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# In-depth exploration of the high and normal pH beef proteome: First insights emphasizing the dynamic protein changes in *Longissimus thoracis* muscle from pasture-finished Nellore bulls over different *postmortem* times

Iliani Patinho<fnref n="1" />, Daniel S. Antonelo<fnref n="2" />, Eduardo F. Delgado<fnref n="3" />, Laura Alessandroni<fnref n="4" />, Julio C.C. Balieiro<fnref n="2" />, Carmen J. Contreras-Castillo<fnref n="1" /> and Mohammed Gagaoua<fnref n="5" />*

## Abstract

This study aimed to evaluate for the first time the temporal dynamic changes in early *postmortem* proteome of normal and high ultimate pH (pHu) beef samples from the same cattle using a shotgun proteomics approach. Ten selected carcasses classified as normal (pHu < 5.8; n = 5) or high (pHu ≥ 6.2; n = 5) pHu beef from pasture-finished Nellore (*Bos indicus*) bulls were sampled from *Longissimus thoracis* muscle at 30 min, 9 h and 44 h *postmortem* for proteome comparison. The temporal proteomics profiling quantified 863 proteins, from which 251 were differentially abundant (DAPs) between high and normal pHu at 30 min (n = 33), 9h (n = 181) and 44h (n = 37). Among the myriad interconnected pathways regulating pH decline during *postmortem* metabolism, this study revealed the pivotal role of energy metabolism, cellular response to stress, oxidoreductase activity and muscle system process pathways throughout the early *postmortem*. Twenty-three proteins overlap among *postmortem* times and may be suggested as candidate biomarkers to the dark-cutting condition development. The study further evidenced for the first time the central role of ribosomal proteins and histones in the first minutes after animal bleeding. Moreover, this study revealed the disparity in the mechanisms underpinning the development of dark-cutting beef condition among *postmortem* times, emphasizing multiple dynamic changes in the muscle proteome. Therefore, this study revealed important insights regarding the temporal dynamic changes that occur in early *postmortem* of high and normal muscle pHu beef, proposing specific pathways to determine the biological mechanisms behind dark-cutting determination.

<footnote n="1">¹ Department of Agri-food Industry, Food and Nutrition, Luiz de Queiroz College of Agriculture, University of Sao Paulo, Piracicaba, SP 13418-900, Brazil</footnote>

<footnote n="2">² School of Veterinary Medicine and Animal Science, University of Sao Paulo, Pirassununga, SP 13635-900, Brazil</footnote>

<footnote n="3">³ Department of Animal Science, Luiz de Queiroz College of Agriculture, University of Sao Paulo, Piracicaba, SP 13418-900, Brazil</footnote>

<footnote n="4">⁴ Chemistry Interdisciplinary Project (CHIP), School of Pharmacy, University of Camerino, Via Madonna delle Carceri, 62032 Camerino, Italy</footnote>

<footnote n="5">⁵ PEGASE, INRAE, Institut Agro, 35590 Saint-Gilles, France</footnote>

<footnote>* Corresponding author e-mail: mohammed.gagaoua@inrae.fr</footnote>
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**Keywords:** Beef quality defects; Dark-cutting; Beef proteome; Energy metabolism; Nellore cattle; Oxidative stress; Bioinformatics; Biomarkers.
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# 1. Introduction

Brazil has the largest bovine herd in the world and is the largest beef exporter, with a beef livestock agribusiness movement of approximately US$ 169.29 billion. Most of the Brazilian herd has influence of zebu cattle (*Bos taurus indicus*), mainly Nellore breed, which combine productivity, disease resistance and heat tolerance once animals are predominantly finished on pasture (ABIEC, 2022; Carvalho et al., 2014), but keeping in mind that a substantial amount of the animals are also reared under feedlot production systems (Ferraz & de Felício, 2010; de Andrade et al., 2020). However, although beef cattle have adapted well to tropical conditions in Brazil (Carvalho et al., 2014; Rodrigues et al., 2017; Malheiros et al., 2020), beef cattle finished in extensive systems (pasture-based) is usually darker in color appearance and produces a less tender cooked product than those finished in feedlot systems (grain-based) (Antonelo et al., 2022; Gómez et al., 2022) which may be directly related to the rate and extent of muscle pH decline. In fact, ultimate pH (pHu) is associated with muscle glycogen and *postmortem* energy metabolism. Several factors influence muscle pHu, including increased physical activity, feed deprivation, season, and pre-slaughter stress. These events are well-known to rapidly reduce muscle glycogen stores (Zhao et al., 2022; Terlouw et al., 2021; García-Torres et al., 2021) which leads to a high pHu (Poleti et al., 2018; Kiyimba et al., 2021) that negatively affect the final beef quality (Wu et al., 2020; England et al., 2016; Franco et al., 2015). As a result, carcasses with darker lean are generally downgraded during the process of grading, leading to significant economic losses globally for the beef sector (Gagaoua, Warner, et al., 2021; Ponnampalam et al., 2017). Thus, it is essential to avoid failures in the categorization of carcasses according to the pHu and develop early *postmortem* tools for their monitoring.

Several recent comprehensive reviews reported that proteomics techniques together with mass spectrometry (MS) and bioinformatics approaches have been widely used to show the mechanisms that drive the beef quality development in *postmortem* muscle, in addition to investigating the dynamic changes and modifications that occur in the muscle proteome and search for candidate biomarkers (Picard & Gagaoua, 2020; Gagaoua et al., 2021). Proteomics was successfully applied to the discovery of biomarkers of dark-cutting beef from *Bos taurus* (Franco et al., 2015; Mahmood et al., 2018; Poleti et al., 2018; Fuente-Garcia et al., 2019; Wu et al., 2020; Fuente-Garcia et al., 2020; Sentandreu et al., 2021; Kiyimba et al., 2022; 2023), however a
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strong disparity (very weak overlap) among the studies was revealed using a meta-analysis
integromics approach (Gagaoua et al., 2021). Although certain consistent pathways were
reported, the authors evidenced a very weak overlap in the putative biomarkers among the eight
dark-cutting beef proteomics studies (< 8%) with only 10 common protein biomarkers reported
across no more than two studies. From the ten common proteins, eight were related to dark-
cutting beef conditions in different directions (positive and negative). It was suggested that the
molecular signatures underpinning dark-cutting beef development are more complex than
expected, and exploratory studies of the muscle proteomes of the same animals at both early
*postmortem* and at the usual sampling times used to categorized carcasses based on their high
pHu (24 or 48h) would allow further insights on the biochemical mechanisms and discovery of
more appropriate biomarkers.

Foreseeing the above, the current study used a shotgun proteomics approach to fill the gap in
the early *postmortem* muscle metabolism, while considering *Bos indicus* cattle. We aim to
evaluate using for the first time a serial-time analysis approaches the temporal dynamic changes
in the abundances of the proteins that occur in high and normal muscle pHu carcasses sampled at
three different times these being 30 min, 9 h and 44 h *postmortem*. The study further aimed to
reveal new features behind the protein changes by considering new early *postmortem* sampling
times along of the possibility of proposing a more robust list of biomarkers of dark-cutting beef
condition.

## 2. Materials and Methods

All procedures used in this study were conducted in accordance with the Institutional Animal
Care and Use Committee Guidelines (protocol 2019/22) and were approved by the committees of
the “Luiz de Queiroz” College of Agriculture of the University of Sao Paulo.

### 2.1. Animals, treatments, and sampling

Ten carcasses classified as normal pHu beef (at 24, 48 and 72h *postmortem*) from pasture-
finished Nellore (*Bos indicus*) bulls, ranging from 30 to 42 months of age (4 to 6 permanent
incisors teeth) and 349 ± 31 kg of hot carcass weight were selected under industrial conditions.
The carcasses were from a larger experiment at a commercial meat processor under standard
slaughtering conditions (for further details refer to Patinho *et al.* (2024)). More specifically,
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hundred carcasses were sampled from early *postmortem* time until 72h *postmortem*. The carcasses that were corresponding to the objectives of this study were being carcasses with normal pHu < 5.8; n = 5 or high pHu ≥ 6.2; n = 5 were targeted for the proteomics trial. For all the sampling times, samples (~ 30 g) were excised from the left side of the carcasses by targeting the *Longissimus thoracis* (LT) muscle adjacent between the 10th and 11th ribs immediately after skinning (approximately 30 min *postmortem*). The same carcasses, stored at 2 ± 2°C were further sampled from the same location at 9 h and 44 h *postmortem* following the same sampling conditions. All samples were immediately snap frozen in liquid nitrogen and stored at -80 °C for proteomics analyses.

## 2.2. pH determination and other meat quality evaluation

Muscle pH was determined using the iodoacetate method as described by Bendall (1978). Briefly, the three targeted sampling times for the proteomics analyses these being 30 min, 9 h and 44 h LT muscle samples, as well as the samples taken at 3 h, 6 h, 24 h and 72 h (all given in the pH decline curves of Fig. 1) were powdered, added to a buffer containing fresh 5 mM sodium iodoacetate and 150 mM KCl (pH 7.0) at a 1:8 ratio (wt/vol), and homogenized. Muscle homogenates were centrifuged at 13,000 × g for 5 min at room temperature, equilibrated to 25 °C and measured using a digital pH meter (Lucadema, LUCA-210 model, São José do Rio Preto, Brazil) coupled with automatic temperature compensation and glass penetration electrode.

To ensure that the samples were correctly categorized as high pH (true dark-cutters) from normal pH, meat quality traits including color parameters: lightness (L*), redness (a*), yellowness (b*), and computed chroma (C*) and hue angle (h°), as well % Methmyoglobin (MetMb, %), % Deoxymyoglobin (DeoxyMb, %), % Oxymyoglobin (OxyMb, %) and Surface color stability), and texture traits: Warner-Bratzler shear force and drip loss%, were evaluated at 72h (Table 1) and details on the protocols are described in detail in previous studies (Gagaoua, Monteils, & Picard, 2018a; Patinho *et al.* (2024).

## 2.3. Proteome Analysis

### 2.3.1. Extraction and quantification of whole-muscle proteome
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The frozen 30 min, 9 h and 44 h LT muscle samples from normal (n = 5) and high (n = 5) pHu sampled were powdered in liquid nitrogen. Then, 50 mg of ground tissue was homogenized in 0.5 mL of ice-cold lysis buffer containing 7 M urea, 2 M thiourea, 2% CHAPS, 1% dithiothreitol [DTT] and 1% phenylmethylsulfonyl fluoride [PMSF] for 45 s using a homogenizer (IKA, T10 model, Campinas, Brazil) (Bouley, Chambon, & Picard, 2004). Homogenates were then centrifuged at 12,000 × g for 30 min at 4 °C to remove debris (Zhu et al., 2021). The supernatants, which correspond to the total proteins, were collected and quantified using the Bradford Assay (#B6916, Sigma-Aldrich, St. Louis, USA) with bovine serum albumin (BSA) as the standard (Bradford, 1976).

