Amino acid turnover -
Many of these proteins were identified and quantified across multiple tissues to yield a total of 8, protein half-lives. Substantially more unique proteins 5, and peptides 76, were identified in this reference set than could be quantified precisely enough to report half-life values Supplementary Table 1.
Quantification generally requires higher data quality than does identification, including for mass spectrometry-based proteomics of very complex samples. The likelihood of determining a peptide half-life strongly correlates with the observed intensity of that peptide Supplementary Fig.
The half-life distributions for the proteins that were quantified in each tissue class are shown in Fig. These half-lives spanned a wide range, from less than one day to hundreds of days. The median half-life for each tissue ranged from 3.
Protein half-lives appear to be largely conserved across tissues, but there are notable differences. The correlation plots in Fig. In contrast, skeletal muscle proteins turn over somewhat more slowly. All three cartilage tissues had similar protein half-lives and substantial overlap of the proteins whose half-lives could be determined, suggesting that protein turnover rates are conserved among similar tissues Supplementary Fig.
This also appears to be largely true for the two muscle tissues; however, we observed that some proteins notably those associated with contractile, metabolic, and regenerative functions turned over faster in intrinsic laryngeal muscle than sternocleidomastoid Supplementary Fig.
Venn diagrams show the number of protein half-lives determined for each tissue blue circles depict tissues listed at the right; red circles depict tissues listed above; purple circles indicate the total number of half-lives for each tissue and the number of proteins with half-lives that are shared between tissues purple intersections.
Most proteins have similar half-lives among tissues, but researchers may be interested in those with statistically significant differences between tissues. ApplE Turnover calculates P -values and fold changes for the half-lives of each protein observed in multiple tissues and allows for easy visualization of the data supporting each difference Fig.
Researchers can use the built-in search box to easily find their protein of interest, and then display all peptide half-lives for that protein to show the distribution and confidence of turnover rates for separate tissues. Figure 4 shows the supporting information for the half-lives reported for Myosin-4 MYH4 , a motor protein and component of the skeletal muscle contractile apparatus.
These data confidently show that MYH4 turns over more slowly in muscle than in mucosa. The calculated half-lives for MYH4 in skeletal muscle and mucosa are indicated by blue and red horizontal lines, respectively.
The resource uncovered protein turnover characteristics that appear unique to blood and its function within the circulatory system and in perfused tissues.
First, the protein half-lives for blood exhibit a clear bimodal distribution Fig. A gene ontology GO analysis—performed on each protein set using the other set as background Supplementary Data 1 —corroborated the plasma designation for the shorter half-life set by identifying extracellular proteins associated with regulation of various catalytic activities, defense response, and humoral immunity; in contrast, the longer half-life set was comprised of intracellular proteins involved in metabolic and biosynthetic processes, along with oxidoreduction and other catalytic functions.
Although the bimodal distribution of protein half-lives in whole blood is satisfactorily explained by distinct turnover rates for circulating plasma and cells, we caution that these half-life estimates may be less precise than those reported for solid tissues.
This is because the model assumes that proteins undergo synthesis, lifetime residence, and degradation solely within the analyzed tissue. This assumption does not hold for blood, however, as plasma proteins are predominantly synthesized in the liver and secreted into circulation; and blood cells arise in bone marrow, enter circulation, and are eventually degraded in the liver and spleen.
Future studies might improve measurement precision and half-life estimates somewhat by fractionating blood prior to proteomic analysis. Even then, additional model development is required to accurately reflect amino acid recycling and the relative amounts of heavy and light isotopic amino acids available in the multiple biologic compartments in which blood proteins are synthesized.
The issue of blood protein synthesis occurring outside of the analyzed tissue also has a small impact upon turnover estimates in the solid tissues. We detected little hemoglobin in these tissues, consistent with effective transcardial perfusion of the vasculature with buffer solution at the time of harvest, but we did identify plasma proteins that presumably entered the tissues by diffusion.
Inspection of the model curve fits for abundant plasma proteins within the solid tissues reveals that some data points lie in an impossible region above the curve representing free lysine turnover.
