DIET IMPACTS PREIMPLANTATION HISTOTROPH PROTEOME IN

Subscriber access provided by Kaohsiung Medical University

Article

DIET IMPACTS PREIMPLANTATION HISTOTROPH PROTEOME IN BEEF CATTLE KaLynn Harlow, Emily Taylor, Theresa Casey, Victoria Hedrick, Tiago Sobreira, Uma K. Aryal, Ronald P Lemenager, Bethany Funnell, and Kara Stewart J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00077 • Publication Date (Web): 03 May 2018 Downloaded from http://pubs.acs.org on May 5, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Proteome Research

DIET IMPACTS PREIMPLANTATION HISTOTROPH PROTEOME IN BEEF CATTLE

KaLynn Harlowa; Emily Taylora; Theresa Caseya; Victoria Hedrickb; Tiago Sobreirab; Uma K. Aryalb; Ronald P Lemenagera; Bethany Funnellc; Kara Stewarta,*

a

Department of Animal Sciences, College of Agriculture, Purdue University; bBindley

Bioscience Center – Purdue University; cDepartment of Veterinary Clinical Sciences, College of Veterinary Medicine, Purdue University

Corresponding Author. Dr. Kara Stewart Creighton Hall West Lafayette, IN 47907 [email protected] 765-496-6199 KEY WORDS proteome, histotroph, cow, nutrition, liquid chromatography-tandem mass spectrometry (LC-MS/MS)

1 ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT In ruminants, the period from fertilization to implantation is relatively prolonged, and survival of embryos depends on uterine secretions, or histotroph. Our objective was to determine if prebreeding diet affected histotroph proteome in beef cattle. Cows were assigned to 1 of 4 diets: control (CON), high protein (PROT), high fat (OIL), or high protein and fat (PROT+OIL). After 185d on diets, an intravaginal progesterone implant (CIDR) was inserted for 7 days. At 9 days post CIDR removal, animals with a corpus luteum were selected (n = 16; 4/treatment). Proteins were isolated from histotroph collected by uterine lavage and analyzed with LC-MS/MS. Over 2000 proteins were expressed (n ≥ 3 cows/treatment), with 1239 proteins common among every group. There were 20, 37, 85, and 123 proteins unique to CON, PROT+OIL, PROT, and OIL, respectively. Relative to CON, 23, 14, and 51 proteins were differentially expressed in PROT+OIL, PROT, and OIL, respectively. Functional analysis found that 53% of histotroph proteins were categorized as extracellular exosome, 3.28% as cell-cell adhesion, and 17.4% in KEGG metabolic pathways. Differences in proteomes among treatments support that prebreeding diet affects histotroph. Understanding the impact of diet on histotroph proteins may help to improve conception rates.


Page 2 of 43

Page 3 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


INTRODUCTION Reproductive efficiency is a key determinant of beef cow profitability as calves are their sole output 1. Nutrient intake, before and after calving, is a major factor affecting the duration of subsequent calving-to-conception interval and overall pregnancy rate 1. Although prepartum nutrition plays a key role in regulating the interval to resumption of postpartum estrous cycling, mainly through its effects on body condition and energy balance, both concurrent plane of nutrition and dietary composition during the breeding season affect conception and pregnancy rates. In cattle, the majority of pregnancy loss is attributed to early embryonic death which occurs before maternal recognition of pregnancy on day 16, with ovulation defined as day 0 2. These differences in conception-pregnancy rates in cattle are postulated to be due to differences in hormonal status; in particular, higher progesterone levels are associated with increased embryonic development and survival 3-4. Exogenous supplementation of heifers with progesterone in early pregnancy (day 3-8) altered the expression of a large number of genes in the endometrium, while induction of low serum progesterone concentration altered the normal temporal changes that occurred in the expression of genes in the endometrium 2, 5. Expression of genes altered by progesterone treatments encoded proteins that were involved in processes that may affect uterine environment. For example, DGAT2 and FABP3 were among the genes with the highest fold change response to progesterone treatments. These genes encode proteins involved in triglyceride synthesis and transport 5. The nutritional state of the dam may also affect early embryonic survival in the preimplantation stage of development. Beef heifers fed a high plane of nutrition, and subsequently offered a sub-maintenance plane of nutrition immediately post- artificial insemination (AI), experienced a 50% reduction in conception rate relative to those maintained



on a high plane of nutrition 6. Nutrient restriction for 6 days immediately following AI resulted in poorer quality embryos that were delayed in stage of development 7. Studies that modified composition of diets but were isocaloric suggest that nutrient source and composition may affect embryo survival. A meta-analysis found feeding feedstuffs high in protein and fat (dried distiller’s grains plus solubles, DDGS) prebreeding improved conception rates with timed artificial insemination (AI) in cattle 8. Fat supplementation alone, and source of fat, was associated with improved conception rate and embryo survival in dairy cows 9-10. The positive effects on reproduction from supplemental fat were associated with increased luteinizing hormone and subsequent ovarian follicle development 11, increased progesterone concentrations from increased circulating cholesterol (precursor molecule to progesterone), or inhibition of prostaglandin synthesis 12. In ruminants, the period from oocyte fertilization to implantation is relatively prolonged. Prior to implantation, secretions from glandular epithelium, known as uterine histotroph, are critical for embryo survival and development 13. The nutrient content of the uterine histotroph increases by at least 3-fold in early pregnancy 14, and contains a combination of saccharides, amino acids, lipids, proteins, growth factors, and cytokines 15. Proteomic analysis of the histotroph is beginning to shed further light on the role of uterine secretions in embryonic survival and conception. For example, proteins in uterine flushes collected on day 16 from pregnant and nonpregnant ewes were measured using liquid chromatography massspectrometry/mass-spectrometry (LC MS/MS), and authors reported on the top 100 histotroph proteins expressed in both groups. Functional analysis of these proteins found 38% were associated with growth and remodeling, 30% with nutrition, 22% with immune system, and 5% with oxidative stress 16. LC-MS/MS proteomic analysis of histotroph collected from cattle found


Page 4 of 43

Page 5 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


it changed significantly between day 7 to day 13 of the preimplantation period. A total of 707 proteins (≥1 unique peptide) were detected across the two days. Functional analysis of high confidence proteins (119 and 129 on day 7 and 13, respectively, with 108 shared between days) showed enrichment in categories related to metabolism (41%), transport (16%), multicellular organismal development (9%), and signal transduction (8%) 13. A total of 40 proteins were identified with high confidence from proteome analysis of uterine flushes from heifers with viable and degenerate embryos seven days after timed AI 17. Nearly two-fold more proteins were detected in flushes from uteri with viable embryos. Proteins were associated with response to stress were found across both treatments; while cellular component assembly, macromolecular complex assembly, protein complex assembly, cytoskeletal organization, and cell cycle were only associated with flushes from uteri with viable embryos 17. We hypothesized that prebreeding diet affects preimplantation embryo growth and survival, and therefore conception rates, by affecting the composition of the histotroph. To test the hypothesis, and to determine the molecular factors associated with histotroph quality and composition, we performed label-free quantitative proteomics analysis of uterine lavages of Angus-Simmental beef cattle taken at ~day 7 of the estrous cycle. Using state-of-the-art LCMS/MS analysis, we identified over 3,500 proteins (present in at least one animal), of which 1239 proteins were commonly detected in all four experimental groups. Histotroph proteins identified in our study were compared with proteins measured by LC MS/MS in uterine flushes collected from cows or sheep at similar reproductive stages for previous studies 13, 16-17. The number of high confidence proteins identified in our study was three fold more than previous studies, and thus provides a comprehensive and robust set of data that describes the maternal contribution to the preimplantation protein environment of the uterus.