### 2.3.2. Shotgun proteomics: LC-MS/MS analysis and protein quantification

For the preparation of the protein bands for shotgun proteomics analysis, the protein extracts stored at -80°C were first diluted with Milli-Q water following an in-house Excel spreadsheet calculator, adjusted to a final volume of 40 µg in an equal volume with fresh Laemmli denaturing buffer containing 125 mM Tris (pH 6.8), 4% w/v SDS, 10% v/v β-mercaptoethanol, 20% v/v glycerol, and 0.004% bromophenol blue (#S3401, Sigma-Aldrich, St. Louis, USA) and heated at 90 °C for 10 minutes (Lamri, della Malva, Djenane, Albenzio, & Gagaoua, 2023). The denatured protein extracts were then loaded in 10 wells (one gel for sampling each time) freshly prepared 12% resolving and 4% stacking gels in one-dimensional gel electrophoresis (1D sodium dodecyl sulfate polyacrylamide gel electrophoresis: SDS-PAGE) using a Mini-PROTEAN® Tetra Cell System (Bio-Rad, Hercules, CA, USA). The running of the electrophoresis, all the samples and gels together to avoid technical variability, was conducted using a TGS buffer containing 25 mM Tris (pH 8.6), 192 mM glycine and 0.1% SDS (#T7777, Sigma-Aldrich, Saint Louis, USA). The running conditions were set for 15 minutes at 4 watts to concentrate the proteins in one large band in the stacking gel.

The gels were carefully removed under sterile conditions, washed with fresh Milli-Q water, stained with EZ Blue Gel staining reagent (Sigma-Aldrich, Saint Louis, USA) for 15 min to clearly visualize the protein bands. The colored gels were then washed several times with Milli-Q water under gentle shaking. The protein bands carefully excised using sterile and disposable scalpels, immediately placed into sterile Eppendorf tubes to be reduced, alkylated, destained and dried, before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses as
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previously described (Lamri, della Malva, Djenane, López-Pedrouso, et al., 2023). For LC-MS/MS analysis, identification of the proteins and quantification, the dry protein bands were first digested by a sequence grade Trypsin (Promega, USA) following the steps described by (Lamri, della Malva, Djenane, Albenzio, & Gagaoua, 2023). The database search results were screened and exported when the false discovery rate (FDR) of the number of peptides is higher than 2 and the protein match level was <1%. The raw data from the LC-MS/MS were aligned against the *Bos taurus* database (https://www.uniprot.org/proteomes/UP000009136)

## 2.4. Statistical analyses

For the meat quality traits, a variance analysis (ANOVA) at a level of 5% (Tukey comparison) was performed to compare the pH values over *postmortem* time as well as for the comparison of the meat quality traits (pH, color and texture) evaluated at 72h *postmortem*. The whole meat quality data (pH, color and texture traits) were further projected in a principle component analyses (PCA) to highlight the degree of the relationships among the variables and their ability to separate the two pH groups (individuals) within the corresponding bi-plot. To check the suitability of the factorial model, the Kaiser-Meyer-Olkin (KMO) test for sampling adequacy was used and the overall KMO value is reported.

For the proteome data identified and quantified in the biological replicates of each pHu group and within each sampling time were considered in a pairwise manner for the statistical analyses. The database was uploaded to the online webservice MetaboAnalyst 5.0 tool (https://www.metaboanalyst.ca/) for analyses. First, the data were auto scaled prior to analysis and missing values (<3.1% of the database) were computed using the k-nearest neighbor algorithm. Second, the data were normalized by log-transformation and pareto scaling. For each sampling time, pairwise comparisons between the high vs. normal pHu were performed to analyze the protein abundances. Therefore, volcano plots were plotted and proteins with a minimum fold change of 1.2 and P-value < 0.05 were considered as regulated differently in the three comparison groups (high vs. normal pHu at 30 min, 9 h, and 44 h *postmortem*). The statistical comparisons were performed with adjusted P-values to Bonferroni correction.

## 2.5. Bioinformatics analyses
The gene names of the differentially abundant proteins (DAPs) were converted into human
identifiers (gene names) based on the orthologous and homologous links, to avoid any limitation
of the Gene Ontology (GO) annotations on bovine proteins as previously described (Gagaoua,
Hughes, et al., 2020). For the GO analyses, the protein lists were analyzed using Metascape®
webservice tool (https://metascape.org/) to identify the enriched pathways, clustering analyses of
the GO terms and comparative heatmaps (hierarchical clustering of pathways) and network
analysis. The parameters were set to proteins overlap > 3, an enrichment *P*-value < 0.05, and an
enrichment factor > 1.5. Benjamini–Hochberg *P*-value correction algorithm and hypergeometric
test were used to display the first statistically significant enriched ontology terms in each cluster.
This approach provided a comprehensive gene list annotation curated via GO Biological
Processes, GO Molecular Functions, Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathways and Reactome gene sets. For the protein-protein interactions (PPI), the STRING
database (https://string-db.org/) was used to evaluate the known and predicted PPI among the
different proteins lists for each of the three-*postmortem* time-points and within the common
DAPs proteins. A confidence score of 0.500 was chosen as the minimum required interaction
cutoff score with no additional interactors. The collected nodes of the subnetworks were used to
create clusters of the corresponding DAPs (up-and down-regulated in the whole LT muscle
proteome from high and normal pHu beef) using the Markov Clustering Algorithm (MCL) with
an inflation parameter of 1.8. For the secretome analysis, the computational prediction of the
putatively secreted proteins in bovine muscle through classical (involving a signal peptide) or
non-classical way (without signal peptide on the sequence) was performed using the webservice
open source ProteINSIDE tool (https://www.proteinside.org/).

**3. Results**

Unless for 30 min *postmortem* time, the pH declines curves were significantly different across all the sampling times considered (3 h, 6 h, 9 h, 24 h, 44 h and 72 h) between the normal and high pH carcasses evaluated in this trial (Fig. 1A,B). The variance analysis performed on the meat quality traits (Table 1) confirmed, differences in the pHu whatever the time of measurement (24 h, 44 h and 72h *postmortem*), lower lightness (*L**), redness (*a**), yellowness (*b**) and chroma (*C*) values in high pH group, along of higher DeoxyMb, % and lower OxyMb, %. These results are in line to several studies in the literature including a recent similar study on *Bos taurus*
reported by Kiyimba *et al.* (2023). Tenderness evaluated by Warner-Bratzler shear force was also and as known in the literature lower in high pH group (36.85 N) compared normal pH group (96.08 N), evaluated in this trial at 72h *postmortem*. These results were further confirmed by the PCA given in **Fig. 2A** and the separation of the observations of the two pH groups in its bi-plot (**Fig. 2B**).

The shotgun proteomics allowed quantifying at an FDR of 1% and with a minimum of 2
peptides 863 proteins in the whole muscle samples (n = 5 normal pHu and n = 5 high pHu beef).
The statistical comparison of the protein profiles in the proteome of LT muscle from normal and
high pHu beef revealed 251 DAPs across the three *postmortem* times (30 min, 9 h and 44 h).
Specifically, Volcano plot analyses allowed identifying 33, 181 and 37 DAPs for the proteome
comparisons from normal and high pHu beef at 30 min (**Table 2**), 9 h (**Table 3**) and 44 h (**Table
4**) *postmortem*, respectively.

**3.1. Comparison of the LT muscle proteome from normal and high pHu beef at 30 min**
*postmortem*

From the 33 DAPs identified in the LT muscle proteome to differ between normal and high
pHu beef at 30 min *postmortem* (Table 2), 9 were up-regulated and 24 were down-regulated in
high pHu beef (Fig. 3A). The pathway enrichment analysis of this protein list revealed 12
significant enriched terms (Fig. 3B). The top 5 enriched terms were “cellular response to stress”,
“generation of precursor metabolites and energy”, “parkinson disease”, “nucleobase-containing
small molecule metabolic process”, and “diseases of programmed cell death” (Fig. 3C). The PPI
network of the 33 DAPs is given in Fig. 3D. The network revealed interconnectedness among the
functional pathways, mainly among four major pathways, these being TCA cycle and respiratory
electron transport, calcium regulation, ribosome and proteasomes. The hierarchical heatmap
clustering of the 33 up- and down-regulated proteins between high and normal pHu beef at 30
min *postmortem* revealed no common changing pathways. However, two terms related to cellular
response to stress (R-HSA-2262752) and regulation of translation (GO:0006417) were
exclusively enriched in high pHu beef only, while six terms related to parkinson disease
(hsa05012), nucleobase-containing small molecule metabolic process (GO:0055086), generation
of precursor metabolites and energy (GO:0006091), regulation of endocytosis (GO:0030100),
carbohydrate metabolic process (GO:0005975), and calcium ion binding (GO:0005509) were
exclusively enriched in normal pHu beef only (Fig. 3E). The statistics details of each enriched
term are given in Table S1.

**3.2. Comparison of the LT muscle proteome from normal and high pHu beef at 9 h**
*postmortem*

As shown in **Table 3**, a higher number of proteins than 30 min *postmortem* (n = 181 DAPs) were identified to differ between normal and high pHu beef at 9 h *postmortem*. The results of the Volcano plot evidenced 77 up-regulated and 104 down-regulated in high pHu beef (**Fig. 4A**). The pathway enrichment analysis revealed 20 significantly enriched terms (**Fig. 4B**). Among these, the top 5 enriched ones were related in the following order to “generation of precursor metabolites and energy”, “cellular response to stress”, “muscle structure development”, “axon guidance” and “muscle system process” (**Fig. 4C**). The PPI network revealed a strong interconnectedness between the proteins and the functional pathways to which they belong (**Fig. 4D**). To the best of our knowledge, this is the first comprehensive PPI network depicting the dynamic changes occurring in early (9 h) *postmortem* muscle of dark-cutting beef as revealed by shotgun proteomics. Such complexity of the mechanisms is further confirmed by the hierarchical heatmap clustering of the up- and down-regulated proteins between high and normal pHu beef at 9 h *postmortem* (**Fig. 4E**). The findings revealed 14 common changing pathways, including generation of precursor metabolites and energy (GO:0006091), cellular response to stress (R-HAS-2262752), muscle system process (GO:0003012), and oxidoreductase activity (GO:0016491). Interestingly, one term was exclusively enriched in high pHu beef only, this being regulation of ryanodine-sensitive calcium-release channel activity (GO:0060314), while five terms were exclusively enriched in normal pHu beef only, including muscle cell development (GO:0055001) and structural constituent of muscle (GO:0008307). The statistics details of each enriched term are given in **Table S2**.