Supplementary Fig. However, the example in Supplementary Fig. The explanation for this discrepancy between the data and model-predicted maxima is that albumin is not synthesized in skeletal muscle, but exogenously with access to a separate amino acid pool.
Therefore, caution is required when interpreting half-life values for exogenous proteins. Most proteins were found to turn over in a matter of days, but some long-lived proteins have half-lives on the scale of months or years.
These long-lived proteins are of particular relevance to aging and degenerative diseases 25 , 37 , 38 , 39 , Such proteins were identified in all solid tissues but were most prevalent in cartilage. GO analysis of cartilage proteins with half-lives greater than 21 d implicated these proteins in cell adhesion, morphogenesis, and the extracellular matrix Supplementary Data 2.
These GO terms are representative of structural proteins, such as collagens, which are expected to have long half-lives 25 , A notable feature of these long-lived proteins is their relatively poor fit to the three-compartment model, compared to the short-lived proteins Fig.
Our model assumes that all proteins are available for degradation and that degraded proteins are continually being replaced by newly synthesized proteins. However, we observed that a fraction of each long-lived protein was not degraded and remained labeled with Lys8, at least for the d time course of the experiment.
This observation directly impacts the model fit: whereas the time series data appear to form an asymptote in Lys0 incorporation for many long-lived proteins, the model fails to fit this asymptote and continues to rise.
Relative lysine fractions open blue circles and model fits orange curves for peptides from representative short- RAB7A and long-lived COL6A2 proteins. The mean squared error MSE for each model fit is shown to the right of each plot. The model fits well to most peptide data, as illustrated for RAB7A, but fits poorly to long-lived proteins.
In such cases, as shown for COL6A2, the model tends to underestimate Lys0 incorporation at 14 and 30 d, while overestimating it at 60 d see inset. One explanation for this observation is that only a fraction of the long-lived protein is turning over while the remainder is static.
Special consideration should be taken when using the reported half-lives for these long-lived proteins. Such cases are readily identifiable by their long half-lives, poor fits to the model, and consequent wide confidence intervals. A possible biologic contributor to inaccurate half-life estimates for the long-lived proteins in our dataset is late postnatal tissue development.
The mice used in this study were 8—week-old young adults, whereas mice reach full adult maturity around 12 weeks Any tissue growth and maturation during this time would result in the rate of protein synthesis exceeding the rate of decay; however, our model assumes a steady-state in protein abundance.
Such growth will have a negligible effect when modeling proteins that turn over quickly, but a substantial impact on proteins with very slow rates of decay. If the long-lived proteins identified in this study exhibited much greater synthesis than degradation rates or were not being degraded at all at early timepoints, then their reported half-lives are underestimated and, in reality, much longer than as determined by our model.
Another possible explanation is that these proteins undergo a non-exponential degradation, whereby they exhibit faster degradation rates in the first few hours of their molecular lifetimes followed by prolonged periods of relative stability A single protein can be decorated with post-translational modifications PTMs that dramatically alter the function of the protein.
These different forms of a given protein are defined as proteoforms and have substantial biological importance In this study, we were able to identify thousands of peptides containing PTMs and determine their half-lives.
Although the identity of an intact proteoform cannot be definitively determined through the identification of a single peptide, the identification of both a modified and unmodified peptidoform is evidence for two distinct groups of proteoforms: one which contains the PTM and one which does not.
We determined half-lives for 3, peptides that contained PTMs. Of these 3, half-lives, 2, had an additional half-life as an unmodified peptidoform.
All proteoform group comparisons had their P -value and fold change determined by ApplE Turnover. Most PTMs were not found to have a significant influence on protein turnover, but there was a significant difference in 66 cases including acetylation, methylation, phosphorylation, hydroxylation, and carboxylation Supplementary Data 3.
For example, we observed an N-terminal acetylation that appeared to increase the half-life of the protein Peptidylprolyl isomerase A PPIA in both liver and cricoid cartilage Fig. In support of this observation, N-terminal acetylation has previously been shown to influence proteoform half-life The PTM results reported here highlight the ability of ApplE Turnover to report proteoform-specific differences observed in the half-life data.
