ArticleNature communications2018
Integrated omics dissection of proteome dynamics during cardiac remodeling.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Practical guidelines for rigor and reproducibility in preclinical and clinical studies on cardioprotection.Basic research in cardiology · 2018Guideline
- Consensus statement on mass spectrometry-based proteomic analysis of cardiac tissue.Nature cardiovascular research · 2026Article
- Longevity of cardiac and skeletal muscle proteins is dependent on tissue and subcellular compartmentation patterns.Cell reports · 2026Article
- Multi-omic analysis of human PHACTR1 signaling networks.Communications biology · 2026Article
- HDAC5 Inhibition as a Therapeutic Strategy for Titin Deficiency-Induced Cardiac Remodeling: Insights from Human iPSC Models.Medicines (Basel, Switzerland) · 2025Article
- Turnover Rates and Numbers of Exchangeable Hydrogens in Deuterated Water Labeled Samples.International journal of molecular sciences · 2025Article
- Article
- Improved Method to Determine Protein Turnover Rates with Heavy Water Labeling by Mass Isotopomer Ratio Selection.Journal of proteome research · 2025Article
- An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions.bioRxiv : the preprint server for biology · 2024Article
- Myocardial Infarction Suppresses Protein Synthesis and Causes Decoupling of Transcription and Translation.JACC. Basic to translational science · 2024Article
- Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions.Nature communications · 2024Article
- Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions.bioRxiv : the preprint server for biology · 2024Article
- Engineering extracellular vesicles for targeted therapeutics in cardiovascular disease.Frontiers in cardiovascular medicine · 2024Review
- Cardiac Development at a Single-Cell Resolution.Advances in experimental medicine and biology · 2024Article
- GSTM2 alleviates heart failure by inhibiting DNA damage in cardiomyocytes.Cell & bioscience · 2023Article
- Identification and functional analysis of senescent cells in the cardiovascular system using omics approaches.American journal of physiology. Heart and circulatory physiology · 2023Review
- MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases.Cell reports methods · 2023Article
- Determining and interpreting protein lifetimes in mammalian tissues.Trends in biochemical sciences · 2023Review
- Review
- Beta-blocker/ACE inhibitor therapy differentially impacts the steady state signaling landscape of failing and non-failing hearts.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Transcript abundance and protein abundance show modest correlation in many biological models, but how this impacts disease signature discovery in omics experiments is rarely explored. Here we report an integrated omics approach, incorporating measurements of transcript abundance, protein abundance, and protein turnover to map the landscape of proteome remodeling in a mouse model of pathological cardiac hypertrophy. Analyzing the hypertrophy signatures that are reproducibly discovered from each omics data type across six genetic strains of mice, we find that the integration of transcript abundance, protein abundance, and protein turnover data leads to 75% gain in discovered disease gene candidates. Moreover, the inclusion of protein turnover measurements allows discovery of post-transcriptional regulations across diverse pathways, and implicates distinct disease proteins not found in steady-state transcript and protein abundance data. Our results suggest that multi-omics investigations of proteome dynamics provide important insights into disease pathogenesis in vivo.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.