dergunova
- Department of Molecular Bases of Human Genetics, Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (J.A.R.); (V.V.S.); (K.D.G.); (S.A.L.); (L.V.D.)
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21
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Giansanti P, Samaras P, Bian Y, Meng C, Coluccio A, Frejno M, Jakubowsky H, Dobiasch S, Hazarika RR, Rechenberger J, Calzada-Wack J, Krumm J, Mueller S, Lee CY, Wimberger N, Lautenbacher L, Hassan Z, Chang YC, Falcomatà C, Bayer FP, Bärthel S, Schmidt T, Rad R, Combs SE, The M, Johannes F, Saur D, de Angelis MH, Wilhelm M, Schneider G, Kuster B. Mass spectrometry-based draft of the mouse proteome. Nat Methods 2022; 19:803-811. [PMID: 35710609 DOI: 10.1038/s41592-022-01526-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/17/2022] [Indexed: 01/06/2023]
Abstract
The laboratory mouse ranks among the most important experimental systems for biomedical research and molecular reference maps of such models are essential informational tools. Here, we present a quantitative draft of the mouse proteome and phosphoproteome constructed from 41 healthy tissues and several lines of analyses exemplify which insights can be gleaned from the data. For instance, tissue- and cell-type resolved profiles provide protein evidence for the expression of 17,000 genes, thousands of isoforms and 50,000 phosphorylation sites in vivo. Proteogenomic comparison of mouse, human and Arabidopsis reveal common and distinct mechanisms of gene expression regulation and, despite many similarities, numerous differentially abundant orthologs that likely serve species-specific functions. We leverage the mouse proteome by integrating phenotypic drug (n > 400) and radiation response data with the proteomes of 66 pancreatic ductal adenocarcinoma (PDAC) cell lines to reveal molecular markers for sensitivity and resistance. This unique atlas complements other molecular resources for the mouse and can be explored online via ProteomicsDB and PACiFIC.
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Affiliation(s)
- Piero Giansanti
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Patroklos Samaras
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Yangyang Bian
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany.,College of Life Science, Northwest University, Xi'an, China
| | - Chen Meng
- Bavarian Biomolecular Mass Spectrometry Center, Technical University of Munich, Freising, Germany
| | - Andrea Coluccio
- Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.,Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Internal Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Martin Frejno
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Hannah Jakubowsky
- Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.,Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Internal Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Sophie Dobiasch
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Institute of Radiation Medicine, Department of Radiation Sciences, Helmholtz Zentrum München, Neuherberg, Germany.,German Cancer Consortium (DKTK), Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rashmi R Hazarika
- Population epigenetics and epigenomics, Technical University of Munich, Freising, Germany.,Institute of Advanced Study (IAS), Technical University of Munich, Freising, Germany
| | - Julia Rechenberger
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Julia Calzada-Wack
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Johannes Krumm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Sebastian Mueller
- Department of Internal Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany.,Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Chien-Yun Lee
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Nicole Wimberger
- Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.,Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Internal Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Ludwig Lautenbacher
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Zonera Hassan
- Medical Clinic and Policlinic II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Yun-Chien Chang
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Chiara Falcomatà
- Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.,Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Internal Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Florian P Bayer
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Stefanie Bärthel
- Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.,Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Internal Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Tobias Schmidt
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Roland Rad
- Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.,Department of Internal Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany.,Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Institute of Radiation Medicine, Department of Radiation Sciences, Helmholtz Zentrum München, Neuherberg, Germany.,German Cancer Consortium (DKTK), Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthew The
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Frank Johannes
- Population epigenetics and epigenomics, Technical University of Munich, Freising, Germany.,Institute of Advanced Study (IAS), Technical University of Munich, Freising, Germany
| | - Dieter Saur
- Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.,Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Internal Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Martin Hrabe de Angelis
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Chair of Experimental Genetics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany.,Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Günter Schneider
- Medical Clinic and Policlinic II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,University Medical Center Göttingen, Department of General, Visceral and Pediatric Surgery, Göttingen, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany. .,Bavarian Biomolecular Mass Spectrometry Center, Technical University of Munich, Freising, Germany. .,German Cancer Consortium (DKTK), Munich, Germany. .,German Cancer Research Center (DKFZ), Heidelberg, Germany. .,Institute of Advanced Study (IAS), Technical University of Munich, Freising, Germany.
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22
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Assum I, Krause J, Scheinhardt MO, Müller C, Hammer E, Börschel CS, Völker U, Conradi L, Geelhoed B, Zeller T, Schnabel RB, Heinig M. Tissue-specific multi-omics analysis of atrial fibrillation. Nat Commun 2022; 13:441. [PMID: 35064145 PMCID: PMC8782899 DOI: 10.1038/s41467-022-27953-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 12/16/2021] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript (cis-eQTL) and protein (cis-pQTL) abundance. We further establish a novel targeted trans-QTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans-eQTLs and five trans-pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization.
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Affiliation(s)
- Ines Assum
- Computational Health Center, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), München, Germany
- Department of Informatics, Technical University Munich, München, Germany
| | - Julia Krause
- University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany
- Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany
| | - Markus O Scheinhardt
- Institute of Medical Biometry and Statistics, University of Lübeck, University Hospital of Schleswig-Holstein, Lübeck, Germany
| | - Christian Müller
- University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany
- Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany
| | - Elke Hammer
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- Partner site Greifswald, DZHK (German Center for Cardiovascular Research), Greifswald, Germany
| | - Christin S Börschel
- Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany
- Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- Partner site Greifswald, DZHK (German Center for Cardiovascular Research), Greifswald, Germany
| | - Lenard Conradi
- Department of Cardiovascular Surgery, University Heart and Vascular Center Hamburg, Hamburg, Germany
| | - Bastiaan Geelhoed
- Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany
- Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Tanja Zeller
- University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany
- Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany
| | - Renate B Schnabel
- Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany.
- Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.
| | - Matthias Heinig
- Computational Health Center, Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), München, Germany.
- Department of Informatics, Technical University Munich, München, Germany.
- Partner site Munich, DZHK (German Center for Cardiovascular Research), Munich, Germany.
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23
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Reixachs-Solé M, Eyras E. Uncovering the impacts of alternative splicing on the proteome with current omics techniques. WILEY INTERDISCIPLINARY REVIEWS. RNA 2022; 13:e1707. [PMID: 34979593 PMCID: PMC9542554 DOI: 10.1002/wrna.1707] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/27/2021] [Accepted: 11/29/2021] [Indexed: 12/15/2022]
Abstract
The high‐throughput sequencing of cellular RNAs has underscored a broad effect of isoform diversification through alternative splicing on the transcriptome. Moreover, the differential production of transcript isoforms from gene loci has been recognized as a critical mechanism in cell differentiation, organismal development, and disease. Yet, the extent of the impact of alternative splicing on protein production and cellular function remains a matter of debate. Multiple experimental and computational approaches have been developed in recent years to address this question. These studies have unveiled how molecular changes at different steps in the RNA processing pathway can lead to differences in protein production and have functional effects. New and emerging experimental technologies open exciting new opportunities to develop new methods to fully establish the connection between messenger RNA expression and protein production and to further investigate how RNA variation impacts the proteome and cell function. This article is categorized under:RNA Processing > Splicing Regulation/Alternative Splicing Translation > Regulation RNA Evolution and Genomics > Computational Analyses of RNA
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Affiliation(s)
- Marina Reixachs-Solé
- The John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia.,EMBL Australia Partner Laboratory Network and the Australian National University, Canberra, Australian Capital Territory, Australia
| | - Eduardo Eyras
- The John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia.,EMBL Australia Partner Laboratory Network and the Australian National University, Canberra, Australian Capital Territory, Australia.,Catalan Institution for Research and Advanced Studies, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
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24
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Brümmer A, Dreos R, Marques AC, Bergmann S. Analysis of eukaryotic lincRNA sequences indicates signatures of hindered translation linked to selection pressure. Mol Biol Evol 2021; 39:6460347. [PMID: 34897509 PMCID: PMC8826458 DOI: 10.1093/molbev/msab356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Long intergenic noncoding RNAs (lincRNAs) represent a large fraction of transcribed loci in eukaryotic genomes. Although classified as noncoding, most lincRNAs contain open reading frames (ORFs), and it remains unclear why cytoplasmic lincRNAs are not or very inefficiently translated. Here, we analyzed signatures of hindered translation in lincRNA sequences from five eukaryotes, covering a range of natural selection pressures. In fission yeast and Caenorhabditis elegans, that is, species under strong selection, we detected significantly shorter ORFs, a suboptimal sequence context around start codons for translation initiation, and trinucleotides (“codons”) corresponding to less abundant tRNAs than for neutrally evolving control sequences, likely impeding translation elongation. For human, we detected signatures for cell-type-specific hindrance of lincRNA translation, in particular codons in abundant cytoplasmic lincRNAs corresponding to lower expressed tRNAs than control codons, in three out of five human cell lines. We verified that varying tRNA expression levels between cell lines are reflected in the amount of ribosomes bound to cytoplasmic lincRNAs in each cell line. We further propose that codons at ORF starts are particularly important for reducing ribosome-binding to cytoplasmic lincRNA ORFs. Altogether, our analyses indicate that in species under stronger selection lincRNAs evolved sequence features generally hindering translation and support cell-type-specific hindrance of translation efficiency in human lincRNAs. The sequence signatures we have identified may improve predicting peptide-coding and genuine noncoding lincRNAs in a cell type.
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Affiliation(s)
- Anneke Brümmer
- Department of Computational Biology (DBC), University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Rene Dreos
- Center for Integrative Genomics (CIG), University of Lausanne, Lausanne, Switzerland
| | - Ana Claudia Marques
- Department of Computational Biology (DBC), University of Lausanne, Lausanne, Switzerland
| | - Sven Bergmann
- Department of Computational Biology (DBC), University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
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25
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Shichkova P, Coggan JS, Markram H, Keller D. A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation. Front Mol Neurosci 2021; 14:604559. [PMID: 34858137 PMCID: PMC8631404 DOI: 10.3389/fnmol.2021.604559] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 10/05/2021] [Indexed: 12/12/2022] Open
Abstract
Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.
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Affiliation(s)
- Polina Shichkova
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Jay S Coggan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
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26
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Nicolet BP, Zandhuis ND, Lattanzio VM, Wolkers MC. Sequence determinants as key regulators in gene expression of T cells. Immunol Rev 2021; 304:10-29. [PMID: 34486113 PMCID: PMC9292449 DOI: 10.1111/imr.13021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 12/12/2022]
Abstract
T cell homeostasis, T cell differentiation, and T cell effector function rely on the constant fine-tuning of gene expression. To alter the T cell state, substantial remodeling of the proteome is required. This remodeling depends on the intricate interplay of regulatory mechanisms, including post-transcriptional gene regulation. In this review, we discuss how the sequence of a transcript influences these post-transcriptional events. In particular, we review how sequence determinants such as sequence conservation, GC content, and chemical modifications define the levels of the mRNA and the protein in a T cell. We describe the effect of different forms of alternative splicing on mRNA expression and protein production, and their effect on subcellular localization. In addition, we discuss the role of sequences and structures as binding hubs for miRNAs and RNA-binding proteins in T cells. The review thus highlights how the intimate interplay of post-transcriptional mechanisms dictate cellular fate decisions in T cells.