MATERIALS AND METHODS The animal portion of this study was conducted at the Purdue Animal Sciences Research and Education Center near West Lafayette, IN. All animals were handled in compliance with procedures approved by the Purdue’s Animal Care and Use Committee. All proteomics sample preparation, LC-MS/MS data collection and data analysis was performed at the Purdue Proteomics Facility, Bindley Bioscience Center. Animals and diets Primiparous and multiparous, Angus-Simmental cows (n = 48; BW = 611 ± 52.4kg; BCS = 5.83 ± .36) were used in a 2 x 2 factorial study design (Figure 1). Cows were placed on diets for ~185d, which encompassed the last trimester and early lactation of their previous pregnancy. All diets were formulated to be isocaloric and either meet or exceed all other nutrient requirements (NRC, 2000). Postpartum average daily gain (ADG) was targeted at 0.77 kg. Ingredient compositions of feed used to formulate diets were obtained by wet chemistry methods (AOAC, 1990) before trial initiation [(Dairy One, Ithaca, NY), and described in Table 1]. All diets included corn stover. Dried distiller’s grains plus solubles (DDGS) is a common feedstuff that is high in both fat and protein. An 8% fat DDGS was used to create a diet that was high in both fat (4.8%) and protein (17.1% CP). In order to maintain some similarities in amino acid and fatty acid profiles, corn by products were then used to create the additional diets. Corn gluten feed was used in the control diet and high protein (17.1%), low fat (2.0%) diet. Mix 30 is a supplement including fat from corn processing and was used to make a high fat (4.8%), low protein (11.9%) diet. Cows were blocked by cow weight, body condition score, and age and randomly assigned to one of four treatments: 1) a silage-based total mixed ration (TMR) (CON);


Page 6 of 43

Page 7 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


2) a TMR high in fat and protein (PROT+OIL); 3) a TMR high in fat and low in protein (OIL); or 4) a TMR high in protein and low in fat (PROT). Cows and calves were housed individually in pens. All dietary treatments were fed as a total mixed ration ad libitum in concrete bunks once daily at 0800. Daily feed delivery adjustments were made based on bunk scores and ingredient dry matter (DM) adjustments were made weekly. Due to bunk design, calves had access to cow diets and may have contributed to dietary dry matter disappearance. Since calves were less than three months of age during the study, it is assumed that their dry matter consumption was minimal. At trial initiation, BW was defined by taking the average of two consecutive preprandial weights. Also, initial BCS (1 = emaciated, 9 = obese; Wagner et al., 1988) was defined by a single investigator one day prior to trial initiation. Uterine histotroph flushing After 185d on respective diets, at which point the animals were 109 ± 17 days postpartum, a controlled internal drug release (CIDR) was inserted into the cow’s vagina for 7 days. Nine days after the CIDR was removed, transrectal ultrasonography of the ovaries was performed. Cows selected (n=4/treatment) for study had a corpus luteum (CL) present on their ovary. A Foley catheter was placed into uterine horn ipsilateral to the CL, and 30 ml of a sterile saline solution was flushed into the uterine horns. The uterus was then massaged via transrectal palpation and the saline was allowed to flow back out of the uterus via the catheter and collected in 15 ml conical tubes. Samples were frozen at -80ºC, freeze dried, and stored at -20ºC. Sample preparation for proteome analysis of uterine histotroph. Freeze-dried histotroph samples were re-suspended in 1 mL of 20 mM Tris-HCl (pH 7.5) containing 100 mM NaCl, 5% glycerol, 1 mM EDTA, and 5 mM dithiothreitol (DTT), and



proteins were precipitated using 5 volume of cold (-20˚C) acetone overnight. Precipitated proteins were collected by centrifugation at 14,000 rpm for 15 min at 4˚C, and pellets were washed once with 80% cold (-20˚C) acetone. After drying briefly in CentriVap Concentrator (Labconco), pellets were dissolved in 40 µl of 8 M urea for 1 h at room temperature. Protein concentration in each sample was determined by bicinchoninic acid (BCA) assay (Pierce BCA Protein Assay Kit; Thermo Fisher Scientific) using BSA as standard, and volume containing 100 µg of total protein was used for digestion. Samples were first reduced with 10 mM DTT at 60 °C for 45 min, and then cysteines alkylated with 20 mM iodoacetamide (IAA) at room temperature in the dark for 45 min. Before digestion, the concentration of urea was brought down to 1.5 M by adding 25 mM ammonium bicarbonate. Digestion was performed at 37˚C overnight using mass spec grade trypsin and Lys-C mix from Promega at a 1:25 (w/w) enzyme-to-substrate ratio. The digested peptides were desalted using Pierce C18 spin columns (Pierce Biotechnology, Rockford, IL) using the protocol provided by the manufacturer. Peptides were eluted using 80% acetonitrile containing 0.1% Formic Acid (FA) and dried in Centrivap. Peptides were resuspended in 50 µl of the loading buffer (3% acetonitrile, 97% water and 0.1% FA), and 2 µl was used to determine peptide concentration in each sample using BCA assay. Peptide sample volume was adjusted to a final concentration of 0.2 µg/µL based on BCA assay and 5 µL (1 µg of total peptide) was loaded to the LC column for LC-MS/MS analysis. LC-MS/MS data collection Samples were analyzed by reverse-phase HPLC-ESI-MS/MS using the Dionex UltiMate 3000 RSLC nano System (Thermo Fisher Scientific) coupled to the Q-Exactive High Field (HF) Hybrid Quadrupole Orbitrap MS (Thermo Fisher Scientific) and a Nano- electrospray Flex ion source (Thermo Fisher Scientific). Purified peptides were loaded onto a trap column (300 µm ID


Page 8 of 43

Page 9 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


× 5 mm) packed with 5 µm 100 Å PepMap C18 medium and washed using a flow rate of 5 µl/minute with 98% purified water/2% acetonitrile (ACN)/0.01% formic acid (FA). The trap column was then switched in-line with the analytical column after 5 minutes. Peptides were separated using a reverse phase Acclaim PepMap RSLC C18 (75 µm x 15 cm) analytical column using a 120-minute method at a flow rate of 300 nl/minute. The analytical column was packed with 2 µm 100 Å PepMap C18 medium (Thermo Fisher Scientific). Mobile phase A consisted of 0.01% FA in water and a mobile phase B consisted of 0.01 % FA in 80% ACN. The linear gradient started at 5% B and reached 30% B in 80 minutes, 45% B in 91 minutes, and 100% B in 93 minutes. The column was held at 100% B for the next 5 minutes before being brought back to 5% B and held for 20 minutes. Sample was injected into the QE HF through the Nanospray Flex™ Ion Source fitted with an emission tip from Thermo Scientific. Column temperature was maintained at 35˚C. MS data were acquired with a Top20 data-dependent MS/MS scan method. The full scan MS spectra were collected over 300-1,650 m/z range with a maximum injection time of 100 milliseconds, a resolution of 120,000 at 200 m/z, spray voltage of 2 and AGC target of 1 ×106. Fragmentation of precursor ions was performed by high-energy C-trap dissociation (HCD) with the normalized collision energy of 27 eV. MS/MS scans were acquired at a resolution of 30,000 at m/z. The dynamic exclusion was set at 20 s to avoid repeated scanning of identical peptides. Instrument optimization and recalibration was carried out at the start of each batch run using Pierce calibration solution. The sensitivity of the instrument was also monitored using E. coli digest at the start of the sample run. LC-MS/MS data analysis MaxQuant software (v. 1.5.5.1) 18-20 with its built-in Andromeda search engine 20 was used for database searches for protein identification and label free MS1 quantitation. The MS/MS spectra



were searched against the Uniprot Bos taurus protein database (downloaded from Uniprot site). The minimal length of seven amino acids was required in the database search. Database search was performed with enzyme specificity for trypsin and Lys-C, allowing up to two missed cleavages. Oxidation of methionine and Acetyl (Protein N-term) were defined as variable modifications, and Iodoethanol was defined as a fixed modification. The ‘unique plus razor peptides’ were used for peptide quantitation. Razor peptides are the non-unique peptides assigned to the protein group with the most other peptides. The false discovery rate (FDR) for peptides and proteins identification was set at 1%. All the LC-MS/MS raw data files were deposited and made publically available through MassIVE website (https://massive.ucsd.edu/) under ID MSV000081991. Many factors can affect protein detection and quantitation by LC-MS/MS including sample complexity and dynamic ranges of protein abundances. To increase the confidence of our identification, a protein was considered “expressed” by a treatment group if it was identified in at least three of the four animals within a treatment group. If only one animal within a group expressed a protein, it was defined as “not specific to treatment,” and if no animals expressed a protein, it was “not expressed.” A protein was considered “inconclusive” if two of the four animals expressed it (Table 2). Statistical Analysis To identify differentially expressed proteins in different treatments relative to the control, we performed analysis of variance (ANOVA) and Tukey HSD (Honestly Significant Difference) as a post hoc test in R (https://www.r-project.org/), using protein LFQ intensities if significance was detected between the control and treatment samples. After Tukey HSD, treatment differences with adjusted-P ≤ 0.1 were considered significant.