**3.3. Comparison of the LT muscle proteome from normal and high pHu beef at 44 h**
*postmortem*

From the 37 DAPs identified in the LT muscle proteome to differ between normal and high
pHu beef at 44 h *postmortem* (Table 4), 19 were up-regulated and 18 were down-regulated in
high pHu beef (Fig. 5A). The pathway enrichment analysis revealed 19 significantly enriched
terms (Fig. 5B). Among these, the top 5 enriched terms were oxidoreductase activity, generation of precursor metabolites and energy, glutathione metabolic process, locomotory behavior and chaperone binding (Fig. 5C). The PPI network of the 37 DAPs revealed an interconnectedness among the proteins and the functional pathways to which they belong (Fig. 5D). The hierarchical heatmap clustering of the up- and down-regulated proteins between high and normal pHu beef at 44 h postmortem (Fig. 5E) revealed one common changing pathway related to oxidoreductase activity (GO:0016491). Five terms were exclusively enriched in high pHu beef only, including NAD binding (GO:0051287) and muscle system process (GO:0003012), while six terms were exclusively enriched in normal pHu beef only, including regulation of oxidative stress-induced intrinsic apoptotic signaling pathway (GO:1902175) and response to hypoxia (GO:0001666). The statistics details of each enriched term are given in Table S3.

**3.4. Protein overlap and comparison of the DAPs changing during the postmortem period**

Functional enrichment analysis and clustering on the total DAPs identified to change in the LT muscle proteome from normal and high pHu beef among the three *postmortem* time comparisons are given in **Fig. 6**. The overlap among the three protein lists revealed 23 common proteins: ALDH2, ANK1, ANXA4, ATP5B, CYB5R3, CYC1, DNAJA2, FHL1, GAPDH, GSTM2, IDH3B, IDH3G, MB, NDUFA7, NDUFA9, SERPINA3, SERPINC1, SLC25A3, SOD1, TMOD1, TUBA1B, UBE2L3 and UNC45B (**Fig. 6A**). These proteins belong to eight significantly enriched GO terms (**Fig. 6B**) mainly dominated by oxidoreductase activity (GO:0016491) and generation of precursor metabolite and energy (GO: 0006091) (**Fig. 6C**). The relative abundance of the 23 DAPs proteins are clustered in the heatmap (**Fig. 6D**) using the averages of pH group/pH to highlight the proteins mainly up in high pHu and those Down in high pHu. The PPI network of the 23 overlap proteins revealed a strong interconnectedness among the proteins and a domination of the TCA cycle and respiratory electron transport (**Fig. 6E**). The hierarchical heatmap clustering of the total DAPs of each *postmortem* time (30 min, 9 h and 44 h) revealed five common enriched terms (**Fig. 6F**), these being generation of precursor metabolite and energy (GO: 0006091), cellular response to stress (R-HAS-226752), chaperone binding (GO:0051087), oxidoreductase activity (GO:0016491) and muscle structure development (GO:0061061). All of these terms were more abundant at 9 h *postmortem*, except for the chaperone binding that was more abundant at 44 h *postmortem*. Moreover, it must be noted that
the cellular response to chemical stress (R-HSA-9711123), NADH metabolic pathway (GO:0006734), cardiac muscle contraction (hsa04260), platelet degranulation (R-HSA-114608), structural constituent of muscle (GO:0008307), alpha-actinin binding (GO:0051393) and actin binding (GO:0003779) were significantly and exclusively specific to the LT muscle proteome at 9 h *postmortem*. The secretome analyses (Fig. 7) further evidenced more proteins (n = 12) that are secreted either through the classical (n = 8) or the non-classical pathways (n = 4) at 9h *postmortem* compared to the protein lists of the 30min (n = 2) and 44h *postmortem* (n = 1). The only protein secreted through the classical pathway (extracellular protein) was SERPINC1, which was common with the 9h *postmortem* (Fig. 7). This protein also known as antithrombin-III belongs to the superfamily of serpins, previously reviewed for their major role in meat tenderization (Gagaoua, Hafid, et al., 2015).

**3.5. Comparison of DAPs proteins of this study with Gagaoua's dark-cutting beef proteome database**

The input proteins identified in this trial were matched against the dark-cutting beef proteome database previously built in the integromics study by Gagaoua, Warner, et al. (2021). The results revealed 28 common proteins whatever the *postmortem* time (Fig. 8). More specifically, at 30 min *postmortem*, ATIC, MDH1 and UGP2 were the common proteins (Fig. 8A). The hierarchical heatmap clustering of the DAPs revealed 8 common enriched GO terms (Fig. 8D). Interestingly, 12 enriched terms were significantly and exclusively specific to Gagaoua's database. At 9 h *postmortem*, 23 (ACTA1, ACTN2, ATP5B, BLMH, CACNB1, CKM, CMYA5, COPS7A, COQ8A, COX6A2, EPM2A, FHL1, LDB3, LMCD1, MYOM2, MYOT, NME2, PDLIM5, PGAM2, PHKB, PKM2, PRKAA2, UQCRC2) proteins overlapped between datasets (Fig. 8B). The hierarchical heatmap clustering of the DAPs revealed 16 common enriched terms (Fig. 8E), from which only 3 cluster terms were exclusively enriched in the Gagaoua's database, these being actin-myosin filament sliding (GO:0033275), glycogen metabolism (R-HAS-8982491) and protein homodimerization activity (GO:0042803), while one term was exclusively enriched in the present work, this being oxidoreductase activity, acting on the aldehyde or oxo group of donors, NAD or NADP as acceptor (GO:0016620). At 44 h *postmortem*, two proteins (DHRS7B and FHL1) overlapped between studies (Fig. 8C). The hierarchical heatmap clustering of the DAPs revealed seven common enriched terms (Fig. 8F). Interestingly, one term was exclusively
enriched in the present database, this being NAD binding (GO:0051287), while 12 terms were
exclusively enriched in the Gagaoua's database only.

4. Discussion

The global beef chain has been directed to meet the beef quality demands of the market and consumers expectations. Purchasing decisions are primarily driven by lean beef color as consumers often consider color an indicator of freshness and wholesomeness (Suman & Joseph, 2013). For this reason, dark-cutting carcasses are discounted during meat grading and downgraded in value, leading to economic losses for the beef sector (Ponnampalam et al., 2017). The dark-cutting condition usually arises after cattle experience physical and psychological stress caused by several negative *ante mortem* factors such that muscle glycogen is depleted, hence impacting the rate of pH decline resulting in abnormally dark muscles (Terlouw et al., 2021; Ponnampalam et al., 2017; Tarrant, 1989). Thus, there is a need to strengthen our knowledge on the factors underpinning the biochemical mechanisms at interplay in meat quality traits development including dark-cutting beef condition.

The application of proteomics in meat research has recently yielded valuable insights on the biochemical processes underpinning meat quality development and variability over conventional biochemistry methods (Purslow, Gagaoua, & Warner, 2021; Gagaoua et al., 2024), hence contributing greatly to a better understanding of the complexity of the muscle proteome and its importance in determining several meat quality traits (Gagaoua, Schilling, Zhang, & Suman, 2022). Several recent studies reviewed in the Gagaoua's integromics meta-analysis (Gagaoua, Warner, et al., 2021) have used multiple approaches likely label-free proteomics, two-dimensional gel electrophoresis or OFFGEL proteomics approaches (Franco et al., 2015; Fuente-Garcia et al., 2019; Fuente-Garcia et al., 2020; Hughes, Clarke, Li, Purslow, & Warner, 2019; Kiyimba et al., 2021; Mahmood, Turchinsky, Paradis, Dixon, & Bruce, 2018; Poleti et al., 2018; Wu et al., 2020; Sentandreu et al., 2021) to decipher the mechanisms related to the dark-cutting development. All these studies focused on muscle samples taken 24 h or 48 h *postmortem*, which correspond also to the same time of pHu measurement on the chilled carcasses. However, to date and to the best of our knowledge, shotgun proteomics approach coupled with in-depth bioinformatics and applied in a serial time analysis to investigate the dynamic changes in early *postmortem* proteome of dark-cutting beef have never been studied.
In this study, the temporal proteomics (analyses of the muscle proteomes at several *postmortem* times of the same carcasses) of the LT *postmortem* muscle from normal and high pHu beef evidenced substantial dynamic changes along with the involvement of myriad and interconnected biological pathways, some of which were never revealed before to be related with dark-cutting beef determination. Interestingly, the hierarchical heatmap (Fig. 6) revealed that the significant enrichment of “generation of precursor metabolites and energy”, “cellular responses to stress”, “chaperone binding”, “oxidoreductase activity” and “muscle structure development” are common features and demonstrated to be the central role in driving the dark-cutting beef development. In addition, insights on how these complex and interconnected pathways are changing as the dark-cutting beef develops at each *postmortem* time are for the first time revealed. In the following sections, we discuss in-depth the functions and metabolic pathways to which certain of the DAPs belong, some of which may be proposed as candidate biomarkers to monitor the dynamic changes in early *postmortem* (30 min, 9 h and 44 h) proteome of dark-cutting beef.

**4.1. Pivotal role of energy metabolism at 30 min postmortem**

As expected, the energy metabolism was identified as one of the major signatures characterizing the differences in the LT muscle proteome between normal and high pHu beef at 30 min *postmortem* (Fig. 3). Interestingly, all the DAPs belonging to the TCA cycle (UGP2, ATIC and MDH1), mitochondrial respiration (ATP5B and NDUFA9) and β-oxidation (HADHA and ADIPOQ), which are known mechanisms responsible for producing energy during the initial events of the *postmortem* metabolism, were down-regulated in dark-cutting beef compared to normal-pH beef. UGP2, an enzyme involved in glycogen biosynthesis (Pescador et al., 2010), was up-regulated in normal-pH beef, suggesting lower glycogen or weak breakdown during peri-slaughter events in dark-cutting beef. Poleti et al. (2018) reported a greater abundance of UGP2 in muscle with higher glycogen concentration, which produced a normal-pH at 24 h *postmortem*. ATIC was also down-regulated in dark-cutting beef, which may have decreased the IMP/AMP (Antonelo et al., 2022) levels resulting in a lower phosphofructokinase activation and consequently decreased glycolysis (England, Matarneh, Scheffler, Wachet, & Gerrard, 2015), as also observed in earlier studies (Shen, Gerrard, & Du, 2008). Thus, as long as the glycolytic enzymes are not inhibited by the low pH during the early *postmortem* metabolism, glycolysis will
continue to contribute to the pH decline, as observed by the negative correlation of pH$_{45 min}$ with MDH1 (Gagaoua, Terlouw, et al., 2015), a protein that plays an essential role in the TCA cycle, further involved in apoptosis onset (Lee, Kim, Cho, & Youn, 2009).