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Affiliation(s)
- Benoit P. Nicolet
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - Nordin D. Zandhuis
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - V. Maria Lattanzio
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - Monika C. Wolkers
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
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27
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Zrimec J, Buric F, Kokina M, Garcia V, Zelezniak A. Learning the Regulatory Code of Gene Expression. Front Mol Biosci 2021; 8:673363. [PMID: 34179082 PMCID: PMC8223075 DOI: 10.3389/fmolb.2021.673363] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.
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Affiliation(s)
- Jan Zrimec
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Filip Buric
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mariia Kokina
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Victor Garcia
- School of Life Sciences and Facility Management, Zurich University of Applied Sciences, Wädenswil, Switzerland
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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28
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Martinez-Seidel F, Beine-Golovchuk O, Hsieh YC, Eshraky KE, Gorka M, Cheong BE, Jimenez-Posada EV, Walther D, Skirycz A, Roessner U, Kopka J, Pereira Firmino AA. Spatially Enriched Paralog Rearrangements Argue Functionally Diverse Ribosomes Arise during Cold Acclimation in Arabidopsis. Int J Mol Sci 2021; 22:6160. [PMID: 34200446 PMCID: PMC8201131 DOI: 10.3390/ijms22116160] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/23/2021] [Accepted: 06/01/2021] [Indexed: 12/15/2022] Open
Abstract
Ribosome biogenesis is essential for plants to successfully acclimate to low temperature. Without dedicated steps supervising the 60S large subunits (LSUs) maturation in the cytosol, e.g., Rei-like (REIL) factors, plants fail to accumulate dry weight and fail to grow at suboptimal low temperatures. Around REIL, the final 60S cytosolic maturation steps include proofreading and assembly of functional ribosomal centers such as the polypeptide exit tunnel and the P-Stalk, respectively. In consequence, these ribosomal substructures and their assembly, especially during low temperatures, might be changed and provoke the need for dedicated quality controls. To test this, we blocked ribosome maturation during cold acclimation using two independent reil double mutant genotypes and tested changes in their ribosomal proteomes. Additionally, we normalized our mutant datasets using as a blank the cold responsiveness of a wild-type Arabidopsis genotype. This allowed us to neglect any reil-specific effects that may happen due to the presence or absence of the factor during LSU cytosolic maturation, thus allowing us to test for cold-induced changes that happen in the early nucleolar biogenesis. As a result, we report that cold acclimation triggers a reprogramming in the structural ribosomal proteome. The reprogramming alters the abundance of specific RP families and/or paralogs in non-translational LSU and translational polysome fractions, a phenomenon known as substoichiometry. Next, we tested whether the cold-substoichiometry was spatially confined to specific regions of the complex. In terms of RP proteoforms, we report that remodeling of ribosomes after a cold stimulus is significantly constrained to the polypeptide exit tunnel (PET), i.e., REIL factor binding and functional site. In terms of RP transcripts, cold acclimation induces changes in RP families or paralogs that are significantly constrained to the P-Stalk and the ribosomal head. The three modulated substructures represent possible targets of mechanisms that may constrain translation by controlled ribosome heterogeneity. We propose that non-random ribosome heterogeneity controlled by specialized biogenesis mechanisms may contribute to a preferential or ultimately even rigorous selection of transcripts needed for rapid proteome shifts and successful acclimation.
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Affiliation(s)
- Federico Martinez-Seidel
- Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; (O.B.-G.); (Y.-C.H.); (K.E.E.); (M.G.); (B.-E.C.); (D.W.); (A.S.); (J.K.); (A.A.P.F.)
- School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia;
| | - Olga Beine-Golovchuk
- Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; (O.B.-G.); (Y.-C.H.); (K.E.E.); (M.G.); (B.-E.C.); (D.W.); (A.S.); (J.K.); (A.A.P.F.)
- Heidelberg University, Biochemie-Zentrum, Nuclear Pore Complex and Ribosome Assembly, 69120 Heidelberg, Germany
| | - Yin-Chen Hsieh
- Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; (O.B.-G.); (Y.-C.H.); (K.E.E.); (M.G.); (B.-E.C.); (D.W.); (A.S.); (J.K.); (A.A.P.F.)
- Institute for Arctic and Marine Biology, UiT Arctic University of Norway, 9037 Tromsø, Norway
| | - Kheloud El Eshraky
- Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; (O.B.-G.); (Y.-C.H.); (K.E.E.); (M.G.); (B.-E.C.); (D.W.); (A.S.); (J.K.); (A.A.P.F.)
| | - Michal Gorka
- Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; (O.B.-G.); (Y.-C.H.); (K.E.E.); (M.G.); (B.-E.C.); (D.W.); (A.S.); (J.K.); (A.A.P.F.)
| | - Bo-Eng Cheong
- Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; (O.B.-G.); (Y.-C.H.); (K.E.E.); (M.G.); (B.-E.C.); (D.W.); (A.S.); (J.K.); (A.A.P.F.)