Page 10 of 43

Page 11 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Functional analysis of proteome and comparison to previous studies Functional analysis of histotroph proteome was performed using DAVID Bioinformatics Resources 6.8 (NIAID/NIH). Function of proteins described within text was obtained from the UniProt Knowledgebase (UniProtKB; http://www.uniprot.org/help/uniprotkb) 21. Histotroph proteins detected in our study with high confidence were compared with proteins measured in uterine flushes from ruminants at similar reproductive stages using LC-MS/MS in previous studies 13, 16-17. Supplemental files from each study was downloaded and sorted for non-redundant proteins. To allow comparative analysis across the four studies, proteins IDs were converted into ENSEMBL stable gene ID using the Biomart tool (http://useast.ensembl.org/biomart) 22.



Page 12 of 43

RESULTS & DISCUSSION Analytical strategy and LC-MS/MS reproducibility To investigate the consequences of prebreeding diet on histotroph we performed label-free quantitative proteomic analysis of histotroph collected from Angus-Simmental cattle fed on different diets (11.9% protein and 2.0% fat, CON), high protein (17.1% protein and 2.0% fat, PROT), high fat (11.9% protein and 4.8% fat, OIL), or high protein and fat (17.1% protein and 4.8% fat. PROT+OIL). Examining the changes in histotroph proteome in different treatment groups relative to the control may provide clues to the molecular events associated with the effect of diets on histotroph. LC-MS/MS data were acquired on a Thermo Q-Exactive Orbitrap HF mass spectrometer that was directly connected to a Dionex UltiMate 3000 HPLC system. Data were collected using four biological replicates for each treatment condition. Raw data were processed using MaxQuant

18

and its built-in Andromeda search engine

20

. Search results were

filtered at 1% FDR at both peptides and protein levels, and proteins were quantified by label-free MS1 quantitation. Shotgun proteomics produces a robust analysis of peptide segments for protein identification, making it ideal for discovery. However, the stochastic nature of LC-MS/MS can somewhat limit comparing protein expression across conditions

23

. In particular, the label-free

quantitative (LFQ) proteomics approach utilized in this study is more flexible than stable isotope labeling workflows, and, in general, covers a greater area of the proteome at a higher dynamic range 24. However, there are often missing values in LFQ proteomic data because low-abundant peptides are not always fragmented in complex peptide mixtures. In addition, the overall abundance of a peptide is determined by the surroundings of its corresponding cleavage sites, which is influenced by protease cleavage efficiency, and/or some peptides are more easily


Page 13 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


ionized than others 25-27. Stronger signals can overshadow proteins exhibiting less abundance in complex samples

23

, potentially removing these low-signal proteins from detection. Sample

complexity also leads to decreased sensitivity to accommodate most peptide features

28

. To

overcome some of these limitation, quantitative proteomic analysis, generally utilize sample prefractionation to reduce the complexity, and perform repeated analysis using multiple biological and/or technical replicates to determine the LC-MS/MS reproducibility for protein identification and relative quantitation. LC-MS/MS reproducibility is the key factor for successful proteomic analysis. To test reproducibility among technical and biological replicates coefficient of variation (CV) and correlation coefficients were generated using Data Analysis and Extension Tool (DAnTE)

29

.

The average CV of peptide signal (MS1) intensity in 3 technical replicates was ~17.2 % (Figure 2A). Correlation of relative protein abundances in three technical replicates (Figure 2B) was found to be high (r2 = 0.996 between replicate 1 and 2; and r2 = 0.995 between replicate 1 and 3). The reproducibility of LC-MS analysis was evaluated by determining correlation between the biological replicates using LFQ intensity (Figure 2C), and again, there was strong a correlation between the biological replicates. These results confirmed high reproducibility of our analysis.

Global analysis of histotroph proteome common across all treatments The model used in this study focused on the dam’s contribution to uterine environment in the preimplantation period. Comparison of endometrial gene expression and histotroph protein expression between pregnant and non-pregnant cattle on the same days of estrous found no differences between groups until pregnancy recognition on Day 15-16 30-31. These findings support that histotroph composition during the preimplantation period is independent of the



presence of a conceptus before pregnancy recognition 13, 30. For our study, nine days after CIDR removal, animals were ultrasounded to select those with CL for histotroph recovery. Using this approach, we estimate that animals were approximately at Day 6-7 of the estrous cycle when uterine lavages were done, and therefore are representative of the histotroph in the preimplantation period of cattle. There was no difference in final body condition scores and no difference in final weights of cows on differing diets (P = 0.3842 and P = 0.3326, respectively). Therefore, any proteome differences between treatments was due to differences in diet since all diets were formulated to be isocaloric. Proteomic analysis across the entire group (n=16) resulted in identification of 3586 proteins in at least one cow. Of those, 2013 proteins were considered expressed in at least one treatment (n≥3/treatment), and 1239 proteins were common to all treatments (n≥3 in all four treatments; Figure 3A; Supporting Table S-1). The 1239 protein IDs common to all treatments were submitted to DAVID Bioinformatics Resources 6.8 for functional analysis and were mapped to 1190 DAVID IDs. This yielded 1080 ID (90.80%) sorted into gene ontology (GO) Biological Process categories (Supporting Table S-2), 1133 IDs (95.20%) sorted into GO Cellular Component categories (Supporting Table S-3), and 731 IDs (61.40%) sorted into KEGG Pathways (Supporting Table S-4). Within cellular component over 50% of histotroph proteins (629 of expressed protein) were categorized as being associated with Extracellular exosome (Table 3), and 3.29% (39) of histotroph proteins enriched the GO Biological Process Cell-cell adhesion (Table 3). Over 200 (17.40%) histotroph proteins common across all treatments were clustered in KEGG pathway Metabolic pathways. Breaking metabolic pathways down further, Biosynthesis of amino acids (30 proteins), TCA Cycle (17 proteins; Figure 3B),


Page 14 of 43

Page 15 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Glycolysis/Glycogenolysis (23 protein), Fatty acid degradation (17 proteins), and Fatty acid metabolism (15 proteins) were highly enriched with histotroph proteins common across all treatments (Table 3). It is intriguing that over half the proteins were categorized as being exosomal within the cellular compartment gene ontology and suggests that exosomes play a critical role in the delivery of nutrients and energy, and potentially information from the dam to the embryo. Exosomes are a type of extravesicles (EV), which are released by cells into the extracellular environment, and can serve as vehicles for the transfer of proteins, lipids and RNAs between cells both locally (autocrine and paracrine) and remotely 32. Exosomes are formed in cells by inward budding of late endosomes, called multi-vesicular bodies, and are then released into the extracellular environment by fusion of the multi-vesicular body with the plasma membrane 33

. Proteins on the surface of EVs promote interaction with other cells through adhesion of the

vesicles to lipids and ligands on the surface of the recipient cell, internalization of the whole vesicle into recipient cells, or fusion of the EV membrane with the plasma membrane of the recipient cell 34. EVs are present in reproductive fluids, which supports possible roles for them in the intercellular communication involved in conception 32. Successful implantation is dependent on coordination between the embryo and the endometrium, and EVs are believed to play a role in this process 32. In particular, it has been suggested that the endometrial epithelium release EVs that are involved in the transfer of signaling miRNAs and adhesion molecules either to the blastocyst or to the adjacent endometrium into the uterine cavity, which in turn affect endometrial receptivity and implantation. Of the top 100 proteins found associated with exosomes, and thus listed as exosomal markers on the Exocarta website 35, 81 were present in our dataset. Included in these proteins



were five RAB proteins, six 14-3-3 proteins, six annexins (including annexin II), and five heat shock proteins (HSP, including HSP90AA1). RAB proteins and 14-3-3 proteins also play roles in vesicle trafficking. Annexins provide membrane scaffold, and are involved in trafficking and organization of vesicles, exocytosis, endocytosis and calcium ion formation36. HSP function by stabilizing new proteins to ensure correct folding or by helping to refold proteins that were damaged during cell stress36. HSP90AA1 was shown to interact with annexin II and tissue plasminogen activator in exosomes to increase plasmin dependent cell motility37. Although not among the HSP in this group, extracellular vesicles isolated from uterine luminal fluid of cyclic and pregnant sheep were found to express HSP70 38, further supporting the potential association of the HSP found in histotroph proteome to be associated with exosomes. MFGE8, milk fat globule EGF factor 8-aka lactadherin, was also among these 81 proteins. MFGE8 is a glycoprotein that is found within exosome and milk fat globule membranes. As a membrane protein MFGE8 promotes recognition of apopototic bodies for phagocytosis 39. It also plays a role in lipid uptake and metabolism following the binding to integrin receptors 40, and may function as immunomodulatory factor 41. Enrichment of histotroph proteins in the cell-cell adhesion category further supports the potential mechanism of maternally derived exosomes, or other extracellular vesicles, functioning to transfer nutrients and other bioactive substances from the dam to the embryo in the preimplantation period. Proteins within this category included two VAMP associated proteins (VAPA and VAPB), which are type IV membrane proteins present in the plasma membrane and intracellular vesicles. VAMP associated proteins function in vesicle trafficking, membrane fusion, protein complex assembly and cell motility. Other proteins found in this group involved in vesicle trafficking were annexin A2, USO1, RAB11B, and HSPA8. Multiple proteins (VASP,