Furthermore, *postmortem* energy metabolism and the rate of pH decline can also be affected by mitochondria (Dang et al., 2020; Matarneh, Yen, Bodmer, El-kadi, & Gerrard, 2021). The mitochondrial β-oxidation pathway is the major energy-producing process in tissues and is performed through consecutive reactions with a breakdown of fatty acids into acetyl-CoA. It has been reported that lipids, the essential regulators of cellular stabilization and metabolism, have a pivotal role in *postmortem* energy production and in sustaining the needed energy during early *postmortem* for cell death processes such as apoptosis (Ouali et al., 2013).

Interestingly, in the present study, HADHA and ADIPOQ were up-regulated in normal-pH beef, which suggests a more intense mitochondrial β-oxidation. Matarneh et al. (2017) showed that intact mitochondria slowed the rate of pH decline during the first 30 min *postmortem*, stabilizing ATP levels and reducing glycolytic flux. However, after 2 h *postmortem*, mitochondria have increased ATP hydrolysis, glycogen breakdown, lactate accumulation, and pH decline, which was associated to the activation of the ATP5B, named ATP synthase (F₁F₀ ATP synthase or Complex V), an important protein in the production of ATP from ADP in the respiratory chain (Matarneh, Beline, Luz, Shi, & Gerrard, 2018). Accordingly, another earlier study by Ishii, Shirai, Makino, & Nishikata, (2014) demonstrated that ATP5B increases ATP hydrolysis and then glycolytic flux. A very recent study by Schulte et al. (2022) reported a lower pH 1 h *postmortem* associated with a more intense glycolysis in normal-pH beef when compared to dark-cutting beef.

In the present study and for Nellore cattle, the lower abundance of mediators of the β-oxidation, TCA cycle and mitochondrial electron transport chain, which may suggest a lower mitochondrial activity even under energy deprivation, significantly contribute to delaying the rate and extent of pH decline during early *postmortem* in high pHu beef, which partially explain the initial events of the dark-cutting development, since this effect may be cumulative over *postmortem* time (Matarneh et al., 2018).
## 4.2. Pivotal role of ribosomal proteins and histones on the cellular response to stress at 30 min *postmortem*

The cellular responses to stress was identified as the major signature characterizing the differences in the LT muscle proteome between normal and high pHu beef at 30 min *postmortem* (Fig. 3). Among the candidate biomarkers, ribosomal proteins (RPL18A, RPL24, RPS9 and RPS18) were the central proteins to modify this pathway in dark-cutting beef. Greater abundance of ribosomal proteins may suggest that several proteins experiencing changes during early *postmortem* metabolism, as a resilient response of muscle cells to energy deprivation (Ubaida-Mohien et al., 2019), would play essential roles for the energy-demanding cell-death processes such as autophagy and apoptosis (Ouali et al., 2013).

In this study, EIF5A, a p53/TP53-dependent regulator of apoptosis that can be activated temporarily to promote cell survival and permanently to stimulate cell death (Green, Galluzzi, & Kroemer, 2014), was also up-regulated in dark-cutting beef. Such apoptotic stimulation can be achieved through mitochondrial Ca²⁺ overload (Giorgi et al., 2015), which inhibits autophagy, thereby delaying apoptosis onset (Tasdemir et al., 2008) and consequently activating the pro-apoptotic factors such as Bcl-2. In this sense, greater abundance of calcium regulation proteins (CAM2KA and CALR) is in good agreement with the cell deaths roles (apoptosis and autophagy) of EIF5A in dark-cutting beef.

Furthermore, greater abundance of histones (HIST3H2BB and HIST1H2AJ) observed to impact on the cellular responses to stress pathway in dark-cutting beef may be associated to the hallmarks of apoptosis, such as DNA fragmentation, condensation of chromatin and actin degradation, detected in muscles during the *postmortem* storage period (Gagaoua et al., 2015; Longo, Lana, Bottero, & Zolla, 2015; Ouali et al., 2013). Therefore, this study reveals for the first time a pivotal role of ribosomal proteins and histones in Nellore *Bos indicus* cattle as a response to stress, hence confirming recent findings on *Bos taurus* cattle reported by Kiyimba et al. (2023). Further studies are warranted to decipher their roles on the initial events of the dark-cutting beef development.

## 4.3. Pivotal role of energy metabolism and cellular response to stress on muscle structure and development at 9 h *postmortem*
A strong interconnectedness between the proteins and the functional pathways to which they belong was depicted in early (9 h) *postmortem* muscle. The findings revealed 14 common changing pathways, including the aforementioned energy metabolism and cellular responses to stress. The identification of a high number of DAPs from the energy metabolism pathway early *postmortem* phase is in a good agreement with previous studies (Gagaoua, Warner, et al., 2021; Gagaoua, Terlouw, Richardson, Hocquette, & Picard, 2019; Fuente-Garcia et al., 2020). Among them, UQCR1 and UQCR2 were down-regulated in dark-cutting beef. These subunits of the cytochrome bc1 complex are part of the mitochondrial respiratory chain involved in electron transport during oxidative phosphorylation and ATP synthesis (Mato et al., 2019) and have been associated to a normal pHu decline and beef color stability (Suman, Wang, Gagaoua, Kiyimba, & Ramanathan, 2023; Poleti et al., 2018; Yu et al., 2017). Moreover, several mediators of glycolysis were up-regulated in normal-pH beef, such as PKM and ENO3. Earlier studies from Spain on young bulls by Fuente-Garcia et al. (2020) and Sierra et al. (2021) further reported lower PKM abundance in high pHu beef, which can be explained by the early glycogen depletion causing a decrease in the glycolytic flux, as suggested in this study by the up-regulation of UGP2 in dark-cutting beef at 30 min *postmortem*. The glycolytic enzyme ENO3 plays an important role in pH decline and *postmortem* metabolism (Gagaoua, Bonnet, Ellies-Oury, Koning, & Picard, 2018b) and its down-regulation indicates a cellular stress response to deprivation of oxygen supply and differences in glucose levels (Sedoris, Thomas, & Miller, 2010). Furthermore, it was possible to observe that ENO3 interacts with other proteins in the PPI network (Fig. 4D), for example, heat shock proteins (Wulff, Jokumsen, Højrup, & Jessen, 2012), contractile proteins (Hughes, Clarke, Li, Purslow, & Warner, 2019) and oxidative stress (Gagaoua, Terlouw, Boudjellal, & Picard, 2015), showing the complex interactions involved in the pHu determination process.

Interestingly, in the present study, two heat shock proteins, HSPA8 and HSPA5, were observed to be up- and down-regulated, respectively, in dark-cutting beef at 9 h *postmortem*. HSPA8, known as HSP70, is an inducible HSP that is involved in several underlying mechanisms of meat tenderization (Gagaoua, Terlouw, et al., 2021), such as in the transport of proteins into the mitochondria (Wiedemann, Frazier, & Pfanner, 2004), in muscle structure, by binding to structural proteins or regulate cell signaling pathways (Daugaard, Rohde, & Jäättelä, 2007; Zhu et al., 2021) and in oxidative stress, by sequestering pro-apoptotic factors (Jiang et al., 2011).
However, HSPA5 (HSP70 family) has chaperone activity due to its binding to Ca²⁺, which corresponds to 25% of the Ca²⁺ reserves in the sarcoplasmic reticulum (Lièvremont, Rizzuto, Hendershot, & Meldolesi, 1997). Thus, when Ca²⁺ reserves decreasing or depleted, there is a greater amount of proteins in the sarcoplasmic reticulum leading to increased HSPA5 expression (Coe & Michalak, 2009). As HSPA5 is located mainly within the sarcoplasmic reticulum, the greater abundance of this HSP could be related to muscle fibers, since fast-twitch glycolytic fibers have a greater volume of sarcoplasmic reticulum (Rodrigues et al., 2017; Baylor & Hollingworth, 2012). Therefore, during *postmortem* time, apoptosis can affect skeletal muscle integrity through the modulation of Ca²⁺ flow with implications on muscle ultrastructural changes, hence playing a pivotal role in the determination of final meat quality (Gagaoua, Terlouw, et al., 2021; Gagaoua, Troy, & Mullen, 2021; Purslow, Gagaoua, & Warner, 2021).

In the present study and at 9h *postmortem*, muscle cell development and structural constituent of muscle were identified to be exclusively enriched in normal pHu beef only (Fig. 4E), which may indicate a severe disorganization of the sarcomere during early *postmortem* in normal-pHu beef due to a disruption of sarcoplasmic reticulum caused by apoptosis, hence increasing the activation of the endogenous proteases such as calpains and caspases, with central roles in proteolysis (Ertbjerg, 2022). Among the changing proteins, ACTA1, ACTN2, CAPN3 and MYOM2 participate in both enriched terms and were up-regulated in normal pHu compared to high pHu beef at 9 h *postmortem*. ACTA1 is the first protein targeted by caspases and is considered as a good hallmark of apoptosis onset (Ouali et al., 2013) while ACTN2 is an actin-binding protein that is the main component of skeletal muscle fibers, being expressed only in fast-twitch muscle fibers exhibiting high glycolytic activity (Lek, Quinlan, & North, 2009). The recent proteomics study by Schulte et al. (2022) demonstrated that similar abundances of HSP27 and ACTA1 may indicate an earlier onset of both apoptosis and *rigor mortis* in normal-pHu when compared to high-pHu beef. Moreover, CAPN3, the major intracellular protease, is especially found in type II fibers (Ojima et al., 2010). The down-regulation of HSP70 and CAPN3 in normal pHu may influence beef color determination (Gagaoua, Terlouw, et al., 2015) by changing reflectance and light scattering at the surface of meat cuts (Gagaoua, Terlouw, & Picard, 2017; Purslow, Gagaoua, & Warner, 2021). MYOM2, a structural protein, is also expressed in glycolytic fibers and has recently been associated to CKM expression (Gagaoua, Troy, & Mullen, 2021) since the rate of CKM fragmentation influences the rate of energy
depletion and the pH decline. Thus, the proposed disorganization of sarcomere during early *postmortem* in normal-pHu beef due to the proteolytic activity is closely related to the more glycolytic muscle metabolism when compared to the oxidative muscle metabolism in dark-cutting beef.