Half-lives for PPIA-derived peptides in liver and cartilage, plotted as a function of mean squared error of the model fit ; calculated half-lives for PPIA are indicated by horizontal lines. The N-terminal peptide of PPIA with acetylation green exhibits a longer half-life than the non-acetylated form of the same peptide red , in both liver and cartilage.
Other peptides from PPIA blue do not contain the N-terminus and so may be from the modified or unmodified proteoform.
This protein half-life dataset and corresponding ApplE Turnover software serve as a powerful resource for studying fundamental protein turnover, interrogating biological questions, and developing therapeutics.
We find that the turnover rates for individual proteins can vary significantly across tissue classes, consistent with known differences in tissue composition and physiologic function. We also show that anatomically distinct tissues within a given class have largely similar protein turnover rates. As noted, the three-compartment model does not fully accommodate the physiologic complexity of turnover dynamics for blood and long-lived proteins, warranting caution when interpreting their half-life estimates.
Isotopic labeling strategies, such as implemented here, represent a gold standard approach to peptide and protein half-life determination because they allow high-precision measurement of relative abundance with no impact on protein structure.
There are, to our knowledge, no methods or datasets for orthogonal validation of proteome-wide half-lives using an independent assay; consequently, this is not a feature of previous labeling studies. Here, to ensure internal and external consistency and precision of the model-based half-life values generated by ApplE Turnover, we pursued a three-pronged validation approach.
Second, we validated the peptide and protein half-lives output by ApplE Turnover using a previously published software tool, Turnover GUI a Comparison of calculated peptide half-lives in cartilage, generated by ApplE Turnover and Turnover GUI.
b Relative fractions of the three isotopic label combinations Lys0Lys0, Lys0Lys8, Lys8Lys8 of missed cleavage peptides in cartilage, as a function of peptide half-life.
The expected values for each experimental timepoint are plotted as solid curves; the observed data are matched-color open circles.
Note that each of these missed cleavage plots is analogous to the single plot in Fig. Results from other tissues are shown in Supplementary Figs. Third, we conducted further validation of our half-life measurements, as well as assumptions underpinning the three-compartment model, by analyzing peptides containing two lysine residues.
The proteomic data in this resource predominantly arise from peptides containing a single lysine generated by LysC digestion; however, peptides with a missed cleavage contain two lysines and, by extension, one of three isotope label combinations: Lys0Lys0, Lys0Lys8, or Lys8Lys8.
Given that ApplE Turnover excludes these missed cleavage peptides from its training set, we used the three-compartment model to predict the relative abundances of Lys0Lys0, Lys0Lys8, and Lys8Lys8 peptides as a function of peptide half-life at each labeling timepoint.
In summary, the resource of protein half-lives we describe here provides researchers with insight into the dynamics of protein turnover rates for thousands of proteins across several unique tissue types. Methods for determining comprehensive protein turnover rates in mammalian tissue are cost- and time-prohibitive, and the complexity of tissue proteomics coupled with stable isotope labeling creates a challenge for protein half-life determination.
Thus, we have made this resource, including all of the raw and processed data, publicly available, and we have provided a tutorial demonstrating how to access, visualize, and analyze these data Supplementary Note 1. Furthermore, the tools used to process the raw data MetaMorpheus, the FlashLFQ algorithm within mzLib, and ApplE Turnover are written with open-source code to provide analysis transparency and facilitate reproducibility 34 , The ApplE Turnover software tool greatly enhances the benefit of the data in this resource, including future additions by us or others, because it enables protein- and tissue-specific searches along with facile data visualization.
Animal experiments were performed with approval of the Animal Care and Use Committee of the University of Wisconsin School of Medicine and Public Health and complied with all relevant ethical regulations.
NBSGW mice Jackson Laboratory stock 47 were obtained from the Humanized Mouse Core of the University of Wisconsin-Madison. Lys8 food was chosen over the Lys6 option to provide additional separation between the peptide isotopic envelopes, which can improve SILAC quantification.
