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      In utero pulse injection of isotopic amino acids quantifies protein turnover rates during murine fetal development

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          Summary

          Protein translational control is critical for ensuring that the fetus develops correctly and that necessary organs and tissues are formed and functional. We developed an in utero method to quantify tissue-specific protein dynamics by monitoring amino acid incorporation into the proteome after pulse injection. Fetuses of pregnant mice were injected with isotopically labeled lysine and arginine via the vitelline vein at various embyonic days, and organs and tissues were harvested. By analyzing the nascent proteome, unique signatures of each tissue were identified by hierarchical clustering. In addition, the quantified proteome-wide turnover rates were calculated between 3.81E−5 and 0.424 h −1. We observed similar protein turnover profiles for analyzed organs (e.g., liver vs. brain); however, their distributions of turnover rates vary significantly. The translational kinetic profiles of developing organs displayed differentially expressed protein pathways and synthesis rates, which correlated with known physiological changes during mouse development.

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          Highlights

          • We report an in utero amino acid pulse injection method

          • This method can be used to study protein turnover in fetal development

          • Tissue-specific protein dynamics can be studied for the nascent proteome

          • Protein turnover can be monitored at various gestational stages

          Motivation

          Protein translational control is a highly regulated step in the gene expression program during mammalian development; however, quantitative techniques to monitor protein synthesis rates in a developing fetus ( in utero) are limited. Here, we developed an in utero stable isotope labeling approach to quantify tissue-specific protein dynamics of the nascent proteome during mouse fetal development.

          Abstract

          Baeza et al. report an in utero method to monitor amino acid incorporation into the proteome after pulse injection. This method enables quantification of the nascent proteome during various gestational stages in a tissue-specific manner and provides insights into fetal proteome turnover.

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          Most cited references61

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          MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

          Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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            Global quantification of mammalian gene expression control.

            Gene expression is a multistep process that involves the transcription, translation and turnover of messenger RNAs and proteins. Although it is one of the most fundamental processes of life, the entire cascade has never been quantified on a genome-wide scale. Here we simultaneously measured absolute mRNA and protein abundance and turnover by parallel metabolic pulse labelling for more than 5,000 genes in mammalian cells. Whereas mRNA and protein levels correlated better than previously thought, corresponding half-lives showed no correlation. Using a quantitative model we have obtained the first genome-scale prediction of synthesis rates of mRNAs and proteins. We find that the cellular abundance of proteins is predominantly controlled at the level of translation. Genes with similar combinations of mRNA and protein stability shared functional properties, indicating that half-lives evolved under energetic and dynamic constraints. Quantitative information about all stages of gene expression provides a rich resource and helps to provide a greater understanding of the underlying design principles.
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              Semi-supervised learning for peptide identification from shotgun proteomics datasets.

              Shotgun proteomics uses liquid chromatography-tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of confident peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic Saccharomyces cerevisiae dataset, and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach.
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                Author and article information

                Contributors
                Journal
                Cell Rep Methods
                Cell Rep Methods
                Cell Reports Methods
                Elsevier
                2667-2375
                26 February 2024
                26 February 2024
                26 February 2024
                : 4
                : 2
                : 100713
                Affiliations
                [1 ]Department of Biochemistry & Biophysics, University of Pennsylvania, Philadelphia, PA 19104, USA
                [2 ]The Center for Fetal Research, Division of Pediatric General, Thoracis and Fetal Surgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
                [3 ]Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, MO 63110, USA
                Author notes
                []Corresponding author peranteauw@ 123456chop.edu
                [∗∗ ]Corresponding author bagarcia@ 123456wustl.edu
                [4]

                These authors contributed equally

                [5]

                Lead contact

                Article
                S2667-2375(24)00028-6 100713
                10.1016/j.crmeth.2024.100713
                10921036
                38412836
                cf71430e-a340-407c-bef6-631c655aa771
                © 2024 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 12 May 2023
                : 20 December 2023
                : 29 January 2024
                Categories
                Article

                fetal development,in utero injection,proteomics,protein turnover,pulse labeling

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