- School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia;
- Biotechnology Research Institute, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Malaysia
| | - Erika V. Jimenez-Posada
- Grupo de Biotecnología-Productos Naturales, Universidad Tecnológica de Pereira, Pereira 660003, Colombia;
- Emerging Infectious Diseases and Tropical Medicine Research Group—Sci-Help, Pereira 660009, Colombia
| | - Dirk Walther
- Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; (O.B.-G.); (Y.-C.H.); (K.E.E.); (M.G.); (B.-E.C.); (D.W.); (A.S.); (J.K.); (A.A.P.F.)
| | - Aleksandra Skirycz
- Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; (O.B.-G.); (Y.-C.H.); (K.E.E.); (M.G.); (B.-E.C.); (D.W.); (A.S.); (J.K.); (A.A.P.F.)
| | - Ute Roessner
- School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia;
| | - Joachim Kopka
- Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; (O.B.-G.); (Y.-C.H.); (K.E.E.); (M.G.); (B.-E.C.); (D.W.); (A.S.); (J.K.); (A.A.P.F.)
| | - Alexandre Augusto Pereira Firmino
- Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; (O.B.-G.); (Y.-C.H.); (K.E.E.); (M.G.); (B.-E.C.); (D.W.); (A.S.); (J.K.); (A.A.P.F.)
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29
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Karollus A, Avsec Ž, Gagneur J. Predicting mean ribosome load for 5'UTR of any length using deep learning. PLoS Comput Biol 2021; 17:e1008982. [PMID: 33970899 PMCID: PMC8136849 DOI: 10.1371/journal.pcbi.1008982] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 05/20/2021] [Accepted: 04/19/2021] [Indexed: 01/07/2023] Open
Abstract
The 5’ untranslated region plays a key role in regulating mRNA translation and consequently protein abundance. Therefore, accurate modeling of 5’UTR regulatory sequences shall provide insights into translational control mechanisms and help interpret genetic variants. Recently, a model was trained on a massively parallel reporter assay to predict mean ribosome load (MRL)—a proxy for translation rate—directly from 5’UTR sequence with a high degree of accuracy. However, this model is restricted to sequence lengths investigated in the reporter assay and therefore cannot be applied to the majority of human sequences without a substantial loss of information. Here, we introduced frame pooling, a novel neural network operation that enabled the development of an MRL prediction model for 5’UTRs of any length. Our model shows state-of-the-art performance on fixed length randomized sequences, while offering better generalization performance on longer sequences and on a variety of translation-related genome-wide datasets. Variant interpretation is demonstrated on a 5’UTR variant of the gene HBB associated with beta-thalassemia. Frame pooling could find applications in other bioinformatics predictive tasks. Moreover, our model, released open source, could help pinpoint pathogenic genetic variants. The human genome carries a complex code. It consists of genes, which provide blueprints to assemble proteins, and regulatory elements, which control when, where, and how often particular genes are transcribed and translated into protein. To read the genome correctly and specifically to find the causes of inherited diseases, we need to be able to find and interpret these regulatory elements. Here, we focus on particular regions of the genome, the so-called 5’ untranslated regions, which play an important role in determining how often a transcribed gene is translated into protein. We develop deep learning models which can quantitatively interpret regulatory elements in human 5’ untranslated regions and use this information to predict a proxy of the translation efficiency. Our model generalizes a previous model to 5’ untranslated regions of any length, just as they are encountered in natural human genes. Because this model requires only the sequence as input, it can give estimates for the impact of mutations in the sequence, even if these particular mutations are very rare or entirely novel. Such estimates could help pinpoint mutations that disrupt the normal functioning of gene regulation, which could be used to better diagnose patients suffering from rare genetic disorders.
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Affiliation(s)
- Alexander Karollus
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Žiga Avsec
- Department of Informatics, Technical University of Munich, Garching, Germany
- Graduate School of Quantitative Biosciences (QBM), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Julien Gagneur
- Department of Informatics, Technical University of Munich, Garching, Germany
- Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- * E-mail:
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30
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De Marchi T, Pyl PT, Sjöström M, Klasson S, Sartor H, Tran L, Pekar G, Malmström J, Malmström L, Niméus E. Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance. J Proteome Res 2021; 20:2983-3001. [PMID: 33855848 PMCID: PMC8155562 DOI: 10.1021/acs.jproteome.1c00243] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Indexed: 12/21/2022]
Abstract
Proteogenomic approaches have enabled the generat̲ion of novel information levels when compared to single omics studies although burdened by extensive experimental efforts. Here, we improved a data-independent acquisition mass spectrometry proteogenomic workflow to reveal distinct molecular features related to mammographic appearances in breast cancer. Our results reveal splicing processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Furthermore, we confirm previously detected enrichments of molecular pathways associated with estrogen receptor-dependent activity and provide novel evidence of epithelial-to-mesenchymal activity in mammography-detected spiculated tumors. Several transcript-protein pairs displayed radically different abundances depending on the overall clinical properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant tumor subgroups, which in turn alter both cancer biology and the abundance of biomarker candidates and drug targets.