Page 16 of 43

Page 17 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


TWF1, TES, DBNL, CAPG, CAPZ) sorted into the cell-adhesion category were involved in actin filament assembly and capping, processes that are associated with cell motility and vesicle transport. EPCAM and its paralogue TACSTD2 were also found in this cluster. EpCAM functions in homotypic cell adhesion, in that EpCAM on the surface of one cell binds to the EpCAM on a neighboring cell thereby holding the cells together. Proteins that prevent oxidative stress (PARK1, PRDX1, PRDX6) were also clustered in the cell-cell adhesion category. The 143-3 proteins (YWHAE, YWHAZ) function to regulate cellular processes such as metabolism, protein trafficking, signal transduction, apoptosis and cell cycle regulation. Thus, the proteome signature of uterine histotroph common across all treatments supports a receptive uterus is enriched in proteins for maternal-embryo communication (cell-cell adhesion & exosomes), as well as energy and protein production. Impact of diet on histotroph proteome There were 20, 37, 85, and 123 proteins unique to flushes from cows on CON, PROT+OIL, PROT, and OIL diets, respectively (Figure 3A). Relative to the CON group, 23, 14, and 51 proteins were found to be differentially expressed (P-adj ≤ 0.1; Table 4; Figure 4), respectively. With the inclusion of proteins either expressed by a treatment group and not by the control, or vice-versa, the list of differentially expressed proteins expands to 127, 165, and 269 for the PROT+OIL, PROT, and OIL, respectively, relative to the CON group (Table 4). Relative to control, the OIL diet had the greatest effect on the histotroph proteome (Table 4). There were 51 proteins differentially expressed (P-adj ≤ 0.1), with 34 upregulated and 17 downregulated relative to CON (Supporting Table -5). This was expanded to include proteins which were expressed in the OIL group but not the CON group (180 proteins; Supporting Table



S-6), or were expressed in the CON group but not the OIL group (38 proteins; Supporting Table S-7). DAVID functional analysis of the 214 proteins upregulated or unique to OIL relative to control mapped to 205 DAVID IDs; 172 IDs (83.90%) were categorized into gene ontologies within Biological Process (Supporting Table S-8) and 184 IDs (89.80%) into Cellular Component (Supporting Table S-10). Within Cellular Component 40.00% of the proteins were categorized as Extracellular Exosome (Supporting Table S-11), and 2.44% were in Mitochondrial respiratory chain complex I (Supporting Table S-12; Table 5). Proteins sorted into Mitochondrial respiratory chain complex I were all associated with oxidative phosphorylation and the electron transport chain; the majority were subunits of NADH dehydrogenase (Supporting Table S-12). The KEGG Pathway Metabolic pathways was also highly enriched by proteins up-regulated by or unique to OIL diet. Further exploration of proteins in this category found they were involved in the citric acid cycle, catabolism of amino acids, and nutrient recycling (Supporting Table S-14). Within the GO Biological Process, 19 proteins (9.27%) upregulated or unique to the OIL were categorized as involved in Oxidation-Reduction Process (Supporting Table S-9). Most of these proteins were involved in processes associated with energy metabolism. For example, two (NDUFS8, NDUFA6) were subunits of NADH dehydrogenase, and a third (NDUFA4) was cytochrome C oxidase, the terminal component of the electron transport chain. Other proteins within this group function to protect against oxidative stress, such as SOD1, PRDX4, ETHE1, and two members of the thioredoxin family, TXN TXN2. The protein PRDX4 catalyzes reduction of hydrogen peroxide and organic hydroperoxides to water and alcohols. ETHE1 functions to suppress apoptosis and other forms of toxicity by catalyzing oxidative catabolism of


Page 18 of 43

Page 19 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


hydrogen sulfide in the mitochondrial matrix as well as prevents accumulation of harmful substances in the nucleus. TXN and TXN2 function to reduce other proteins as well as reactive oxygen species. TXN (thioredoxin) was upregulated in histotroph of animals on OIL diet by almost a two-fold change relative to CON. Oxidative stress can severely impact embryonic development 42-43. The upregulation of TXN and other proteins that protect against oxidative stress suggests that diets high in fat may increase conception rates in part by reducing reactive oxygen species. FASN and MARC2 were also clustered in the Oxidation-Reduction Process category, and both function in energy production. FASN or fatty acid synthase is an enzyme important for lipid anabolism, and MARC is also involved in lipogenesis 44. The presence of these proteins is consistent with lipid accumulation in epithelial cells of the endometrium during diestrus 45-46. The upregulation of FASN2 and MARC2 in OIL diet fed animals suggest that high fat enhanced the lipid production by endometrial cells and created a more energy rich/lipid rich environment that is essential for elongation of the conceptus 45-46. Other proteins in this group (GMPR2, PLOD1 and PCBD1) function in the metabolism of other substrates, such as purines, lysine, histidine, phenylalanine, tyrosine, etc. and thus may be involved with the recycling of components for protein synthesis. MRPS36 and DOHH were also present in this category and are essential for protein translation. There were 6 upregulated proteins (P-adj ≤ 0.1) in the PROT+OIL group relative to the CON group and 17 downregulated proteins. Animals in the PROT+OIL group expressed 73 proteins not detected in CON animals, and CON animals expressed 31 proteins not present in flushes from PROT+OIL fed group. Proteins up-regulated or unique to PROT+OIL group mapped to 75 DAVID ID, and 72 proteins (96.00%) were sorted into GO Cellular Component, with 36 of the 72 proteins mapped (50%) enriching the extracellular exosome category.



Relative to CON fed animals, 11 proteins were upregulated (P-adj ≤ 0.1) in the histotroph of PROT animals, and 3 proteins were downregulated. There were 132 proteins expressed in PROT but not CON, and only 19 proteins were expressed by CON but not PROT. DAVID analysis resulted in 140 mapped proteins, with 124 proteins (88.60%) sorted into GO Cellular Component, of which 49 proteins (35.00%) were associated with GO Cellular Component Extracellular exosome. We had predicted that proteins present in histotroph from PROT+OIL animals would be similar and/or additive to histotroph from OIL or PROT supplemented animals. This for the most part was true, with 1408 shared between PROT+OIL and OIL. However, proteome signatures were also distinct from each other. For example, there were 381 proteins distinct (expressed in one, but not the other) between OIL and PROT+OIL. A potential explanation for differences may be the availability of nutrients as fed. In the OIL diet specifically, the fat was provided as a supplement on top of the feed, whereas in the PROT+OIL diet the fat was part of the DDGS. DDGS are made from corn by initially removing the fat and then adding it back at the end of processing the feed. Therefore, the fat has time to absorb into the grains compared to our OIL treatment, for which liquid fat is added on top of the feed. Therefore, the availability of the fat may be different to the animal. Comparison of this result with previously published histotroph proteomes Proteins measured in our study were compared with proteins identified in uterine flushes of ruminants (cows and sheep) at similar reproductive stages also measured using LC-MS/MS 13, 1617

. Over 1800 proteins were considered expressed in at least one of the four groups based on the

criteria that the protein was measured in at least three of the four animals within a group in our study. Beltman et al. 17 found 40 high confidence proteins (36 non-redundant) in histotroph