The mice had previously undergone subrenal graft implantation, followed by explantation, in an unrelated study that involved no systemic manipulations and had no bearing on the current work. The mice were 8—10 weeks old at time zero.
These control mice were raised on a standard unlabeled diet ; Teklad , switched to Silantes unlabeled Lys0 food for two weeks, then switched to the Lys8—SILAC diet at time zero. The mice were sacrificed 14 d after the introduction of the heavy isotopically-labeled food and tissues were compared with the 14 d samples from the primary experiment Supplementary Fig.
Next, liver, skeletal muscle [sternocleidomastoid, intrinsic laryngeal thyroarytenoid-lateral cricoarytenoid complex ], cartilage arytenoid, cricoid, thyroid , and mucosa vocal fold samples were harvested. Sternocleidomastoid and cricoid are reported in the main text as representative skeletal muscle and cartilage, respectively.
Cell lysates were prepared using the FASP method The resulting peptides were small enough to pass through the MWCO filter and collected after centrifugation. Peptides were desalted using C18 Bond Elut OMIX μL pipette tips Agilent and eluted with 0.
Next, 7. A customized fork of MetaMorpheus v. This customized fork allowed for the creation of an averagine 51 model with a modified 13 C abundance of 1. Note that this software is suitable for analysis of data containing peptide mixtures of heavy and light stable-isotope amino acids, commonly referred to as SILAC Stable Isotope Labeling by Amino acids in Cell culture whether performed in cells or in larger organisms.
xml protein database of Mus musculus accessed May 19, with fixed carbamidomethylation of cysteine and variable oxidation of methionine.
All other parameters were maintained at their default values except for the addition of variable Lys8 for mass calibration and G-PTM-D, the specification of the SILAC turnover label for quantification, and the allowance of up to one missed proteolytic cleavage. The specification of the SILAC turnover label allowed for the identification and quantification of partially labeled peptides containing a missed cleavage and both a labeled and unlabeled lysine.
The intensities for these partially labeled peptides are distributed between the fully labeled and fully unlabeled intensities in the output. The heavy and light intensities are used to create a relative abundance of unlabeled new peptide intensity divided by the sum of unlabeled and labeled peptide intensity, ranging from 0 to 1.
ApplE Turnover can readily analyze data from traditional pulse experiments in which a heavy isotope is administered to unlabeled mice at time zero as well as our experimental design in which heavy-labeled mice are administered unlabeled food at time zero. In the current experiment, all unlabeled peptides are newly synthesized because there was no unlabeled lysine available before the unlabeled pulse.
In contrast, labeled peptides represent a combination of new and old synthesis because labeled lysine continued to circulate within the mouse after the introduction of unlabeled lysine. For this analysis, relative abundances that had missing values for either the unlabeled or labeled peptide were discarded.
Peptides were required to have at least six total valid relative abundances, of which at least three were required from a single timepoint. Thus, each peptide half-life value in this resource results from between 6 and 22 biologically independent replicates mice.
Peptides failing to meet these criteria were discarded, accounting for much of the difference between the numbers of identified and quantified peptides and proteins in this study Supplementary Fig. Peptide sequences identified by MetaMorpheus are queried against the original protein database to find every possible protein that each sequence could have originated from, effectively undoing protein parsimony.
This step prevents peptides that originate from multiple proteins from skewing any of the protein half-lives.
ApplE Turnover employs a three-compartment model to accurately account for the recycling of heavy lysine in each tissue Fig. Our implementation of this model utilizes equations 23—28 and the four fitting parameters k s t , k b t , k a0 , and k b i reported by Guan and coworkers We then optimized the computational steps to train the model and fit the data, as explained below.
The three compartments circles each contain a time-dependent ratio of unlabeled blue to labeled red lysine. Unlabeled lysine enters the free amino acids compartment from metabolized food. Protein degradation from the all proteins compartment contributes labeled, as well as some unlabeled, lysine to the free amino acids compartment—amino acid recycling from any individual protein of interest is considered negligible.