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Affiliation(s)
- Tommaso De Marchi
- Division
of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Lund University, Solvegatan 19, Lund SE-223 62, Sweden
| | - Paul Theodor Pyl
- Division
of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Lund University, Solvegatan 19, Lund SE-223 62, Sweden
| | - Martin Sjöström
- Division
of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Lund University, Solvegatan 19, Lund SE-223 62, Sweden
| | - Stina Klasson
- Department
Plastic and Reconstructive Surgery, Skåne
University Hospital, Inga Marie Nilssons gata 47, Malmö SE-20502, Sweden
| | - Hanna Sartor
- Division
of Diagnostic Radiology, Department of Translational Medicine, Skåne University Hospital, Entrégatan 7, Lund SE-22185, Sweden
| | - Lena Tran
- Division
of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Lund University, Solvegatan 19, Lund SE-223 62, Sweden
| | - Gyula Pekar
- Division
of Oncology and Pathology, Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund SE-22185, Sweden
| | - Johan Malmström
- Division
of Infection Medicine, Department of Clinical Sciences Lund, Faculty
of Medicine, Lund University, Klinikgatan 32, Lund SE-22184, Sweden
| | - Lars Malmström
- S3IT, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
- Institute
for Computational Science, University of
Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
| | - Emma Niméus
- Division
of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Lund University, Solvegatan 19, Lund SE-223 62, Sweden
- Department
of Surgery, Skåne University Hospital, Lund 222 42, Sweden
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31
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Mitschka S, Mayr C. Endogenous p53 expression in human and mouse is not regulated by its 3'UTR. eLife 2021; 10:65700. [PMID: 33955355 PMCID: PMC8137139 DOI: 10.7554/elife.65700] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 05/05/2021] [Indexed: 12/14/2022] Open
Abstract
The TP53 gene encodes the tumor suppressor p53 which is functionally inactivated in many human cancers. Numerous studies suggested that 3′UTR-mediated p53 expression regulation plays a role in tumorigenesis and could be exploited for therapeutic purposes. However, these studies did not investigate post-transcriptional regulation of the native TP53 gene. Here, we used CRISPR/Cas9 to delete the human and mouse TP53/Trp53 3′UTRs while preserving endogenous mRNA processing. This revealed that the endogenous 3′UTR is not involved in regulating p53 mRNA or protein expression neither in steady state nor after genotoxic stress. Using reporter assays, we confirmed the previously observed repressive effects of the isolated 3′UTR. However, addition of the TP53 coding region to the reporter had a dominant negative impact on expression as its repressive effect was stronger and abrogated the contribution of the 3′UTR. Our data highlight the importance of genetic models in the validation of post-transcriptional gene regulatory effects.
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Affiliation(s)
- Sibylle Mitschka
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Christine Mayr
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, United States
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32
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Abstract
Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.
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Affiliation(s)
- Mark C. Blaser
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon Kraler
- Center for Molecular Cardiology, University of Zurich, Schlieren, CH
| | - Thomas F. Lüscher
- Center for Molecular Cardiology, University of Zurich, Schlieren, CH
- Heart Division, Royal Brompton & Harefield Hospitals, London, UK
- National Heart and Lung Institute, Imperial College, London, UK
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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33
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Wu B, Qiao J, Wang X, Liu M, Xu S, Sun D. Factors affecting the rapid changes of protein under short-term heat stress. BMC Genomics 2021; 22:263. [PMID: 33849452 PMCID: PMC8042900 DOI: 10.1186/s12864-021-07560-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 03/26/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Protein content determines the state of cells. The variation in protein abundance is crucial when organisms are in the early stages of heat stress, but the reasons affecting their changes are largely unknown. RESULTS We quantified 47,535 mRNAs and 3742 proteins in the filling grains of wheat in two different thermal environments. The impact of mRNA abundance and sequence features involved in protein translation and degradation on protein expression was evaluated by regression analysis. Transcription, codon usage and amino acid frequency were the main drivers of changes in protein expression under heat stress, and their combined contribution explains 58.2 and 66.4% of the protein variation at 30 and 40 °C (20 °C as control), respectively. Transcription contributes more to alterations in protein content at 40 °C (31%) than at 30 °C (6%). Furthermore, the usage of codon AAG may be closely related to the rapid alteration of proteins under heat stress. The contributions of AAG were 24 and 13% at 30 and 40 °C, respectively. CONCLUSION In this study, we analyzed the factors affecting the changes in protein expression in the early stage of heat stress and evaluated their influence.
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Affiliation(s)
- Bingjin Wu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
| | - Jianwen Qiao
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Plant Protection, Northwest A&F University, Yangling, 712100 Shaanxi China
| | - Xiaoming Wang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
| | - Manshuang Liu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
| | - Shengbao Xu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
| | - Daojie Sun
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100 Shaanxi China
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34
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Domitrovic T, Moreira MH, Carneiro RL, Ribeiro-Alves M, Palhano FL. Natural variation of the cardiac transcriptome in humans. RNA Biol 2020; 18:1374-1381. [PMID: 33258390 DOI: 10.1080/15476286.2020.1857508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
We investigated the gene-expression variation among humans by analysing previously published mRNA-seq and ribosome footprint profiling of heart left-ventricles from healthy donors. We ranked the genes according to their coefficient of variation values and found that the top 5% most variable genes had special features compared to the rest of the genome, such as lower mRNA levels and shorter half-lives coupled to increased translation efficiency. We observed that these genes are mostly involved with immune response and have a pleiotropic effect on disease phenotypes, indicating that asymptomatic conditions contribute to the gene expression diversity of healthy individuals.