Page 20 of 43

Page 21 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


flushes done on estrous cycle day 7 in beef heifers. Koch et al. 16 reported on the 100 most abundant proteins in flushes from pregnant and non-pregnant sheep on day 16. Mullen et al. 13 identified 300 proteins as being expressed on Day 7 and 510 proteins on Day 13 of the estrous cycle in beef cattle with a total of 538 non redundant found upon down-load of data. Overlap between proteins measured in our study with these previous studies was found (Figure 5; Supporting Table S-15). In particular, of the 1838 proteins expressed by at least one group (n≥3 cows/treatment), 403 (75%) overlapped with proteins identified in Mullen et al. study 13

, 118 (90%) overlapped with proteins identified in Koch et al. study 16, and 34 (94%)

overlapped with Beltman et al. proteins 17. Eighteen proteins were found common among all four studies (Table 6), and included three heat-shock proteins as well as valosin containing protein (VCP), which also regulates protein quality. Several proteins common across all four studies were involved in glycolysis (LDHB,TPI1,TKT); also found was peroxiredoxin 1(PRDX1), which functions as an antioxidant, and apolipoprotein A1(APOA1). ApoA-I is a component of high-density lipoprotein (HDL). HDL is a molecule that transports cholesterol and phospholipids through the blood. ApoA-I also attaches to cell membranes and promotes the movement of cholesterol and phospholipids from inside the cell to the outer surface. Functional analysis of the 450 proteins that overlapped at least once with our study similarly found enrichment in categories related to negative regulation of endopeptidase activity (20 proteins, 4.5%) and cellcell adhesion (18 proteins, 4.0%) within biological process ontologies (Supporting Table S-16), and within cellular component 65% of the proteins (286 proteins) were categorized as extracellular exosome (Supporting Table S-17). KEGG pathways highly enriched by proteins that overlapped with ours at least once included proteasome (20 proteins, 4.5%), biosynthesis of



amino acids (20 proteins, 4.5%) and coagulation and complement cascade (15 proteins, 3.3%; Supporting Table S-18). Over three-fold more high-confidence proteins were identified in uterine lavages from our study compared to the next highest number reported by Mullen et al. 13. The relative robustness of our data may be due to study design and LC-MS instrumentation used to collect proteomics data, data analysis and quantitative pipeline to measure expressed proteomes. Our study started with 48 cows (n=12 on each diet), from which 16 animals (n=4/treatment) were selected for flushing based on ultrasound analysis of corpus luteum quality. Six animals total (three within treatment) were used in the Koch study, twelve samples (five animals used for both time points, and a sixth random animal on each day) in the Mullen study, and six (three within treatment) in the Beltman study, and thus may explain some of the difference. Additionally, data used for analysis here were from supplemental files made available with respective manuscript, and thus may have been filtered and selected, and so robustness of other analyses may have also been lost with what was presented in papers. In our study the Thermo Scientific Q-Exactive HF hybrid quadrupole Orbitrap MS for LC-MS/MS was used for data collection. This instrument has a higher resolving power, scan speed and sensitivity than the LTQ Orbitrap MS used in the previous histotroph proteome studies 13, 16-17

. MaxQuant 18-19, 47 software was used for data analysis and protein quantitation was

performed using an MS1 intensity-based method. In contrast, previous proteome studies utilized either Mascot or Scaffold software for data analysis and used spectral counts-based method for protein quantitation. Spectral count based quantification is relatively simple, and by definition, there is a relationship between spectral counts and protein abundance 48. However, peptide and protein quantification using MS1-intensity has distinct advantages over spectral counts 49. Most


Page 22 of 43

Page 23 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


importantly MS1 quantification greatly enhances dynamic range compared to spectral counts50, and is able to more accurately quantify both low-abundant proteins and abundant proteins with saturated signals 51. Although, it is possible to quantify the relative abundance of a protein with greater quantitative accuracy using MS1 intensity, most of the reported large scale histotroph proteome studies have applied spectral counts. We believe that our MS1 intensity-based approach provides improved quantitation of low and medium abundance histotroph proteins compared with spectral counts. To our knowledge, this study represents the most comprehensive analysis of histotroph proteomes so far. Our data were made publically available, and thus provide a resource for comparative analysis and further exploration of hypothesis for others. CONCLUSION Utilizing label-free quantitative proteomic analysis, we characterized the uterine histotroph proteome of beef cattle during the preimplantation stage. To our knowledge, this is the first study to show that diet affects uterine histotroph proteome. Differences were likely due to diet composition as the high fat, high protein or high fat/high protein diets were fed as isocaloric diets, and thus differences were not due to energy content. Higher levels of fat fed as a top dress in the diet had the greatest difference in proteome. Moreover, functional analysis of proteome found that extracellular exosome and cell-cell adhesion categories were very highly enriched with histotroph proteins, supporting that the mechanism of transfer of nutrients and information from the dam to the conceptus in the preimplantion period is via these extracellular vesicles. Also enriched were categories related to energy generation, protein stabilization as well as antioxidants and protease inhibitors, which is consistent with previous analysis of histotroph proteomes in the preimplantation period.



Our comprehensive analysis captured complex cellular networks of histotroph proteome covering a broad range of metabolic processes and also revealed how different diets at the preimplantation stage changes the composition of the histotroph proteome. The list of unique and differentially expressed proteins in this study could play an important role in efforts to improve reproductive efficiency and profitability in the future. An understanding of these differences may improve cattle reproductive programs such that the uterine histotroph can be optimized for implantation and development of the conceptus. Identification of changes which can be made through nutrition to improve reproductive capacity at a time point especially critical to embryo survival is something producers can implement relatively easily, and in a way that can make a larger impact to a herd. All the LC-MS/MS raw data files are deposited and publicly available through MassIVE website (https://massive.ucsd.edu/) under ID number MSV000081991.


Page 24 of 43

Page 25 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


TABLE 1. Dietary feed ingredients and formulated chemical composition of diets fed to beef cows during late gestation and early lactation. Treatment1 CON PROT+OIL OIL PROT Ingredient, formulated, %DM/d Corn Silage 40.50 14.10 35.00 22.40 Rye Silage -12.10 20.00 9.00 Corn stover 27.50 30.90 30.20 27.80 Distiller’s grains + solubles -40.80 --Corn Gluten Feed 29.90 --30.80 Corn Gluten Meal --7.90 -Mix 30 --12.70 -Supplement, formulated, %DM/d Beef Micro 0.05 0.05 0.05 0.05 Limestone 1.80 1.80 1.80 1.80 Salt 0.21 0.21 0.21 0.21 Vit E 0.04 0.04 0.04 0.04 Nutrient Composition CP, % 11.90 17.10 11.90 17.10 NEm, Mcal/kg 0.67 0.67 0.67 0.67 NEg, Mcal/kg 0.40 0.40 0.40 0.40 Fat, % 2.00 4.80 4.80 2.00 NDF, % 42.30 45.60 44.80 45.00 1 CON= silage-based total mixed ration (TMR); PROT+OIL=TMR high in fat and protein; OIL=TMR high in fat and low in protein; PROT= TMR high in protein and low in fat TABLE 2. Approach to categorizing protein expression by treatment (n=4); (E=Expressed;

No. animals

0/4

1/4

2/4

3/4 and 4/4

Not expressed

Not specific to

Inconclusive

Expressed (E)

(NE)

treatment

(IC)

detected/treatment Expression Call

NE=Not Expressed; IC=Inconclusive)



Page 26 of 43

TABLE 3. DAVID functional analysis of proteins common amongst all treatments (n=1239) Gene Ontology/KEGG Pathways

Benjamini Score

Percent

Cellular Component: Extracellular exosome

4.5x10-261

52.90%

Biological Process: Cell-cell adhesion

1.0x10-21

3.28%

KEGG Pathways: Metabolic pathways

7.8x10-16

17.40%

Biosynthesis of amino acids

4.1x10-10

2.52%

TCA Cycle

5.6x10-8

1.43%

Glycolysis/Gluconeogenesis

9.9x10-7

1.93%

Fatty acid degradation

1.3x10-5

1.43%

Fatty acid metabolism

0.0014

1.26%


Page 27 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


TABLE 4. Proteins differentially expressed (DE) by treatment relative to control (CON) PROT+OIL

PROT

OIL

DE relative to CON (P-adj ≤

23 (6 ↑, 17 ↓)

14 (11 ↑, 3 ↓)

51 (34 ↑, 17 ↓)

0.1) PROT+OIL

PROT

OIL

E

NE

IC

E

NE

IC

E

NE

IC

1291

31

133

1390

19

46

1368

38

49

NE

73

85

96

132

40

82

180

29

45

IC

141

47

116

236

13

55

234

25

45

Expression E

Total DE 127 (79 ↑, 48 ↓)

165 (143 ↑, 2 ↓)

269 (214 ↑, 55 ↓)

Proteins Expression: E=expressed, NE= not expressed, IC=inconclusive. ↓=down regulated relative to CON; ↑=up regulated relative to control.