The pool of free amino acids is available for synthesis of all proteins, as well as the protein of interest. Finally, lysine is removed from the system as waste to accommodate nutrient influx and maintain a consistent pool of free amino acids. Note that while this schematic depicts the introduction of unlabeled lysine to a system containing labeled lysine consistent with the experiments used to generate this resource , the three-compartment model and ApplE Turnover are equally applicable to experiments in which labeled lysine is introduced to a system containing unlabeled lysine.
Although intended for homogenous tissue, this method should be reasonably effective for heterogeneous tissues with multiple cell types because the aggregate recycling rate of all cell types in each tissue is determined from the observed data, which itself arises from all cell types in the tissue.
Non-linear regression is used to fit the model to the observed data using the mean squared error MSE between the fit and those data as the loss function. Several steps are taken to prevent the model from becoming trapped in a local minimum.
First, all peptides are modeled using a set of default parameters and their approximate half-lives are determined. Peptides are then sorted by these provisional half-lives and peptides within the inner quartile range are selected for training.
This step helps to prevent training on peptides that originate from sources other than the tissue of interest, such as short half-life blood proteins and poorly-behaved long-lived extracellular matrix proteins. These inner-quartile peptides are sorted by the number of valid values and the sum of all labeled and unlabeled intensities.
The top peptides are then used as the initial training set to fit the model. If there are fewer than peptides available, then all peptides are used for training. A new model is fit to each training peptide by moving each of the three tissue-specific coefficients k s t , k b t , and k a0 individually in small steps until the MSE of the fit no longer decreases.
At each step, a coefficient representing the peptide half-life k b i is also shifted until the MSE no longer decreases for that specific k s t , k b t , or k a0 step.
We discovered that k b i must be optimized at each step or else a sawtooth behavior occurs, which prematurely stops the nonlinear regression in an artificial local minimum.
Once each training peptide has its own set of optimized coefficients, the median value for each tissue-specific coefficient k s t , k b t , and k a0 across the training peptides is saved and used as the starting value for a second round of fitting. In this second round, all training peptides are fit together instead of separately, such that the MSE is the average of all peptide fits.
The k b i remains unique to each peptide, but the k s t , k b t , and k a0 coefficients are forced to be consistent across all peptides. The resulting coefficients are then used as the starting values for a third round of training, this time using the complete set of peptides with half-lives within the inner quartile range.
Such messy peptides were discarded for the current analysis. These peptides are typically contaminants, have low intensity, or are false-positive identifications. After the removal of messy peptides, the model is fit a final time using the set of remaining inner-quartile peptides.
Next, a grid analysis is implemented as follows to check that the coefficients yield a global, rather than a local, minimum. Each coefficient is modified by a factor of 0. There are 12 modification factors and three coefficients, requiring analyses. For each set of coefficients, the k b i is optimized for each peptide and the MSE is recorded.
If the MSE of the modified set is lower than the MSE of the original set, then the modified coefficients are optimized to reduce MSE and additional rounds of shotgun analysis are used to check for a global minimum.
The global coefficients determined from the training set are then applied to every peptide in the tissue under analysis to determine their respective half-lives. Each peptide half-life value is obtained by a nonlinear regression fit of its 6—22 biologically independent observations to the model e.
Confidence intervals for these peptide half-lives are generated using a hybrid of the Monte-Carlo and bootstrapping methods.
First, the sample standard deviation of the model error for each timepoint of each peptide is calculated using Eq. If only one ratio exists for a given timepoint, then the standard deviation is substituted by the absolute difference between the fit and the observed ratio.
In the current analysis, we used simulations to model the half-life error for each peptide. For each simulation, the observed ratios are sampled with replacement until the number of simulated ratios for each timepoint matches the original data. These sampled ratios are then modified using the inverse of a normal distribution in combination with a random number generator and the sample standard deviation for each simulated ratio.
Finally, these simulated data are are fit using the tissue-specific coefficients to determine the simulated half-lives. Protein half-lives and confidence intervals e.
All simulations from each peptide of a given protein are pooled, and the median half-life of these simulations is reported as the half-life of the corresponding protein.