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Affiliation(s)
- Tatiana Domitrovic
- Departamento de Virologia, Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Mariana H Moreira
- Programa de Biologia Estrutural, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de janeiro, Rio de Janeiro, Brazil
| | - Rodolfo L Carneiro
- Programa de Biologia Estrutural, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de janeiro, Rio de Janeiro, Brazil
| | - Marcelo Ribeiro-Alves
- Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Fernando L Palhano
- Programa de Biologia Estrutural, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de janeiro, Rio de Janeiro, Brazil
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35
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Alroy I, Mansour W, Klepfish M, Sheinberger Y. Expanding small-molecule target space to mRNA translation regulation. Drug Discov Today 2020; 26:786-793. [PMID: 33296694 DOI: 10.1016/j.drudis.2020.11.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 10/26/2020] [Accepted: 11/12/2020] [Indexed: 01/05/2023]
Abstract
Multiple layers of regulation are in place on mRNA translation to ensure that cells respond in a fast manner to environmental cues in a tissue-specific and mRNA-selective manner. Here, we discuss mRNA translation regulatory mechanisms and potential drug-intervention targets. Taking on a new scientific rational of translation regulation and consequently a new target space, we have developed a unique discovery platform that is able to identify selective small molecule drugs that affect translation of specific proteins. This approach has enabled targeting of proteins that have been considered undruggable. Our discovery platform was repeatedly utilized to identify compounds in multiple therapeutic programs, including fibrosis, oncology, anti-virals and Huntington's disease. In fibrosis, the lead compound ANI-21 has demonstrated a tissue-specific effect in lowering the translation of Collagen-I and superior efficacy over best standard of care, in both cell and animal models, mediated by a novel mechanism of action. This program is expected to enter clinical studies within 12-18 months.
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Affiliation(s)
- Iris Alroy
- Anima Biotech, Bernardsville, NJ 07924, USA.
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36
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Barzine MP, Freivalds K, Wright JC, Opmanis M, Rituma D, Ghavidel FZ, Jarnuczak AF, Celms E, Čerāns K, Jonassen I, Lace L, Antonio Vizcaíno J, Choudhary JS, Brazma A, Viksna J. Using Deep Learning to Extrapolate Protein Expression Measurements. Proteomics 2020; 20:e2000009. [PMID: 32937025 PMCID: PMC7757209 DOI: 10.1002/pmic.202000009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 08/27/2020] [Indexed: 01/23/2023]
Abstract
Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average R 2 scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, it is demonstrated that the derived models can be "transferred" across experiments and species. For instance, the model derived from human tissues gave a R 2 = 0.51 when applied to mouse tissue data. It is concluded that protein abundances generated in label-free MS experiments can be computationally predicted using functional annotated attributes and can be used to highlight aberrant protein abundance values.
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Affiliation(s)
- Mitra Parissa Barzine
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteEMBL‐EBIWellcome Trust Genome CampusHinxtonCB10 1SDUK
| | - Karlis Freivalds
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
| | | | - Mārtiņš Opmanis
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
| | - Darta Rituma
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
| | | | - Andrew F. Jarnuczak
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteEMBL‐EBIWellcome Trust Genome CampusHinxtonCB10 1SDUK
| | - Edgars Celms
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
| | - Kārlis Čerāns
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
| | - Inge Jonassen
- Computational Biology UnitInformatics DepartmentUniversity of BergenBergenNO5020Norway
| | - Lelde Lace
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
| | - Juan Antonio Vizcaíno
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteEMBL‐EBIWellcome Trust Genome CampusHinxtonCB10 1SDUK
| | | | - Alvis Brazma
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteEMBL‐EBIWellcome Trust Genome CampusHinxtonCB10 1SDUK
| | - Juris Viksna
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
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37
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Beckett L, Xie S, Thimmapuram J, Tucker HA, Donkin SS, Casey T. Mammary transcriptome reveals cell maintenance and protein turnover support milk synthesis in early-lactation cows. Physiol Genomics 2020; 52:435-450. [PMID: 32744883 DOI: 10.1152/physiolgenomics.00046.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
A more complete understanding of the molecular mechanisms that support milk synthesis is needed to develop strategies to efficiently and sustainably meet the growing global demand for dairy products. With the postulate that coding gene transcript abundance reflects relative importance in supporting milk synthesis, we analyzed the global transcriptome of early lactation cows across magnitudes of normalized RNA-Seq read counts. Total RNA was isolated from milk samples collected from early-lactation cows (n = 6) following two treatment periods of postruminal lysine infusion of 0 or 63 g/day. Twelve libraries were prepared and sequenced on an Illumina NovaSeq6000 platform using paired end reads. Normalized read counts were averaged across both treatments, because EBseq analysis found no significant effect of lysine infusion. Approximately 10% of the total reads corresponded to 12,730 protein coding transcripts with a normalized read count mean ≥5. For functional annotation analysis, the protein coding transcripts were divided into nine categories by magnitude of reads. The 13 most abundant transcripts (≥50K reads) accounted for 67% of the 23M coding reads and included casein and whey proteins, regulators of fat synthesis and secretion, a ubiquitinating protein, and a tRNA transporter. Mammalian target of rapamycin, JAK/STAT, peroxisome proliferator-activated receptor alpha, and ubiquitin proteasome pathways were enriched with normalized reads ≥100 counts. Genes with ≤100 reads regulated tissue homeostasis and immune response. Enrichment in ontologies that reflect maintenance of translation, protein turnover, and amino acid recycling indicated that proteostatic mechanisms are central to supporting mammary function and primary milk component synthesis.