Page 28 of 43

TABLE 5. DAVID functional analysis of histotroph proteins upregulated in cows on OIL versus CON diet. (n = 214 upregulated proteins out of 269 differentially expressed proteins) Gene Ontology/KEGG Pathway

Benjamini Score

Percent

Biological Process: Oxidation-Reduction

4.56x10-5

9.27%

2.41x10-21

40.00%

0.087

2.44%

Process Cellular Component: Extracellular Exosome Cellular Component: Mitochondrial respiratory chain complex I


Page 29 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


TABLE 6. Gene name and Ensembl protein ID of the eighteen histotroph proteins that overlapped among our analysis and other studies. Gene name

Protein stable ID

Gene description

AKR1B1

ENSBTAP00000013082 aldo-keto reductase family 1 member B

ALB

ENSBTAP00000022763 albumin

APOA1

ENSBTAP00000002914 apolipoprotein A1

ENO1

ENSBTAP00000017839 enolase 1

HBA

ENSBTAP00000037374 hemoglobin subunit alpha

HBB

ENSBTAP00000043063 hemoglobin subunit beta Spinorphin

HSP90AA1 ENSBTAP00000008225 heat shock protein HSP 90-alpha HSPA8

ENSBTAP00000017497 heat shock cognate 71 kDa protein

HSPB1

ENSBTAP00000041898 heat shock protein beta-1 peptide

IDH1

ENSBTAP00000027348 isocitrate dehydrogenase (NADP(+)) 1, cytosolic

LDHB

ENSBTAP00000026118 Bos taurus lactate dehydrogenase B (LDHB)

PHGDH

ENSBTAP00000008907 phosphoglycerate dehydrogenase

PNP

ENSBTAP00000016346 purine nucleoside phosphorylase

PPIA

ENSBTAP00000015924 peptidyl-prolyl cis-trans isomerase A

PRDX1

ENSBTAP00000004751 peroxiredoxin 1

TKT

ENSBTAP00000004892 Transketolase

TPI1

ENSBTAP00000026358 triosephosphate isomerase 1

VCP

ENSBTAP00000019970 valosin containing protein



FIGURE LEGENDS Figure 1. Study Design. Forty-eight Angus-Simmental cows were placed on 1 of 4 isocaloric diets for ~185d: 1) a silage-based total mixed (TMR) ration - CON; 2) a TMR high in fat and protein - PROT+OIL; 3) a TMR high in protein and low in fat - PROT; or 4) a TMR high in fat and low in protein - OIL, which encompassed the last trimester and early lactation of their previous pregnancy. Figure 2. LC-MS reproducibility. (A) Box plot showing the distribution of relative protein intensities in three technical replicates. Data were plotted using DAnTE 29 using label-free quantitation (LFQ) intensities after log2-transformation; (B) Scatter plots showing the Pearson correlation coefficients of relative protein abundances (LFQ intensities) between biological replicates within the CON experimental group. Results showed good LC-MS reproducibility for protein identification and quantitation. Figure 3. (A) Venn Diagram of proteins expressed (n ≥ 3 cows per treatment) across all treatments; (B) Graphic adapted from KEGG pathway TCA Cycle enriched with 18 proteins (green boxes) that were expressed in all treatment groups. Black boxes indicate proteins involved in the cycle but not detected in all groups, and gray boxes are intermediates in the process. Proteins detected in all groups are listed with Uniprot ID, protein name, and gene symbol: Q32PF2, ATP citrate lyase, ACLY; P20004, aconitase 2, ACO2; Q29RK1, citrate synthase, mitochondrial, CS; P11179, dihydrolipoamide S-succinyltransferase, DLST; Q148J8, isocitrate dehydrogenase 3 (NAD(+)) alpha, IDH3A; Q0QF29, malate dehydrogenase 2, mitochondrial, MDH2; Q148N0, 2-oxoglutarate dehydrogenase, OGDH; P11966, pyruvate dehydrogenase (lipoamide) beta, PDHB; Q3MHX5, succinate-CoA ligase GDP-forming beta subunit, SUCLG2; Q0VCU1, aconitase 1, ACO1; Q148D3, fumarate hydratase, FH; Q0QEQ4, 30 ACS Paragon Plus Environment

Page 30 of 43

Page 31 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


isocitrate dehydrogenase (NADP(+)) 1, cytosolic, IDH1; Q04467, isocitrate dehydrogenase (NADP(+)) 2, mitochondrial, IDH2; Q3T145, malate dehydrogenase 1, MDH1; A7E3V1, succinate dehydrogenase complex flavoprotein subunit A, SDHA; Q3T189, succinate dehydrogenase complex iron sulfur subunit B, SDHB; F1MGC0, succinate-CoA ligase ADPforming beta subunit, SUCLA2. Other proteins involved but not detected are: A6QL81, dihydrolipoamide S-acetyltransferase, DLAT; F1N206, dihydrolipoamide dehydrogenase, DLD; O77784, isocitrate dehydrogenase 3 (NAD(+)) beta, IDH3B; Q148N0, oxoglutarate dehydrogenase-like, OGDHL; F6Q0C0, phosphoenolpyruvate carboxykinase 1, PCK1; A7MB35, pyruvate dehydrogenase (lipoamide) alpha 1, PDHA1; A7MB35, pyruvate dehydrogenase (lipoamide) alpha 2, PDHA2; Q58D82, isocitrate dehydrogenase 3 (NAD(+)) gamma, IDH3G; F1MDS3, phosphoenolpyruvate carboxykinase 2, mitochondrial, PCK2; Q29RK2, pyruvate carboxylase, PC; Q3T189, succinate dehydrogenase complex subunit C, SDHC; P35720, succinate dehydrogenase complex subunit D, SDHD; Q58DR8, succinate-CoA ligase alpha subunit, SUCLG1. Figure 4. Heatmap representation of 37 differentially expressed proteins. The proteins were clustered using log2 transformed LFQ intensities in DAnTE and applied Euclidean distance and average linkage hierarchical clustering method. Across bottom: G1 NNN= CON animal number; G2 NNN =PROT+OIL animal number; G3 NNN = PROT animal number; G4 NNN =OIL animal number. Uniprot ID (right column), protein name, gene symbol: P31800, Cytochrome bc1 complex subunit 1, mitochondrial, UQCRC1; P11179, Dihydrolipoyllysine-residue succinyltransferase component of 2-oxoglutarate dehydrogenase complex, mitochondrial, DLST; P10881, Lupus La protein homolog, SSB; Q58DC0, Serine/threonine-protein phosphatase CPPED1; Q3ZBU0, PDZ and LIM domain 5, PDLIM5; F1MBF0, Eukaryotic translation



initiation factor 2A, E1F2A; P0C1G6, Switch-associated protein 70, SWAP70; F6Q234, Peptidase D, PEPD; A2VE41, EGF-containing fibulin-like extracellular matrix protein 1, EFEMP1; P01156, Neurotensin/neuromedin N, NTS; O02691, 3-hydroxyacyl-CoA dehydrogenase type-2, HSD17B10; Q17QJ1, Acyl-CoA synthetase family member 2, mitochondrial, ACSF2; P33672, Proteasome subunit beta type-3, PSMB3; Q56JX3, 60S ribosomal protein L31, RPL31; Q3T0X5, Proteasome subunit alpha type-1, PSMA1; A0A140T846, DNA-(apurinic or apyrimidinic site) lyase, APEX1; F1MHC2, Syntaxin binding protein 2, STXBP2; Q1JPH2, Ras related v-ral simian leukemia viral oncogene homolog A, RALA; Q17QB3, Acid ceramidase, ASAH1; P82908, 28S ribosomal protein S36, mitochondrial, MRPS36; P04394, NADH dehydrogenase [ubiquinone] flavoprotein 2, mitochondrial, NDUFV2; F1N214, ENAH, actin regulator, ENAH; G3MZB6, Uncharacterized protein; Q2TB10, Zinc finger protein 800, ZNF800; Q3ZBU5, FUS interacting protein (Serine/argininerich) 1, FUSIP1; Q5E998, Cathepsin L2, CTSV; P02465, Collagen alpha-2(I) chain, COL1A2; A5PJK0, Serpin B10, SERPINB10; E1BED7, HYDIN, axonemal central pair apparatus protein, HYDIN; Q0P570, Diphosphomevalonate decarboxylase, MVD; E1BFQ6, Integrin subunit alpha 6, ITGA6; P02313, Non-histone chromosomal protein HMG-17, HMGN2; Q2TBX9, Integrin alpha FG-GAP repeat containing 1, ITFG1; P07107, Acyl-CoA-binding protein, DBI; G8JKZ8, Thioredoxin, TXN; Q2UVX4, Complement C3, C3

Figure 5. Venn Diagram comparison of histotroph proteins reported by previous researchers (Mullen et al., Beltman et al., Koch et al.) relative to present analysis.