**4.4. Pivotal role of oxidoreductase activity and oxidative stress at 44 h postmortem**

The oxidoreductase activity was identified as the major signature characterizing the differences in the LT muscle proteome between normal and high pHu beef at 44 h *postmortem* (Fig. 5). Among the DAPs observed to be constitutive of this pathway, NDUFA7 (Complex I), NDUFA9 (Complex I) and CYC1 (Complex III) were up-regulated in normal-pHu beef while IDH3G was up-regulated in dark-cutting beef. To date and to the best of our knowledge, CYC1 was identified for the first time in this study to be associated with dark-cutting beef. CYC1 is a component of ubiquinol-cytochrome c oxidoreductase, a transmembrane complex of several subunits that is part of the mitochondrial electron transport chain that drives oxidative phosphorylation (Shojapour, Fatemi, Farahmand, & Shasaltaneh, 2021). IDH3G catalyzes the oxidative decarboxylation of isocitrate to 2-oxoglutarate in the TCA cycle, allowing for the concomitant reduction of NAD+ to NADH, which is then used to generate ATP through the electron transport chain (Okamoto et al., 2003). NADH is required in the electron transport chain for maximum ATP production (Gagaoua, Terlouw, et al., 2015), which promotes mitochondrial respiration and metmyoglobin reduction (Tang et al., 2005), thereby influencing the stability of beef color (Suman, Wang, Gagaoua, Kiyimba, & Ramanathan, 2023). Greater abundance of TCA intermediates along with an increased mitochondrial respiration capacity may be a conducive factor for increased mitochondrial oxygen consumption, thus contributing to muscle discoloration via myoglobin deoxygenation, producing dark-cutting beef (Gagaoua, Warner, et al., 2021; Kiyimba et al., 2022; Suman et al., 2023).

Although normal-pHu beef showed a higher impact on oxidoreductase activity, which proposes greater muscle discoloration as confirmed in the results of **Table 1**, several proteins related to the regulation of oxidative stress were up-regulated in this group, such as CYB5R3, DHRS7B, SOD1, SOD2 and PARK7, therefore suggesting greater color stability. For example, CYB5R3 is involved in many oxidation and reduction reactions, such as the reduction of methemoglobin to hemoglobin (Elahian, Sepehrizadeh, Moghimi, & Abbas, 2014). The study by
Zhai, Djimsa, Prenni, Woerner, & Belk (2020) reported that a greater abundance of CYB5R3 increases metabolic stability, inhibits the pro-apoptotic activity of the protein, increases autophagy and, among other functions, promotes late apoptosis in the LT muscle during the *postmortem* period. Like CYB5R3, DHRS7B is an oxidoreductase dependent on NAD or NADP as an acceptor and can promote exogenous carbonyl metabolism (Stambergova, Skarydova, Dunford, & Wsol, 2014), in which its overexpression may be beneficial to reduce carbonyl toxicity to cells in normal-pHu beef (Wu et al., 2020). SOD1 and SOD2 are two of the three superoxide dismutases responsible for destroying free superoxide radicals, acting as an antioxidant factor. López-Pedrouso et al. (2022) also reported a greater abundance of SOD2 in normal-pHu foal followed by a higher color stability. Moreover, PARK7 plays an important role against oxidative stress and cell death and previously reported as a robust biomarker of beef color (Gagaoua, Hughes, et al., 2020). PARK7 can interact with other pathways, such as heat shock proteins (Wu et al., 2020) and energy metabolism proteins (Picard & Gagaoua, 2020), thus delaying apoptotic processes (Gagaoua, Terlouw, et al., 2021) and influencing the ultimate pH and meat tenderization processes. Previous studies have also reported that PARK7 plays a role in determining beef color via mitochondrial protection (Gagaoua, Terlouw, & Picard, 2017; Yang et al., 2018; Wu et al., 2015). Therefore, the high levels of reactive oxygen species, the lack of response to oxidative stress, as well as a weak protein folding response may be other relevant pathways that promote dark-cutting beef condition (Gagaoua, Warner, et al., 2021), which were all observed in the present study.

**4.5. Comparison of the candidate dark-cutting beef protein biomarkers with the literature**

To gain further mechanistic insights on the underpinning pathways related to dark-cutting beef
development, functional annotations comparing the DAPs found in this study over *postmortem*
time with the dark-cutting beef proteome database described in a previous integromics meta-
analysis (Gagaoua, Warner, et al., 2021) were performed. The protein overlaps of the two
proteome repertoires showed 3, 23 and 2 common proteins at 30 min (Fig. 8A), 9 h (Fig. 8B) and
44 h (Fig. 8C) *postmortem*, respectively, mainly belonging to three biological pathways: energy
metabolism proteins, muscle structural proteins and response to oxidative stress. These findings
together with the previous conclusions from the integromics meta-analysis underline the
importance of energy metabolism, oxidative stress and muscle system process on the dark-cutting
beef development (Fig. 8D-F). Interestingly, the protein FHL1 was up-regulated in high pHu beef at 9 and 44 h *postmortem* (Fig. 8B-C), which may be considered as a putative biomarker for dark-cutting condition. FHL1 is recognized as a multifunctional protein, among which it acts in cell proliferation, apoptosis and interacts with metabolic enzymes in response to oxidative stress in muscle (Shathasivam, Kislinger, & Gramolini, 2010). In addition, it can regulate calcium homeostasis (Pillar et al., 2017), having an important role in determining the beef pHu and tenderness (Gagaoua, Bonnet, & Picard, 2020) since Ca²⁺ ions can contribute to the regulation of energy metabolism pathways (Carafoli, 2002) and also affect the enzymatic speed of several metabolic enzymes (Picard & Gagaoua, 2017). The up-regulation of FHL1 was also associated to the weakening of the Z-line in high pHu beef, as it is involved in the assembly of the cellular cytoskeleton structure (Hughes, Clarke, Purslow, & Warner, 2019). In this sense, Lomiwes, Farouk, Wu, & Young (2014) reported that high pHu muscles showed greater degradation of cytoskeletal proteins, such as titin, nebulin and filamin, associated with a up-regulation of FHL1.

The results further revealed a weak overlap in the common proteins, especially at 48h *postmortem*, the usual timing used to assess the proteome-basis of dark-cutting beef in previous study. The confirmed disparity in the protein overlap among dark-cutting beef proteomics study warrants special attention to consider the multidimensional factors behind this quality defect condition.

**5. Conclusion**

This study revealed important insights regarding the dynamic changes that occur in early *postmortem* of normal and high muscle pHu beef, validating specific biochemical pathways to determine the biological mechanisms underpinning the dark-cutting development. In comparison to the previous proteomics studies on dark-cutting beef that used 24 or 48 *postmortem* sampling times only, this study added novel knowledge on the complexity of the mechanisms when they were explored in a temporal manner by additionally considering sampling times of 30 min and 9h *postmortem*. Moreover, the temporal shotgun proteomics coupled with an in-depth bioinformatics analysis allowed for the first time to evidence the dynamic time-course changes and disparity in the molecular signatures underpinning the dark-cutting beef condition development. The pivotal role of energy metabolism, cellular response to stress, muscle system process and oxidoreductase activity were significantly revealed throughout the early *postmortem* metabolism in Nellore *Bos*
* indicus cattle. Twenty-three proteins overlap among *postmortem* times (30 min, 9 h and 44 h *postmortem*) and may be suggested as candidate biomarkers to monitor the proteome changes in the dark-cutting development. Moreover, it is interesting to underline that ribosomal proteins and histones were revealed to affect significantly the cellular response to stress during the initial events of dark-cutting development. Finally, the sampling time seemed a key factor in exploring the underlying mechanisms of dark-cutting beef, hence asking for standardization of the time allowing the discovery of biomarkers of this quality defect to allow generic comparisons among studies. This would further reduce the large disparity we are still observing in the proteomics studies of dark-cutting beef condition.

**CRediT authorship contribution statement**

**Iliani Patinho:** Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – original draft. **Daniel S. Antonelo:** Formal Analysis, Software, Visualization, Writing – review & editing. **Eduardo F. Delgado:** Writing – review & editing. **Laura Alessandroni:** Data curation, Formal Analysis, Visualization, Writing – review & editing. **Julio C.C. Balieiro:** Supervision, Writing – review & editing. **Carmen J. Contreras-Castillo:** Conceptualization, Funding acquisition, Supervision, Writing – review & editing. **Mohammed Gagaoua:** Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

**Acknowledgments**

This work was supported by the São Paulo Research Foundation FAPESP (grant # 2017/26667-2; 2019/26026-2).

**Conflict of interest**

No potential conflict of interest was reported by the authors.

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Zhai, C., Djimsa, B. A., Prenni, J. E., Woerner, D. R., & Belk, K. E. (2020). Tandem mass tag labeling to characterize muscle-specific proteome changes in beef during early postmortem period. *Journal of Proteomics*, **222**, 103794. https://doi.org/10.1016/j.jprot.2020.103794
Zhao, Y., Kong, X., Yang, X., Zhu, L., Liang, R., Luo, X., ... Zhang, Y. (2022). Effect of energy metabolism and proteolysis on the toughness of intermediate ultimate pH beef. *Meat Science, 188*, 108798. https://doi.org/10.1016/j.meatsci.2022.108798

Zhu, Y., Gagaoua, M., Mullen, A. M., Kelly, A. L., Sweeney, T., Cafferky, J., ... Hamill, R. M. (2021). A Proteomic Study for the Discovery of Beef Tenderness Biomarkers and Prediction of Warner–Bratzler Shear Force Measured on Longissimus thoracis Muscles of Young Limousin-Sired Bulls. *Foods, 10*, 952. https://doi.org/10.3390/foods10050952
## Figure captions

**Fig. 1.** pH declines of the carcasses used in this trial. **A)** Individual pH decline curves of the carcasses, which are further depicted with the average curves comparing the normal (n = 5) and high (n = 5) pH groups. The blue and red lines indicate the pH values of pH 6.0 and pH 5.8 on which the meat can be considered dark-cutting meat at 48h *postmortem*. **B)** ANOVA analysis comparing the pH values over *postmortem* times between normal and high pH groups. (Significance: ns: not significant, **P** < 0.01; ***P** < 0.001).

**Fig. 2.** Principal component analysis (PCA). **A)** First principal components (axis) depicting the relationships among the meat quality traits: pH traits over *postmortem* times, color traits, shear force and cook loss, all evaluated at 72h *postmortem*. **B)** Bi-plot highlighting the extent of separation of the carcasses from high and normal pH groups. **C)** Individual and overall Kaiser-Meyer-Olkin (KMO) computed for the PCA.

**Fig. 3.** Differentially abundant proteins (DAPs) between high and normal ultimate pH (pHu) beef at 30 min *postmortem*. **A)** Volcano plot showing the proteins significantly different (fold change ≥ 1.2 and *P*-value ≤ 0.05) between high and normal pHu beef at 30 min *postmortem*. The up- and down-regulated proteins from high *versus* normal pHu beef are shown in red and blue, respectively (details on the full names of the proteins are given in **Table 2**). **B)** Functional enrichment analysis using Metascape®. The top 12 enriched and significant terms ranked by their p-value are given. Stronger colors represent greater significant enrichment. Functional enrichment was performed by selecting gene ontology (GO) Biological Processes, GO Molecular Function, Reactome gene sets, and KEGG pathway as ontology sources. **C)** Functional enrichment network using Metascape®. Each enriched cluster term is presented with the corresponding color, where nodes that share the same cluster ID are typically close to each other. The sizes of the nodes reflect the enrichment significance of the terms. **D)** Protein-protein interaction analysis of the 33 DAPs using STRING database clustered into molecular functions and biological pathways using manual annotations. **E)** Hierarchical heatmap clustering to compare the enriched GO terms within the up- and down-regulated proteins. The heatmap colored by the p-values are indicated by color, where grey cells indicate a lack of significant enrichment in that GO, palest brown indicates a low p-value and darkest brown indicates a high p-value in each of the corresponding pHu class. The statistics details of each enriched term are given in **Table S1**.