ApplE Turnover additionally compares between tissues when multiple input files tissues are provided e. The P -value is calculated by normalizing the observed ratios for all timepoints and implementing a two-tailed t -test.
Normalization is achieved by finding the average k b i for the protein in both tissues and creating a normalization timepoint, which is a theoretical timepoint at which both tissues have relative fractions close to 0.
Ratios are then normalized by subtracting the theoretical ratio at the current timepoint and adding the theoretical ratio at the normalization timepoint. This process maintains the standard deviation of the ratios and allows for a consistent comparison across tissues.
In addition to comparing protein turnover rates across tissues, this method is also used to determine differences in proteoform half-lives caused by PTMs.
Despite the multiple training rounds, numerous analyses to check for a global minimum, and calculation of confidence intervals, the processing of our data in ApplE Turnover required only three minutes per tissue on a quad-core computer.
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Curr Opin Clin Nutr Metab Care — Download references. The work was supported by the Gideon Lang Research Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. School of Environmental and Life Sciences, University of Newcastle, Callaghan, Australia.
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Dunstan, R. et al. Modelling of protein turnover provides insight for metabolic demands on those specific amino acids utilised at disproportionately faster rates than other amino acids.
Amino Acids 51 , — Download citation. Received : 18 October Accepted : 09 April Published : 26 April Issue Date : 01 June Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Download PDF. Abstract The nitrogen balance is regulated by factors such as diet, physical activity, age, pathogenic challenges, and climatic conditions.
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Introduction The study of nitrogen balance has been extensively researched, revealing complex relationships between various pools of metabolites that act to maintain metabolic support for homeostasis and exercise activities el-Khoury et al. Table 1 Summary of the proteinogenic and metabolic utilisation profiles for those amino acids identified as being utilised at disproportionately faster rates than other amino acids in humans Full size table.
Methods The general interactions and flow of protein nitrogen have been well summarised by Tessari Table 2 Average percentage relative abundance compositions of selected amino acids in human protein composition Table A1 and dietary sources Table A1 Full size table.
Table 3 Summary of the parameters used in the modelling of amino acid fluxes based on protein intake, turnover, metabolism, and excretion Full size table. Results and discussion A simplistic model was developed utilising published rates of protein intake, oxidation, protein synthesis, and excretion, to investigate the nitrogen balance in terms of the throughput of individual amino acids.
Table 4 Daily utilisation of protein resources for a 70 kg individual has been calculated by assuming a protein intake of 1. Table 5 Predicted levels of amino acids for a 70 kg male and a 70 kg female with a protein intake set at 1.
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Turnovee Biolo, Turnovwr. Declan Fleming, Sergio P. Amino acid turnover, Thuan T. Nguyen, David N. Herndon, Robert R. Amino acid turnover have investigated Immune-boosting herbs relationships between the rates of furnover protein synthesis and degradation and of transmembrane transport of selected amino acids in leg skeletal muscle of 19 severely burned patients and 18 normal controls in the postabsorptive state. Methods were based on the leg arteriovenous balance technique in combination with biopsies of the vastus lateralis muscle and infusions of isotopic tracers of amino acids. Amino acid turnover major processes discussed below are protein turnover degradation and synthesisdegradation turniver urea, or Pomegranate health benefits into glucose gluconeogenesis, Tufnover Amino acid turnover. Wound healing diet protein turnover is a Turonver process characterized by a double flux of amino Amlno the amino acids released by endogenous body protein breakdown can be reutilized and reconverted to Amino acid turnover synthesis, tkrnover very little loss. Daily rates of protein turnover in humans to g per day are largely in excess of the level of protein intake 50 to 80 g per day. A fast growing rate, as in premature babies or in children recovering from malnutrition, leads to a high protein turnover rate and a high protein and energy requirement. Protein metabolism synthesis and breakdown is an energy-requiring process, dependent upon endogenous ATP supply. Metabolism of proteins cannot be disconnected from that of energy since energy balance influences net protein utilization, and since protein intake has an important effect on postprandial thermogenesis - more important than that of fats or carbohydrates.
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