Page 32 of 43

Page 33 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


SUPPORTING TABLE INFORMATION Table S-1. List of 1239 proteins common to all treatments and LC-MS/MS signals for animals within treatment Table S-2. DAVID Analysis for 1239 proteins common to all treatment groups. GO Biological Process Table S-3. DAVID Analysis for 1239 proteins common to all treatment groups. GO Cellular Component Table S-4. DAVID Analysis for 1239 proteins common to all treatment groups. KEGG Pathways Table S-5. OIL treatment proteins characterized as differentially expressed relative to control as determined by Tukey p-adjusted values (P-adj < 0.1) Table S-6. Proteins expressed by CON and not expressed by OIL Table S-7. Proteins not expressed by CON and expressed by OIL Table S-8. DAVID Analysis of OIL treatment upregulated proteins. GO Biological process Table S-9. OIL Treatment Upregulated Proteins for GO Biological Process Oxidation-reduction process Table S-10. DAVID Analysis of OIL treatment upregulated proteins. GO Cellular component Table S-11. OIL treatment upregulated proteins for GO Cellular Component Extracellular exosome Table S-12. OIL treatment upregulated proteins for GO Cellular Component Mitochondrial respiratory chain complex I Table S-13. DAVID Analysis of OIL treatment upregulated proteins. KEGG Pathway Table S-14. OIL treatment upregulated proteins for KEGG Pathway Metabolic pathways



Table S-15. ENSEMBL Comparison of proteins identified in Harlow et al. with previous uterine histotroph papers Table S-16. DAVID Functional analysis of proteins shared between Harlow et al. and others. GO Biological processes Table S-17. DAVID Functional analysis of proteins shared between Harlow et al. and others. GO Cellular component Table S-18. DAVID Functional analysis of proteins shared between Harlow et al. and others. KEGG Pathways


Page 34 of 43

Page 35 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


AUTHOR INFORMATION Corresponding Author Dr. Kara Stewart [email protected]

CONFLICTS OF INTEREST The authors declare no competing financial interest.

ACKNOWLEDGEMENTS All the LC-MS/MS data collection and data analysis was performed at the Purdue Proteomics Facility in the Bindley Bioscience Center. This work was supported in part by a grant from the Indiana Corn Marketing Council grant number 208230.



REFERENCES 1. Diskin, M. G.; Kenny, D. A., Managing the reproductive performance of beef cows. Theriogenology 2016, 86 (1), 379-387. 2. Forde, N.; Lonergan, P., Transcriptomic Analysis of the Bovine Endometrium: What is Required to Establish Uterine Receptivity to Implantation in Cattle ? Journal of Reproduction and Development 2012, 58 (2), 189-195. 3. Carter, F.; Forde, N.; Duffy, P.; Wade, M.; Fair, T.; Crowe, M. A.; Evans, A. C.; Kenny, D. A.; Roche, J. F.; Lonergan, P., Effect of increasing progesterone concentration from Day 3 of pregnancy on subsequent embryo survival and development in beef heifers. Reproduction, fertility, and development 2008, 20 (3), 368-75. 4. Stronge, A. J.; Sreenan, J. M.; Diskin, M. G.; Mee, J. F.; Kenny, D. A.; Morris, D. G., Post-insemination milk progesterone concentration and embryo survival in dairy cows. Theriogenology 2005, 64 (5), 1212-24. 5. Forde, N.; Beltman, M. E.; Duffy, G. B.; Duffy, P.; Mehta, J. P.; Gaora, P.; Roche, J. F.; Lonergan, P.; Crowe, M. A., Changes in the Endometrial Transcriptome During the Bovine Estrous Cycle: Effect of Low Circulating Progesterone and Consequences for Conceptus Elongation 1. Biology of Reproduction 2010, 84 (2), 266-278. 6. Dunne, L. D.; Diskin, M. G.; Boland, M. P.; O’Farrell, K. J.; Sreenan, J. M., The effect of pre- and post-insemination plane of nutrition on embryo survival in beef heifers. Animal Science 2016, 69 (2), 411-417. 7. Kruse, S. G.; Bridges, G. A.; Funnell, B. J.; Bird, S. L.; Lake, S. L.; Arias, R. P.; Amundson, O. L.; Larimore, E. L.; Keisler, D. H.; Perry, G. A., Influence of post-insemination nutrition on embryonic development in beef heifers. Theriogenology 2017, 90, 185-190. 8. Gunn, P.; Lemenager, R.; Bridges, G., Meta-analysis on the effects of supplementing distiller’s grains to beef cows during early lactation on reproductive efficiency and pre-wean¬ing growth. . J. Anim. Sci. 2012. , 90, 518. 9. Petit, H. V.; Benchaar, C., Milk production, milk composition, blood composition, and conception rate of transition dairy cows fed different profiles of fatty acids. Canadian journal of animal science 2007. 10. Petit, H. V.; Twagiramungu, H., Conception rate and reproductive function of dairy cows fed different fat sources. Theriogenology 2006, 66 (5). 11. Mattos, R.; Staples, C.; Thatcher, W., Effects of dietary fatty acids on reproduction in ruminants. . Rev. Reprod. 2000, 5, 38–45 12. Staples, C.; Burke, J.; Thatcher, W., Influence of supplemental fats on reproductive tissues and performance of lactating cows. . J. Dairy Sci. 1998 81, 856–871. 13. Mullen, M. P.; Elia, G.; Hilliard, M.; Parr, M. H.; Diskin, M. G.; Evans, A. C. O.; Crowe, M. A., Proteomic Characterization of Histotroph during the Preimplantation Phase of the Estrous Cycle in Cattle. Journal of Proteome Research 2012, 11 (5), 3004-3018. 14. Gao, H.; Wu, G.; Spencer, T. E.; Johnson, G. A.; Li, X.; Bazer, F. W., Select Nutrients in the Ovine Uterine Lumen. I. Amino Acids, Glucose, and Ions in Uterine Lumenal Flushings of Cyclic and Pregnant Ewes1. Biology of Reproduction 2009, 80 (1), 86-93. 15. Carter, A., EVOLUTION OF PLACENTAL FUNCTION IN MAMMALS: THE MOLECULAR BASIS OF GAS AND NUTRIENT TRANSFER, HORMONE SECRETION, AND IMMUNE RESPONSES. In Physiol. Rev., 2012; Vol. 92, pp 1543-1576.