**Fig. 4.** Differentially abundant proteins (DAPs) between high and normal ultimate pH (pHu) beef at 9 h *postmortem*. **A)** Volcano plot showing the proteins significantly different (fold change ≥ 1.2 and *P*-value ≤ 0.05) between high and normal pHu beef at 9 h *postmortem*. The up- and down-regulated proteins from high *versus* normal pHu beef are shown in red and blue, respectively (details on the full names of the proteins are given in **Table 3**). **B)** Functional enrichment analysis using Metascape®. The top 20 enriched and significant terms ranked by their p-value are given. Stronger colors represent greater significant enrichment. Functional enrichment was performed by selecting gene ontology (GO) Biological Processes, GO Molecular Function, Reactome gene sets, and KEGG pathway as ontology sources. **C)** Functional enrichment network using Metascape®. Each enriched cluster term is presented with the corresponding color, where nodes that share the same cluster ID are typically close to each other. The sizes of the nodes reflect the enrichment significance of the terms. **D)** Protein-protein interaction analysis of the 181 DAPs using STRING database clustered into molecular functions and biological pathways using manual annotations. **E)** Hierarchical heatmap clustering to
compare the enriched GO terms within the up- and down-regulated proteins. The heatmap colored by the p-values are indicated by color, where grey cells indicate a lack of significant enrichment in that GO, palest brown indicates a low p-value and darkest brown indicates a high p-value in each of the corresponding pHu class. The statistics details of each enriched term are given in Table S2.

**Fig. 5.** Differentially abundant proteins (DAPs) between high and normal ultimate pH (pHu) beef at 44 h *postmortem*. **A)** Volcano plot showing the proteins significantly different (fold change ≥ 1.2 and *P*-value ≤ 0.05) between high and normal pHu beef at 44 h *postmortem*. The up- and down-regulated proteins from high *versus* normal pHu beef are shown in red and blue, respectively (details on the full names of the proteins are given in **Table 4**). **B)** Functional enrichment analysis using Metascape®. The top 19 enriched and significant terms ranked by their p-value are given. Stronger colors represent greater significant enrichment. Functional enrichment was performed by selecting gene ontology (GO) Biological Processes, GO Molecular Function, Reactome gene sets, and KEGG pathway as ontology sources. **C)** Functional enrichment network using Metascape®. Each enriched cluster term is presented with the corresponding color, where nodes that share the same cluster ID are typically close to each other. The sizes of the nodes reflect the enrichment significance of the terms. **D)** Protein-protein interaction analysis of the 37 DAPs using STRING database clustered into molecular functions and biological pathways using manual annotations. **E)** Hierarchical heatmap clustering to compare the enriched GO terms within the up- and down-regulated proteins. The heatmap colored by the p-values are indicated by color, where grey cells indicate a lack of significant enrichment in that GO, palest brown indicates a low p-value and darkest brown indicates a high p-value in each of the corresponding pHu class. The statistics details of each enriched term are given in **Table S3**.

**Fig. 6.** Biological pathway and process enrichment analysis on the total differentially abundant proteins (DAPs) proteins (n = 251) identified to change between high and normal ultimate pH (pHu) beef at 30 min, 9 h and 44 h *postmortem*. **A)** Circos plot depicting the overlap between proteins across the *postmortem* times. Twenty-three common proteins according to *postmortem* time comparison are given. **B)** Functional enrichment analysis of the 23 common proteins using Metascape®. The top 8 enriched and significant terms ranked by their p-value are given. Stronger colors represent greater significant enrichment. Functional enrichment was performed by selecting gene ontology (GO) Biological Processes, GO Molecular Function, Reactome gene sets, and KEGG pathway as ontology sources. **C)** Functional enrichment network using Metascape®. Each enriched cluster term is presented with the corresponding color, where nodes that share the same cluster ID are typically close to each other. The sizes of the nodes reflect the enrichment significance of the terms. **D)** Hierarchical clustering analysis built using the 23 common (DAPs) proteins given in the Circos plot. The heatmap shows the relative abundances of the proteins using only pH group/time averages. The details on the protein names that are up and down, refer to **Table 2**, **Table 3** and **Table 4**. **E)** Protein-protein interaction analysis of the 23 common proteins using STRING database clustered into molecular functions and biological pathways using manual annotations. **F)** Hierarchical heatmap clustering to compare the enriched GO terms within the *postmortem* time comparison. The heatmap colored by the p-values are indicated by color, where grey cells indicate a lack of significant enrichment in that GO, palest brown indicates a low p-value and darkest brown indicates a high p-value in each of the corresponding *postmortem* time.
**Fig. 7.** Secretome analysis. The secretome analyses have been performed for the 3 protein lists (30min, 9h and 44h). The proteins secreted through classical pathways or non-classical pathways are given. SERPINC1 depicted in gold character was a common secreted protein through classical pathways for both 9h and 44h *postmortem*.

**Fig. 8.** Biological pathway and process enrichment analysis on the total differentially abundant proteins (DAPs) proteins (n = 251) identified to change between high and normal ultimate pH (pHu) beef at 30 min, 9 h and 44 h *postmortem* in comparison with the dark-cutting database from Gagaoua et al. (2021). Circos plot depicting the overlap between proteins across the studies at 30 min (A), 9 h (B) and 44 h (C) *postmortem*. Each outside arc represents one study with a different color. On the inside, the dark orange color represents the proteins that appear in multiple lists and the light orange color represents proteins that are unique to that protein list; and purple lines link the same protein (gene name) that are shared by the input. The length of the outside arcs is related to the number of proteins in each list. Hierarchical Heatmap clustering indicating the first top 20 enriched gene ontology (GO) terms at 30 min (D), 9 h (E) and 44 h (F) *postmortem*. Functional enrichment was performed using Metascape® by selecting gene ontology (GO) Biological Processes, GO Molecular Function, Reactome gene sets, and KEGG pathway as ontology sources. The heatmap colored by the p-values are indicated by color, where grey cells indicate a lack of significant enrichment in that GO, palest brown indicates a low p-value and darkest brown indicates a high p-value in each of the corresponding study.
<figure><img src="image_1.png" /></figure>

<table><caption>Fig. 1.</caption><thead><tr><th rowspan="2">pH group</th><th colspan="7">p-m time</th></tr><tr><th>30min</th><th>3h</th><th>6h</th><th>9h</th><th>24h</th><th>44h</th><th>72h</th></tr></thead><tbody><tr><td>High pH</td><td>6.56</td><td>6.55</td><td>6.53</td><td>6.58</td><td>6.55</td><td>6.52</td><td>6.42</td></tr><tr><td>Normal pH</td><td>6.31</td><td>5.92</td><td>5.69</td><td>5.74</td><td>5.72</td><td>5.71</td><td>5.58</td></tr><tr><td>SEM</td><td>0.07</td><td>0.12</td><td>0.15</td><td>0.14</td><td>0.14</td><td>0.14</td><td>0.14</td></tr><tr><td>Significance</td><td>ns</td><td>**</td><td>***</td><td>***</td><td>***</td><td>***</td><td>***</td></tr></tbody></table>
A

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B

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C

<table><caption>Fig. 2.</caption><thead><tr><th>Variables</th><th>L*</th><th>a*</th><th>b*</th><th>C*</th><th>h°</th><th>MetMb, %</th><th>DeoxyMb, %</th><th>OxyMb, %</th><th>Surface color stability</th><th>Cooking loss, %</th><th>WBSF, N</th><th>pH 30min</th><th>pH 3h</th><th>pH 6h</th><th>pH 9h</th><th>pH 24h</th><th>pH 44h</th><th>pH 72h</th><th>Overall KMO</th></tr></thead><tbody><tr><td>KMO values</td><td>0.64</td><td>0.72</td><td>0.81</td><td>0.73</td><td>0.66</td><td>0.63</td><td>0.72</td><td>0.79</td><td>0.62</td><td>0.49</td><td>0.68</td><td>0.83</td><td>0.81</td><td>0.69</td><td>0.76</td><td>0.65</td><td>0.62</td><td>0.70</td><td>0.70</td></tr></tbody></table>
A