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Affiliation(s)
- L Beckett
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana
| | - S Xie
- Bioinformatics Core, Purdue University, West Lafayette, Indiana
| | - J Thimmapuram
- Bioinformatics Core, Purdue University, West Lafayette, Indiana
| | - H A Tucker
- Novus International Incorporated, St. Charles, Missouri
| | - S S Donkin
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana
| | - T Casey
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana
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38
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Yang M, Petralia F, Li Z, Li H, Ma W, Song X, Kim S, Lee H, Yu H, Lee B, Bae S, Heo E, Kaczmarczyk J, Stępniak P, Warchoł M, Yu T, Calinawan AP, Boutros PC, Payne SH, Reva B, Boja E, Rodriguez H, Stolovitzky G, Guan Y, Kang J, Wang P, Fenyö D, Saez-Rodriguez J, Aderinwale T, Afyounian E, Agrawal P, Ali M, Amadoz A, Azuaje F, Bachman J, Bae S, Bhalla S, Carbonell-Caballero J, Chakraborty P, Chaudhary K, Choi Y, Choi Y, Çubuk C, Dhanda SK, Dopazo J, Elo LL, Fóthi Á, Gevaert O, Granberg K, Greiner R, Heo E, Hidalgo MR, Jayaswal V, Jeon H, Jeon M, Kalmady SV, Kambara Y, Kang J, Kang K, Kaoma T, Kaur H, Kazan H, Kesar D, Kesseli J, Kim D, Kim K, Kim SY, Kim S, Kumar S, Lee B, Lee H, Liu Y, Luethy R, Mahajan S, Mahmoudian M, Muller A, Nazarov PV, Nguyen H, Nykter M, Okuda S, Park S, Pal Singh Raghava G, Rajapakse JC, Rantapero T, Ryu H, Salavert F, Saraei S, Sharma R, Siitonen A, Sokolov A, Subramanian K, Suni V, Suomi T, Tranchevent LC, Usmani SS, Välikangas T, Vega R, Zhong H. Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics. Cell Syst 2020; 11:186-195.e9. [DOI: 10.1016/j.cels.2020.06.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 03/12/2020] [Accepted: 06/29/2020] [Indexed: 10/23/2022]
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39
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Buccitelli C, Selbach M. mRNAs, proteins and the emerging principles of gene expression control. Nat Rev Genet 2020; 21:630-644. [PMID: 32709985 DOI: 10.1038/s41576-020-0258-4] [Citation(s) in RCA: 470] [Impact Index Per Article: 117.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2020] [Indexed: 12/15/2022]
Abstract
Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA-protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.
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Affiliation(s)
| | - Matthias Selbach
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany. .,Charité - Universitätsmedizin Berlin, Berlin, Germany.
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40
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Mass-spectrometry-based draft of the Arabidopsis proteome. Nature 2020; 579:409-414. [PMID: 32188942 DOI: 10.1038/s41586-020-2094-2] [Citation(s) in RCA: 253] [Impact Index Per Article: 63.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 01/17/2020] [Indexed: 01/05/2023]
Abstract
Plants are essential for life and are extremely diverse organisms with unique molecular capabilities1. Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions.
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41
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Hernandez‐Alias X, Benisty H, Schaefer MH, Serrano L. Translational efficiency across healthy and tumor tissues is proliferation-related. Mol Syst Biol 2020; 16:e9275. [PMID: 32149479 PMCID: PMC7061310 DOI: 10.15252/msb.20199275] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 02/11/2020] [Accepted: 02/11/2020] [Indexed: 12/20/2022] Open
Abstract
Different tissues express genes with particular codon usage and anticodon tRNA repertoires. However, the codon-anticodon co-adaptation in humans is not completely understood, nor is its effect on tissue-specific protein levels. Here, we first validated the accuracy of small RNA-seq for tRNA quantification across five human cell lines. We then analyzed the tRNA abundance of more than 8,000 tumor samples from TCGA, together with their paired mRNA-seq and proteomics data, to determine the Supply-to-Demand Adaptation. We thereby elucidate that the dynamic adaptation of the tRNA pool is largely related to the proliferative state across tissues. The distribution of such tRNA pools over the whole cellular translatome affects the subsequent translational efficiency, which functionally determines a condition-specific expression program both in healthy and tumor states. Furthermore, the aberrant translational efficiency of some codons in cancer, exemplified by ProCCA and GlyGGT, is associated with poor patient survival. The regulation of these tRNA profiles is partly explained by the tRNA gene copy numbers and their promoter DNA methylation.
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Affiliation(s)
- Xavier Hernandez‐Alias
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Hannah Benisty
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Martin H Schaefer
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Department of Experimental OncologyIEO, European Institute of Oncology IRCCSMilanItaly
| | - Luis Serrano
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu Fabra (UPF)BarcelonaSpain
- ICREABarcelonaSpain
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42
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Regulation of Proteins in Human Skeletal Muscle: The Role of Transcription. Sci Rep 2020; 10:3514. [PMID: 32103137 PMCID: PMC7044165 DOI: 10.1038/s41598-020-60578-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 02/07/2020] [Indexed: 01/19/2023] Open
Abstract
Regular low intensity aerobic exercise (aerobic training) provides effective protection against various metabolic disorders. Here, the roles played by transient transcriptome responses to acute exercise and by changes in baseline gene expression during up-regulation of protein content in human skeletal muscle were investigated after 2 months of aerobic training. Seven untrained males were involved in a 2 month aerobic cycling training program. Mass-spectrometry and RNA sequencing were used to evaluate proteome and transcriptome responses to training and acute exercise. We found that proteins with different functions are regulated differently at the transcriptional level; for example, a training-induced increase in the content of extracellular matrix-related proteins is regulated at the transcriptional level, while an increase in the content of mitochondrial proteins is not. An increase in the skeletal muscle content of several proteins (including mitochondrial proteins) was associated with increased protein stability, which is related to a chaperone-dependent mechanism and/or reduced regulation by proteolysis. These findings increase our understanding of the molecular mechanisms underlying regulation of protein expression in human skeletal muscle subjected to repeated stress (long term aerobic training) and may provide an opportunity to control the expression of specific proteins (e.g., extracellular matrix-related proteins, mitochondrial proteins) through physiological and/or pharmacological approaches.