Page 36 of 43

Page 37 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


16. Koch, J. M.; Ramadoss, J.; Magness, R. R., Proteomic Profile of Uterine Luminal Fluid from Early Pregnant Ewes. Journal of Proteome Research 2010, 9 (8), 3878-3885. 17. Beltman, M. E.; Mullen, M. P.; Elia, G.; Hilliard, M.; Diskin, M. G.; Evans, A. C.; Crowe, M. A., Global proteomic characterization of uterine histotroph recovered from beef heifers yielding good quality and degenerate day 7 embryos. Domest Anim Endocrinol 2014, 46, 49-57. 18. Cox, J.; Mann, M., MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 2008, 26 (12), 1367-72. 19. Cox, J.; Hein, M. Y.; Luber, C. A.; Paron, I.; Nagaraj, N.; Mann, M., Accurate proteomewide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics 2014, 13 (9), 2513-26. 20. Cox, J.; Neuhauser, N.; Michalski, A.; Scheltema, R. A.; Olsen, J. V.; Mann, M., Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 2011, 10 (4), 1794-805. 21. UniProt: the universal protein knowledgebase. Nucleic Acids Research 2017, 45 (D1), D158-D169. 22. Aken, B. L.; Ayling, S.; Barrell, D.; Clarke, L.; Curwen, V.; Fairley, S.; Fernandez Banet, J.; Billis, K.; García Girón, C.; Hourlier, T.; Howe, K.; Kähäri, A.; Kokocinski, F.; Martin, F. J.; Murphy, D. N.; Nag, R.; Ruffier, M.; Schuster, M.; Tang, Y. A.; Vogel, J.-H.; White, S.; Zadissa, A.; Flicek, P.; Searle, S. M. J., The Ensembl gene annotation system. Database 2016, 2016, baw093-baw093. 23. Liu, H.; Sadygov, R. G.; Yates, J. R., A Model for Random Sampling and Estimation of Relative Protein Abundance in Shotgun Proteomics. Analytical Chemistry 2004, 76 (14), 41934201. 24. Bantscheff, M.; Schirle, M.; Sweetman, G.; Rick, J.; Kuster, B., Quantitative mass spectrometry in proteomics: a critical review. Analytical and bioanalytical chemistry 2007, 389 (4), 1017-31. 25. Rodriguez, J.; Gupta, N.; Smith, R. D.; Pevzner, P. A., Does trypsin cut before proline? J Proteome Res 2008, 7 (1), 300-5. 26. Abaye, D. A.; Pullen, F. S.; Nielsen, B. V., Peptide polarity and the position of arginine as sources of selectivity during positive electrospray ionisation mass spectrometry. Rapid communications in mass spectrometry : RCM 2011, 25 (23), 3597-608. 27. Goeminne, L. J. E.; Gevaert, K.; Clement, L., Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics. Molecular & Cellular Proteomics : MCP 2016, 15 (2), 657-668. 28. Michalski, A.; Cox, J.; Mann, M., More than 100,000 Detectable Peptide Species Elute in Single Shotgun Proteomics Runs but the Majority is Inaccessible to Data-Dependent LC−MS/MS. Journal of Proteome Research 2011, 10 (4), 1785-1793. 29. Polpitiya, A. D.; Qian, W. J.; Jaitly, N.; Petyuk, V. A.; Adkins, J. N.; Camp, D. G., 2nd; Anderson, G. A.; Smith, R. D., DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 2008, 24 (13), 1556-8. 30. Forde, N.; Carter, F.; Spencer, T. E.; Bazer, F. W.; Sandra, O.; Mansouri-Attia, N.; Okumu, L. A.; McGettigan, P. A.; Mehta, J. P.; McBride, R.; O'Gaora, P.; Roche, J. F.; Lonergan, P., Conceptus-induced changes in the endometrial transcriptome: how soon does the cow know she is pregnant? Biol Reprod 2011, 85 (1), 144-56. 37 ACS Paragon Plus Environment


31. Berendt, F. J.; Frohlich, T.; Schmidt, S. E.; Reichenbach, H. D.; Wolf, E.; Arnold, G. J., Holistic differential analysis of embryo-induced alterations in the proteome of bovine endometrium in the preattachment period. Proteomics 2005, 5 (10), 2551-60. 32. Machtinger, R.; Laurent, L. C.; Baccarelli, A. A., Extracellular vesicles: roles in gamete maturation, fertilization and embryo implantation. Human Reproduction Update 2016, 22 (2), 182-193. 33. Denzer, K.; Kleijmeer, M. J.; Heijnen, H. F.; Stoorvogel, W.; Geuze, H. J., Exosome: from internal vesicle of the multivesicular body to intercellular signaling device. J Cell Sci 2000, 113 Pt 19, 3365-74. 34. Thery, C.; Ostrowski, M.; Segura, E., Membrane vesicles as conveyors of immune responses. Nature reviews. Immunology 2009, 9 (8), 581-93. 35. Keerthikumar, S.; Chisanga, D.; Ariyaratne, D.; Al Saffar, H.; Anand, S.; Zhao, K.; Samuel, M.; Pathan, M.; Jois, M.; Chilamkurti, N.; Gangoda, L.; Mathivanan, S., ExoCarta: A Web-Based Compendium of Exosomal Cargo. Journal of Molecular Biology 2016, 428 (4), 688692. 36. Gerke, V.; Creutz, C. E.; Moss, S. E., Annexins: linking Ca2+ signalling to membrane dynamics. Nat Rev Mol Cell Biol 2005, 6 (6), 449-61. 37. Santos, T. G.; Martins, V. R.; Hajj, G. N. M., Unconventional Secretion of Heat Shock Proteins in Cancer. Int J Mol Sci 2017, 18 (5), 946. 38. Burns, G.; Brooks, K.; Wildung, M.; Navakanitworakul, R.; Christenson, L. K.; Spencer, T. E., Extracellular Vesicles in Luminal Fluid of the Ovine Uterus. PLoS ONE 2014, 9 (3), e90913. 39. Kusunoki, R.; Ishihara, S.; Aziz, M.; Oka, A.; Tada, Y.; Kinoshita, Y., Roles of milk fat globule-epidermal growth factor 8 in intestinal inflammation. Digestion 2012, 85 (2), 103-7. 40. Khalifeh-Soltani, A.; Ha, A.; Podolsky, M. J.; McCarthy, D. A.; McKleroy, W.; Azary, S.; Sakuma, S.; Tharp, K. M.; Wu, N.; Yokosaki, Y.; Hart, D.; Stahl, A.; Atabai, K., α8β1 integrin regulates nutrient absorption through an Mfge8-PTEN dependent mechanism. eLife 2016, 5, e13063. 41. Robbins, P. D.; Morelli, A. E., Regulation of Immune Responses by Extracellular Vesicles. Nature reviews. Immunology 2014, 14 (3), 195-208. 42. Ornoy, A., Embryonic oxidative stress as a mechanism of teratogenesis with special emphasis on diabetic embryopathy. Reproductive Toxicology 2007, 24 (1), 31-41. 43. Takahashi, M., Oxidative Stress and Redox Regulation on In Vitro Development of Mammalian Embryos. Journal of Reproduction and Development 2012, 58 (1), 1-9. 44. Neve, E. P.; Kofeler, H.; Hendriks, D. F.; Nordling, A.; Gogvadze, V.; Mkrtchian, S.; Naslund, E.; Ingelman-Sundberg, M., Expression and Function of mARC: Roles in Lipogenesis and Metabolic Activation of Ximelagatran. PLoS One 2015, 10 (9), e0138487. 45. Ribeiro, E.; Greco, L.; Bisinotto, R.; Lima, F.; Thatcher, W.; Santos, J., Biology of Preimplantation Conceptus at the Onset of Elongation in Dairy Cows. Biol. Reprod. 2016, 94 (4). 46. Ribeiro, E. S.; Santos, J. E. P.; Thatcher, W. W., Role of lipids on elongation of the preimplantation conceptus in ruminants. Reproduction 2016, 152 (4). 47. Cox J., M. M. M., MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. . Nat. Biotechnol. 2008, 26 1367–1372


Page 38 of 43

Page 39 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


48. Ning, K.; Fermin, D.; Nesvizhskii, A. I., Comparative Analysis of Different Label-Free Mass Spectrometry Based Protein Abundance Estimates and Their Correlation with RNA-Seq Gene Expression Data. Journal of Proteome Research 2012, 11 (4), 2261-2271. 49. Aryal, U. K.; McBride, Z.; Chen, D.; Xie, J.; Szymanski, D. B., Analysis of protein complexes in Arabidopsis leaves using size exclusion chromatography and label-free protein correlation profiling. J Proteomics 2017, 166, 8-18. 50. Bondarenko, P. V.; Chelius, D.; Shaler, T. A., Identification and Relative Quantitation of Protein Mixtures by Enzymatic Digestion Followed by Capillary Reversed-Phase Liquid Chromatography−Tandem Mass Spectrometry. Analytical Chemistry 2002, 74 (18), 4741-4749. 51. Afshar, N.; Black, B. E.; Paschal, B. M., Retrotranslocation of the chaperone calreticulin from the endoplasmic reticulum lumen to the cytosol. Mol Cell Biol 2005, 25 (20), 8844-53.



Figure 1.

Figure 2A.

Figure 2B.

ACS Paragon Plus Environment

Page 40 of 43

Page 41 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3A.

Figure 3B.



Figure 4.

Figure 5.


Page 42 of 43

Page 43 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


For TOC Only


DIET IMPACTS PREIMPLANTATION HISTOTROPH PROTEOME IN

Recommend Documents