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The data provided here are global proteome data and fractional synthetic rate (FSR) estimates of proteins from the liver and mammary generated when female ICR mice were fed either a high fat (HF) or control (CON) diet from five weeks of age, through gestation, and lactation. Maternal nutrition influences metabolic adaptations to lactation. The dam&rsquo;s ability to effectively metabolically transition from a non-lactating to a lactating state is dependent upon her diet and environment prior to and during gestation and lactation. Previous data from our lab demonstrated female ICR mice fed a HF diet (60% kcal from fat) for four weeks starting prior to breeding and through gestation, weighed more, had higher percent body fat, and their feeding behavior was altered compared to mice fed a control diet (10% of kcal from fat) (Teeple et al., 2023a,b). Mice fed a HF diet had heavier litters, and litter weight correlated with lighter liver and heavier mammary weights (Teeple et al., 2023b). During lactation, HF mice produced milk that had higher levels of fat and protein (Chen et al., 2017), lower levels of a marker of DNA damage, 8-OHdG, but higher levels of a marker of lipid peroxidation, MDA (Teeple et al., 2023b). Taken together, HF diet can have positive implications on litter weight, which in turn, may be related to organ size and increased milk components. Given the phenotypic responses of dams to HF diet, our overarching objective was to determine maternal adaptation of the global proteome and protein FSR to HF diet in the liver and mammary gland. Our specific objectives were 1) to determine if protein abundance was related to FSR and &nbsp;2) identify differentially abundant proteins and proteins with different FSR in the liver and mammary gland of mice fed a HF or CON diet. This study was part of a larger study (Teeple et al., 2023a,b), and a subset of ten mice (n=5 CON and n=5 HF) were used for this study. Ten female ICR mice arrived at Purdue at 3 weeks of age, acclimated to their environment for 2 weeks, then started receiving the treatment diets at 5 weeks of age. Mice received treatment diets from 5 weeks of age through day 12 of lactation. On day 11 of lactation, deuterium oxide (D2O; 20 &micro;L/g of body weight IP) was administered and added to drinking water at 4% (v/v) to metabolically label proteins. On d 12 of lactation, animals were euthanized and the liver and abdominal mammary gland were collected for global proteome analysis using liquid chromatography mass spectrometry. In addition, a blood sample was collected and used to measure the level of D2O enrichment in serum to contribute to the protein FSR calculations. In the liver, FSR of 80% of proteins was decreased due to HF diet compared to CON. High fat diet fed mice demonstrated increased abundance and FSR of ketogenic and gluconeogenic enzymes in liver, indicating higher production of ketones and glucose. The liver of HF diet fed mice also had a lower abundance of mTOR, which may be in response to higher ketone levels inhibiting mTOR activity. In the mammary gland, HF diet fed mice had higher abundance of ribosomal proteins supporting greater milk production capacity compared to CON fed mice. Together findings increase understanding of impacts of diets on&nbsp;maternal metabolism and milk production during lactation and expand the general understanding of how HF diet impacts metabolic pathways and proteostatic processes. The R code used to calculate the FSR of proteins can be found using the Github link: <a href="https://github.itap.purdue.edu/nlichti/Beckett_et_al_2026-HF-Diet-FSR-code" style="color:#467886; text-decoration:underline">https://github.itap.purdue.edu/nlichti/Beckett_et_al_2026-HF-Diet-FSR-code</a>. These data (FSR data) are presented in tables S6 through S9.&nbsp; List of Figures<a name="_Hlk200966775"></a> Figure S1: A) Correlation matrix of raw label free quantification (LFQ) intensity values from MaxQuant for liver tissue from mice fed either a high fat (HF) or control (CON) diet. B) The percentage of raw LFQ intensity values (non-zero values) for liver of HF and CON fed mice. C) The number of proteins with non-zero raw LFQ intensity values for liver of HF and CON fed mice. Figure S2: A) Correlation matrix of raw label free quantification (LFQ) intensity values from MaxQuant for mammary gland tissue from mice fed either a high fat (HF) or control (CON) diet. B) The percentage of raw LFQ intensity values (non-zero values) for mammary gland of HF and CON fed mice. C) The number of proteins with non-zero raw LFQ intensity values for mammary gland of HF and CON fed mice. Figure S3: Workflow to calculate fractional synthetic rate of proteins from raw liquid chromatography mass spectrometry files from MaxQuant. The files needed are the mass isotopomer distribution of peptides (allpeptides.txt) and protein group ids corresponding to peptides (msms.txt), and a table giving labeling times and body water enrichment values for each sample.