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Abstract
The premise of this book is the importance of the tumor microenvironment (TME). Until recently, most research on and clinical attention to cancer biology, diagnosis, and prognosis were focused on the malignant (or premalignant) cellular compartment that could be readily appreciated using standard morphology-based imaging.
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Li H, Siddiqui O, Zhang H, Guan Y. Joint learning improves protein abundance prediction in cancers. BMC Biol 2019; 17:107. [PMID: 31870366 PMCID: PMC6929375 DOI: 10.1186/s12915-019-0730-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 12/04/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances. This is particularly the case in cancer samples and when evaluating the same gene across multiple samples. RESULTS Here, we report a method for predicting proteome from transcriptome, using a training dataset provided by NCI-CPTAC and TCGA, consisting of transcriptome and proteome data from 77 breast and 105 ovarian cancer samples. First, we establish a generic model capturing the correlation between mRNA and protein abundance of a single gene. Second, we build a gene-specific model capturing the interdependencies among multiple genes in a regulatory network. Third, we create a cross-tissue model by joint learning the information of shared regulatory networks and pathways across cancer tissues. Our method ranked first in the NCI-CPTAC DREAM Proteogenomics Challenge, and the predictive performance is close to the accuracy of experimental replicates. Key functional pathways and network modules controlling the proteomic abundance in cancers were revealed, in particular metabolism-related genes. CONCLUSIONS We present a method to predict proteome from transcriptome, leveraging data from different cancer tissues to build a trans-tissue model, and suggest how to integrate information from multiple cancers to provide a foundation for further research.
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Affiliation(s)
- Hongyang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA.
| | - Omer Siddiqui
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA
| | - Hongjiu Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA. .,Department of Internal Medicine, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA.
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45
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Hatje K, Mühlhausen S, Simm D, Kollmar M. The Protein-Coding Human Genome: Annotating High-Hanging Fruits. Bioessays 2019; 41:e1900066. [PMID: 31544971 DOI: 10.1002/bies.201900066] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 08/07/2019] [Indexed: 12/19/2022]
Abstract
The major transcript variants of human protein-coding genes are annotated to a certain degree of accuracy combining manual curation, transcript data, and proteomics evidence. However, there is considerable disagreement on the annotation of about 2000 genes-they can be protein-coding, noncoding, or pseudogenes-and on the annotation of most of the predicted alternative transcripts. Pure transcriptome mapping approaches seem to be limited in discriminating functional expression from noise. These limitations have partially been overcome by dedicated algorithms to detect alternative spliced micro-exons and wobble splice variants. Recently, knowledge about splice mechanism and protein structure are incorporated into an algorithm to predict neighboring homologous exons, often spliced in a mutually exclusive manner. Predicted exons are evaluated by transcript data, structural compatibility, and evolutionary conservation, revealing hundreds of novel coding exons and splice mechanism re-assignments. The emerging human pan-genome is necessitating distinctive annotations incorporating differences between individuals and between populations.
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Affiliation(s)
- Klas Hatje
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstr. 124, 4070, Basel, Switzerland
| | - Stefanie Mühlhausen
- Group Systems Biology of Motor Proteins, Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany
| | - Dominic Simm
- Group Systems Biology of Motor Proteins, Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany.,Theoretical Computer Science and Algorithmic Methods, Institute of Computer Science, Georg-August-University Göttingen, Goldschmidtstr. 7, 37077, Göttingen, Germany
| | - Martin Kollmar
- Group Systems Biology of Motor Proteins, Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany
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46
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Wang D, Eraslan B, Wieland T, Hallström B, Hopf T, Zolg DP, Zecha J, Asplund A, Li LH, Meng C, Frejno M, Schmidt T, Schnatbaum K, Wilhelm M, Ponten F, Uhlen M, Gagneur J, Hahne H, Kuster B. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol Syst Biol 2019; 15:e8503. [PMID: 30777892 PMCID: PMC6379049 DOI: 10.15252/msb.20188503] [Citation(s) in RCA: 404] [Impact Index Per Article: 80.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 01/01/2019] [Accepted: 01/08/2019] [Indexed: 11/28/2022] Open
Abstract
Genome-, transcriptome- and proteome-wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein-level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs, that few proteins show tissue-specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.
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Affiliation(s)
- Dongxue Wang
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Basak Eraslan
- Computational Biology, Department of Informatics, Technical University of Munich, Garching bei München, Germany
- Department of Biochemistry, Quantitative Biosciences Munich, Gene Center, Ludwig Maximilian Universität, München, Germany
| | | | - Björn Hallström
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | | | - Daniel Paul Zolg
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Jana Zecha
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Anna Asplund
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Li-Hua Li
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Chen Meng
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Martin Frejno
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Tobias Schmidt
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | | | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Frederik Ponten
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Julien Gagneur
- Computational Biology, Department of Informatics, Technical University of Munich, Garching bei München, Germany
| | | | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
- Center for Integrated Protein Science Munich (CIPSM), Munich, Germany
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