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B

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C

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D

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E

<figure><img src="image_8.png" /><figcaption>Fig. 3.</figcaption></figure>
A

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B

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C

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D

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E

<figure><img src="image_13.png" /><figcaption>Fig. 4.</figcaption></figure>
A

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B

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C

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D

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E

<figure><img src="image_18.png" /><figcaption>Fig. 5.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>Fig. 6.</figcaption></figure>
<table><caption>Fig. 7.</caption><thead><tr><th></th><th>pH group</th><th>Bovine Uniprot ID</th><th>Gene Name</th><th>Localizations</th><th>Signals</th><th>Cytoplasm</th><th>Nucleus</th><th>Extracellular</th><th>Cell membrane</th><th>Mitochondrion</th><th>Plastid</th><th>Endoplasmic reticulum</th><th>Lysosome / Vacuole</th><th>Golgi apparatus</th><th>Peroxisome</th><th>Secretion pathways</th></tr></thead><tbody><tr><td rowspan="2">30 min</td><td>Q3Y5Z3</td><td>ADIPOQ</td><td>Extracellular</td><td></td><td>Signal peptide</td><td>0.20</td><td>0.13</td><td>0.94</td><td>0.20</td><td>0.07</td><td>0.00</td><td>0.16</td><td>0.27</td><td>0.14</td><td>0.00</td><td rowspan="2">Classical</td></tr><tr><td>P52193</td><td>CALR</td><td>Extracellular | Endoplasmic reticulum</td><td></td><td>Signal peptide</td><td>0.23</td><td>0.09</td><td>0.69</td><td>0.19</td><td>0.07</td><td>0.01</td><td>0.78</td><td>0.30</td><td>0.35</td><td>0.03</td></tr><tr><td rowspan="10">9H</td><td>F1MD77</td><td>LAMC1</td><td>Extracellular</td><td></td><td>Signal peptide</td><td>0.21</td><td>0.11</td><td>0.89</td><td>0.41</td><td>0.12</td><td>0.01</td><td>0.34</td><td>0.27</td><td>0.37</td><td>0.01</td><td rowspan="10"></td></tr><tr><td>F1N184</td><td>NXPE4</td><td>Extracellular</td><td></td><td>Signal peptide</td><td>0.39</td><td>0.27</td><td>0.70</td><td>0.37</td><td>0.24</td><td>0.01</td><td>0.24</td><td>0.21</td><td>0.30</td><td>0.02</td></tr><tr><td>P12763</td><td>AHSG</td><td>Extracellular</td><td></td><td>Signal peptide</td><td>0.16</td><td>0.08</td><td>0.94</td><td>0.11</td><td>0.06</td><td>0.03</td><td>0.15</td><td>0.31</td><td>0.22</td><td>0.00</td></tr><tr><td>P41361</td><td>SERPINC1</td><td>Extracellular</td><td></td><td>Signal peptide</td><td>0.20</td><td>0.06</td><td>0.91</td><td>0.23</td><td>0.08</td><td>0.01</td><td>0.32</td><td>0.22</td><td>0.31</td><td>0.01</td></tr><tr><td>P81287</td><td>ANXA5</td><td>Cytoplasm | Extracellular | Lysosome/Vacuole</td><td>NaN</td><td></td><td>0.77</td><td>0.47</td><td>0.68</td><td>0.45</td><td>0.19</td><td>0.01</td><td>0.10</td><td>0.63</td><td>0.03</td><td>0.06</td></tr><tr><td>Q1LZE9</td><td>PRSS23</td><td>Extracellular</td><td></td><td>Signal peptide</td><td>0.11</td><td>0.08</td><td>0.93</td><td>0.28</td><td>0.08</td><td>0.01</td><td>0.18</td><td>0.19</td><td>0.13</td><td>0.02</td></tr><tr><td>Q29443</td><td>TF</td><td>Extracellular</td><td></td><td>Signal peptide</td><td>0.24</td><td>0.07</td><td>0.84</td><td>0.46</td><td>0.19</td><td>0.03</td><td>0.46</td><td>0.38</td><td>0.32</td><td>0.00</td></tr><tr><td>Q3SZV7</td><td>HPX</td><td>Extracellular</td><td></td><td>Signal peptide</td><td>0.20</td><td>0.12</td><td>0.89</td><td>0.27</td><td>0.24</td><td>0.00</td><td>0.30</td><td>0.42</td><td>0.37</td><td>0.01</td></tr><tr><td>F1MBP8</td><td>COPS5</td><td>Cytoplasm | Nucleus | Lysosome/Vacuole</td><td>Nuclear export signal</td><td></td><td>0.57</td><td>0.56</td><td>0.00</td><td>0.38</td><td>0.26</td><td>0.01</td><td>0.18</td><td>0.59</td><td>0.07</td><td>0.12</td></tr><tr><td>O18738</td><td>DAG1</td><td>Cell membrane</td><td></td><td>Signal peptide | Transmembrane domain</td><td>0.18</td><td>0.12</td><td>0.40</td><td>0.79</td><td>0.06</td><td>0.01</td><td>0.16</td><td>0.38</td><td>0.26</td><td>0.02</td><td rowspan="3">Non-classical</td></tr><tr><td>F1N206</td><td>DLD</td><td>Mitochondrion</td><td></td><td>Mitochondrial transit peptide</td><td>0.14</td><td>0.11</td><td>0.07</td><td>0.07</td><td>0.91</td><td>0.10</td><td>0.04</td><td>0.03</td><td>0.06</td><td>0.03</td></tr><tr><td>P10103</td><td>HMGB1</td><td>Cytoplasm | Nucleus</td><td>Nuclear localization signal</td><td></td><td>0.53</td><td>0.73</td><td>0.38</td><td>0.24</td><td>0.14</td><td>0.01</td><td>0.08</td><td>0.05</td><td>0.12</td><td>0.02</td></tr><tr><td>44h</td><td>P41361</td><td>SERPINC1</td><td>Extracellular</td><td></td><td>Signal peptide</td><td>0.20</td><td>0.06</td><td>0.91</td><td>0.23</td><td>0.08</td><td>0.01</td><td>0.32</td><td>0.22</td><td>0.31</td><td>0.01</td><td>Classical</td></tr></tbody></table>
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<figure><img src="image_25.png" /><figcaption>Fig. 8.</figcaption></figure>
<table><caption>**Table 1.** ANOVA comparison of the meat quality traits evaluated at 72h between normal and high pH groups.</caption><thead><tr><th>Meat quality traits</th><th>Normal pH</th><th>High pH</th><th>SEM</th><th>P-values</th></tr></thead><tbody><tr><td colspan="5"><strong>pH traits</strong></td></tr><tr><td>pH 24 h</td><td>5.72</td><td>6.55</td><td>0.14</td><td>***</td></tr><tr><td>pH 44 h</td><td>5.71</td><td>6.52</td><td>0.14</td><td>***</td></tr><tr><td>pH 72 h</td><td>5.58</td><td>6.42</td><td>0.14</td><td>***</td></tr><tr><td colspan="5"><strong>Color traits at 72h</strong></td></tr><tr><td>Lightness (L*)</td><td>39.65</td><td>34.50</td><td>1.26</td><td>*</td></tr><tr><td>Redness (a*)</td><td>23.88</td><td>20.87</td><td>0.63</td><td>**</td></tr><tr><td>Yellowness (b*)</td><td>15.04</td><td>12.53</td><td>0.58</td><td>*</td></tr><tr><td>Chroma (C*)</td><td>28.22</td><td>24.34</td><td>0.84</td><td>*</td></tr><tr><td>Hue angle (h°)</td><td>32.11</td><td>30.96</td><td>0.36</td><td>ns</td></tr><tr><td>% Methmyoglobin (MetMb, %)</td><td>17.24</td><td>18.61</td><td>0.59</td><td>ns</td></tr><tr><td>% Deoxymyoglobin (DeoxyMb, %)</td><td>22.28</td><td>29.08</td><td>1.69</td><td>*</td></tr><tr><td>% Oxymyoglobin (OxyMb, %)</td><td>60.48</td><td>52.31</td><td>1.84</td><td>*</td></tr><tr><td>Surface color stability</td><td>4.69</td><td>4.43</td><td>0.09</td><td>ns</td></tr><tr><td colspan="5"><strong>Texture traits at 72h</strong></td></tr><tr><td>Cooking loss, %</td><td>24.40</td><td>20.83</td><td>1.21</td><td>ns</td></tr><tr><td>Warner-Bratzler Shear force (72h), N</td><td>96.08</td><td>36.85</td><td>11.5</td><td>***</td></tr></tbody></table>
<table><caption>**Table 2.** List of the differentially abundant proteins (DAPs) between high and normal ultimate pH (pHu) beef at 30 min *postmortem* (n = 33).</caption><thead><tr><th>Uniprot ID</th><th>Full protein name</th><th>Gene name</th><th>Regulation in high pH group</th><th>Log2(FC) (high/normal)</th><th>Log10(P -value)</th></tr></thead><tbody><tr><td>Q3Y5Z3</td><td>Adiponectin</td><td>ADIP OQ</td><td>Down</td><td>-0.45</td><td>1.80</td></tr><tr><td>P13214</td><td>Annexin A4</td><td>ANXA4</td><td>Down</td><td>-0.41</td><td>1.74</td></tr><tr><td>Q0VCK0</td><td>Bifunctional purine biosynthesis protein PURH</td><td>ATIC</td><td>Down</td><td>-0.35</td><td>1.59</td></tr><tr><td>P00829</td><td>ATP synthase subunit beta, mitochondrial</td><td>ATP5B</td><td>Down</td><td>-0.44</td><td>1.51</td></tr><tr><td>Q2KJ23</td><td>Bridging integrator 1</td><td>BIN1</td><td>Down</td><td>-0.40</td><td>1.31</td></tr><tr><td>Q27970</td><td>Calpain-1 catalytic subunit</td><td>CAPN1</td><td>Down</td><td>-0.44</td><td>2.06</td></tr><tr><td>Q5EAC6</td><td>Hsp90 co-chaperone Cdc37</td><td>CDC37</td><td>Down</td><td>-2.08</td><td>2.27</td></tr><tr><td>F1MKP6</td><td>Cadherin-13</td><td>CDH13</td><td>Down</td><td>-1.54</td><td>2.35</td></tr><tr><td>F1MPU0</td><td>Clathrin heavy chain</td><td>CLTC</td><td>Down</td><td>-0.60</td><td>1.54</td></tr><tr><td>Q29RK1</td><td>Citrate synthase, mitochondrial</td><td>CS</td><td>Down</td><td>-0.63</td><td>2.39</td></tr><tr><td>Q3MHW9</td><td>NADH-cytochrome b5 reductase 1</td><td>CYB5R1</td><td>Down</td><td>-0.98</td><td>1.40</td></tr><tr><td>P0C591</td><td>Dual specificity phosphatase DUPD1</td><td>DUPD1</td><td>Down</td><td>-0.40</td><td>1.98</td></tr><tr><td>Q3SZ00</td><td>HADHA protein</td><td>HADHA</td><td>Down</td><td>-0.45</td><td>1.34</td></tr><tr><td>O77784</td><td>Isocitrate dehydrogenase [NAD] subunit beta, mitochondrial</td><td>IDH3B</td><td>Down</td><td>-0.82</td><td>1.74</td></tr><tr><td>G3MZU6</td><td>Immunoglobulin-like and fibronectin type III domain-containing protein 1</td><td>IGFN1</td><td>Down</td><td>-1.15</td><td>2.18</td></tr><tr><td>Q3T145</td><td>Malate dehydrogenase, cytoplasmic</td><td>MDH1</td><td>Down</td><td>-0.50</td><td>2.33</td></tr><tr><td>F1N7X3</td><td>Nucleosome assembly protein 1-like 4</td><td>NAP1L4</td><td>Down</td><td>-0.84</td><td>1.46</td></tr><tr><td>P34943</td><td>NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 9, mitochondrial</td><td>NDUFA9</td><td>Down</td><td>-0.64</td><td>2.17</td></tr><tr><td>F1MLD0</td><td>4'-phosphopantetheine phosphatase</td><td>PANK4</td><td>Down</td><td>-0.41</td><td>1.35</td></tr><tr><td>F1MWE0</td><td>Homologous-pairing protein 2 homolog</td><td>PSMC3</td><td>Down</td><td>-0.48</td><td>1.55</td></tr><tr><td>A7MBA2</td><td>PSMD1 protein</td><td>PSMD1</td><td>Down</td><td>-0.36</td><td>1.78</td></tr><tr><td>P12234</td><td>Phosphate carrier protein, mitochondrial</td><td>SLC25A3</td><td>Down</td><td>-0.63</td><td>1.64</td></tr><tr><td>A3KMV5</td><td>Ubiquitin-like modifier-activating enzyme 1</td><td>UBA1</td><td>Down</td><td>-0.42</td><td>1.91</td></tr><tr><td>Q07130</td><td>UTP--glucose-1-phosphate uridylyltransferase</td><td>UGP2</td><td>Down</td><td>-0.42</td><td>1.35</td></tr><tr><td>A5D7J6</td><td>CALR protein</td><td>CALR</td><td>Up</td><td>0.68</td><td>1.45</td></tr><tr><td>Q08E45</td><td>Calcium/calmodulin-dependent protein kinase II alpha</td><td>CAMK2A</td><td>Up</td><td>1.68</td><td>2.51</td></tr><tr><td>Q6EWQ7</td><td>Eukaryotic translation initiation factor 5A-1</td><td>EIF5A</td><td>Up</td><td>1.31</td><td>1.39</td></tr></tbody></table>
<table><caption>**Table 3.** List of the differentially abundant proteins (DAPs) between high and normal ultimate pH (pHu) beef at 9 h postmortem (n = 181).</caption><tr><td>A0A0A<br>0MPA2</td><td>Histone H2A</td><td>HIST1<br>H2AJ</td><td>Up</td><td>1.03</td><td>1.41</td></tr><tr><td>E1B8G9</td><td>Histone H2B</td><td>HIST3<br>H2BB</td><td>Up</td><td>1.03</td><td>1.71</td></tr><tr><td>Q3T003</td><td>60S ribosomal protein L18a</td><td>RPL18<br>A</td><td>Up</td><td>1.11</td><td>1.53</td></tr><tr><td>Q862I1</td><td>60S ribosomal protein L24</td><td>RPL24</td><td>Up</td><td>1.15</td><td>1.83</td></tr><tr><td>Q3T0R1</td><td>40S ribosomal protein S18</td><td>RPS18</td><td>Up</td><td>2.07</td><td>2.05</td></tr><tr><td>A6QLG<br>5</td><td>40S ribosomal protein S9</td><td>RPS9</td><td>Up</td><td>1.11</td><td>1.75</td></tr></table>