&nbsp; Figure S4: Estimator performance for protein Q9Z2N8 (characterized by one peptide sequence, LIANNTTVER), using 3 biological replicates at 1 time point.&nbsp; Results are based on 200 simulated datasets at each combination of fractional synthesis rate (<m:r></m:r><m:omath><m:r>k</m:r></m:omath><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAZCAMAAAAysD/9AAAAAXNSR0IArs4c6QAAAFpQTFRFAAAAAAAAAAA6AABmADqQAGa2OgAAOjo6OjqQOmaQOpDbZgAAZmaQZrbbZrb/kDoAkNv/tmYAtmY6tpA6tv//25A625Bm2//b2////7Zm/9uQ/9u2//+2///bL7tawAAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAEnQAABJ0Ad5mH3gAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAaUlEQVQoU6WPWxaAIAhEoeyllVlZabr/babmkQXIF8M9zABAfWlETi4XriR0s5FQ7CnCy46A7Tn4HXGJIxP3TZN6UO0Jdvg9vGT3MWYLJ5BSDfIAM4mRJTbuB/rOAToRIr3Eia6o/z05fIouBEq6fNpTAAAAAElFTkSuQmCC" style="width:10px; height:20px" />, per unit time) and missing data rate. (A) Average estimation errors unbiased estimates at <m:r></m:r><m:omath><m:r>k</m:r><m:r>&le;3</m:r></m:omath><img src="data:image/png;base64,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" style="width:39px; height:20px" />, with increasingly positive biases at <m:r></m:r><m:omath><m:r>k</m:r><m:r>&gt;4</m:r></m:omath><img src="data:image/png;base64,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" style="width:39px; height:20px" />.&nbsp; At such high rates, the fraction of labeled peptide approaches 100% and accurate estimation becomes difficult. (B) Estimates are most precise at <m:r></m:r><m:omath><m:r>k</m:r><m:r>&asymp;1</m:r></m:omath><img src="data:image/png;base64,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" style="width:39px; height:20px" />&nbsp;(inset highlights curves for tolerances under &plusmn;5%.), and tolerances must be increased to maintain 95% coverage at higher or lower rates. (C) Algorithmic convergence and nominal 95% interval coverage are maintained up to at least <m:r></m:r><m:omath><m:r>k</m:r><m:r>=4</m:r></m:omath><img src="data:image/png;base64,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" style="width:39px; height:20px" />&nbsp;(lines represent different missing data percentages).&nbsp; As expected, randomly missing data points do not influence bias (A) and reduce precision in a way that is consistent with their effect on sample size (B).&nbsp; List of Tables Table S1: Percent deuterium oxide (D2O) enrichment in female ICR mice fed a high fat or control diet. Values were used as P in equation for protein fractional synthetic rate calculations. Table S2: MaxQuant output for global proteome data of liver of female ICR mice fed high fat (HF) or control (CON) diets. Table S3: MaxQuant output for global proteome data of mammary gland of female ICR mice fed high fat (HF) or control (CON) diets.&nbsp; Table S4. Log base 2 of label free quantification (LFQ) intensity of proteins in the liver found in 3 of 5 control (CON) or high fat (HF) groups used for downstream analysis, with 21.6106 a permuted value for measures of zero (0). Table S5. Log base 2 of label free quantification (LFQ) intensity of proteins in the mammary gland found in 3 of 5 control (CON) or high fat (HF) groups used for downstream analysis, with 21.6106 a permuted value for measures of zero (0). Table S6: Fractional synthetic rate estimates (estimate), standard error (se), and confidence interval (lower bound of 2.5% and upper bound 97.5%) for proteins in liver of female ICR mice in high fat (HF) or control (CON) diets.&nbsp; Table S7: Fractional synthetic rate estimates (estimate), standard error (se), and confidence interval (lower bound of 2.5% and upper bound 97.5%) for proteins in mammary gland of female ICR mice in high fat (HF) or control (CON) diets.&nbsp; Table S8: Differential FSR calculations of liver samples between high fat (HF) and control (CON) fed mice.&nbsp; Table S9. Differential FSR calculations of mammary gland between high fat (HF)and control (CON) fed mice. &nbsp; References Chen Y, Wang J, Yang S, Utturkar S, Crodian J, Cummings S, et al. Effect of high-fat diet on secreted milk transcriptome in midlactation mice. Physiol Genomics. 2017;49: 747&ndash;762. Teeple K, Rajput P, Gonzalez M, Han-Hallett Y, Fern&aacute;ndez-Juricic E, Casey T. High fat diet induces obesity, alters eating pattern and disrupts corticosterone circadian rhythms in female ICR mice. PLoS One. 2023a;18: e0279209. Teeple K, Rajput P, Scinto S, Schoonmaker J, Davis C, Dinn M, et al. Impact of high-fat diet and exposure to constant light on reproductive competence of female ICR mice. Biol Open. 2023b;12. doi:10.1242/bio.060088 &nbsp;

应用场景：

The impact of high fat diet on global protein abundance and fractional synthetic rate in liver and mammary gland of peak lactation ICR mice