<table><thead><tr><th>Uniprot ID</th><th>Full protein name</th><th>Gene name</th><th>Regulation in high pH group</th><th>Log2(FC) (high/normal)</th><th>Log10(P -value)</th></tr></thead><tbody><tr><td>P44818</td><td>Very long-chain specific acyl-CoA dehydrogenase, mitochondrial</td><td>ACADV L</td><td>Down</td><td>-0.38</td><td>1.56</td></tr><tr><td>P68138</td><td>Actin, alpha skeletal muscle</td><td>ACTA1</td><td>Down</td><td>-0.69</td><td>1.54</td></tr><tr><td>Q3ZC55</td><td>Alpha-actinin-2</td><td>ACTN2</td><td>Down</td><td>-0.49</td><td>2.52</td></tr><tr><td>P07033</td><td>Acylphosphatase-2</td><td>ACYP2</td><td>Down</td><td>-0.93</td><td>1.36</td></tr><tr><td>A7YWE 4</td><td>Delta-1-pyrroline-5-carboxylate dehydrogenase, mitochondrial</td><td>ALDH4 A1</td><td>Down</td><td>-1.27</td><td>1.85</td></tr><tr><td>F1N7K8</td><td>Methylmalonate-semialdehyde dehydrogenase [acylating], mitochondrial</td><td>ALDH6 A1</td><td>Down</td><td>-0.74</td><td>1.37</td></tr><tr><td>F1N5U0</td><td>Alpha-protein kinase 3</td><td>ALPK3</td><td>Down</td><td>-2.28</td><td>2.69</td></tr><tr><td>A2VE25</td><td>ANK1 protein</td><td>ANK1</td><td>Down</td><td>-1.80</td><td>1.84</td></tr><tr><td>F1MD6 6</td><td>Annexin</td><td>ANXA1 1</td><td>Down</td><td>-0.82</td><td>1.64</td></tr><tr><td>P13214</td><td>Annexin A4</td><td>ANXA4</td><td>Down</td><td>-0.57</td><td>1.94</td></tr><tr><td>P00829</td><td>ATP synthase subunit beta, mitochondrial</td><td>ATP5B</td><td>Down</td><td>-0.29</td><td>1.58</td></tr><tr><td>P13619</td><td>ATP synthase F(0) complex subunit B1, mitochondrial</td><td>ATP5F1</td><td>Down</td><td>-0.93</td><td>1.61</td></tr><tr><td>P13621</td><td>ATP synthase subunit O, mitochondrial</td><td>ATP5O</td><td>Down</td><td>-0.55</td><td>2.54</td></tr><tr><td>E1BL29</td><td>Bleomycin hydrolase</td><td>BLMH</td><td>Down</td><td>-0.91</td><td>1.66</td></tr><tr><td>G3X7Q 7</td><td>Voltage-dependent L-type calcium channel subunit beta-1</td><td>CACNB 1</td><td>Down</td><td>-0.89</td><td>1.49</td></tr><tr><td>A6QP82</td><td>Calpain 3, (P94)</td><td>CAPN3</td><td>Down</td><td>-1.13</td><td>1.42</td></tr><tr><td>Q9XSC 6</td><td>Creatine kinase M-type</td><td>CKM</td><td>Down</td><td>-0.52</td><td>1.75</td></tr><tr><td>F1MBP 8</td><td>COP9 signalosome complex subunit 5</td><td>COPS5</td><td>Down</td><td>-1.40</td><td>1.62</td></tr><tr><td>F1MDK 0</td><td>COP9 signalosome complex subunit 7a</td><td>COPS7A</td><td>Down</td><td>-1.12</td><td>2.01</td></tr></tbody></table>
<table>
  <tr>
    <td>Q29RI0</td>
    <td>Atypical kinase COQ8A, mitochondrial</td>
    <td>COQ8A</td>
    <td>Down</td>
    <td>-0.72</td>
    <td>2.32</td>
  </tr>
  <tr>
    <td>P00423</td>
    <td>Cytochrome c oxidase subunit 4 isoform 1, mitochondrial</td>
    <td>COX4I1</td>
    <td>Down</td>
    <td>-1.03</td>
    <td>1.47</td>
  </tr>
  <tr>
    <td>P07471</td>
    <td>Cytochrome c oxidase subunit 6A2, mitochondrial</td>
    <td>COX6A2</td>
    <td>Down</td>
    <td>-1.55</td>
    <td>2.03</td>
  </tr>
  <tr>
    <td>F1N7T1</td>
    <td>NADH-cytochrome b5 reductase</td>
    <td>CYB5R3</td>
    <td>Down</td>
    <td>-0.82</td>
    <td>2.42</td>
  </tr>
  <tr>
    <td>P00125</td>
    <td>Cytochrome c1, heme protein, mitochondrial</td>
    <td>CYC1</td>
    <td>Down</td>
    <td>-0.78</td>
    <td>1.39</td>
  </tr>
  <tr>
    <td>E1BDM8</td>
    <td>Probable ATP-dependent RNA helicase DDX6</td>
    <td>DDX6</td>
    <td>Down</td>
    <td>-2.54</td>
    <td>1.97</td>
  </tr>
  <tr>
    <td>F1N206</td>
    <td>Dihydrolipoyl dehydrogenase</td>
    <td>DLD</td>
    <td>Down</td>
    <td>-0.70</td>
    <td>2.26</td>
  </tr>
  <tr>
    <td>A5D989</td>
    <td>Elongation factor 1-delta</td>
    <td>EEF1D</td>
    <td>Down</td>
    <td>-0.95</td>
    <td>1.55</td>
  </tr>
  <tr>
    <td>F1MG05</td>
    <td>Elongation factor 1-gamma</td>
    <td>EEF1G</td>
    <td>Down</td>
    <td>-0.88</td>
    <td>2.11</td>
  </tr>
  <tr>
    <td>P68102</td>
    <td>Eukaryotic translation initiation factor 2 subunit 1</td>
    <td>EIF2S1</td>
    <td>Down</td>
    <td>-0.44</td>
    <td>2.90</td>
  </tr>
  <tr>
    <td>Q3ZC09</td>
    <td>Beta-enolase</td>
    <td>ENO3</td>
    <td>Down</td>
    <td>-0.52</td>
    <td>1.77</td>
  </tr>
  <tr>
    <td>A5PK37</td>
    <td>EPM2A protein</td>
    <td>EPM2A</td>
    <td>Down</td>
    <td>-1.59</td>
    <td>1.89</td>
  </tr>
  <tr>
    <td>Q2KJJ9</td>
    <td>Fructose-1,6-bisphosphatase isozyme 2</td>
    <td>FBP2</td>
    <td>Down</td>
    <td>-0.40</td>
    <td>1.58</td>
  </tr>
  <tr>
    <td>Q3SWX4</td>
    <td>Glioblastoma amplified sequence</td>
    <td>GBAS</td>
    <td>Down</td>
    <td>-0.72</td>
    <td>2.35</td>
  </tr>
  <tr>
    <td>Q5EA88</td>
    <td>Glycerol-3-phosphate dehydrogenase [NAD(+)], cytoplasmic</td>
    <td>GPD1</td>
    <td>Down</td>
    <td>-0.53</td>
    <td>1.47</td>
  </tr>
  <tr>
    <td>P28801</td>
    <td>Glutathione S-transferase P</td>
    <td>GSTP1</td>
    <td>Down</td>
    <td>-0.62</td>
    <td>1.65</td>
  </tr>
  <tr>
    <td>Q3B7M2</td>
    <td>Hydroxyacylglutathione hydrolase, mitochondrial</td>
    <td>HAGH</td>
    <td>Down</td>
    <td>-1.26</td>
    <td>1.73</td>
  </tr>
  <tr>
    <td>P62958</td>
    <td>Histidine triad nucleotide-binding protein 1</td>
    <td>HINT1</td>
    <td>Down</td>
    <td>-1.34</td>
    <td>2.59</td>
  </tr>
  <tr>
    <td>A0A0A0MP90</td>
    <td>Histone H2A</td>
    <td>HIST1H2AC</td>
    <td>Down</td>
    <td>-1.18</td>
    <td>1.58</td>
  </tr>
  <tr>
    <td>E1BMK2</td>
    <td>High mobility group protein B1</td>
    <td>HMGB1</td>
    <td>Down</td>
    <td>-0.93</td>
    <td>1.75</td>
  </tr>
  <tr>
    <td>P19120</td>
    <td>Heat shock cognate 71 kDa protein</td>
    <td>HSPA8</td>
    <td>Down</td>
    <td>-0.53</td>
    <td>1.33</td>
  </tr>
  <tr>
    <td>Q5EAC9</td>
    <td>Heat shock protein beta-8</td>
    <td>HSPB8</td>
    <td>Down</td>
    <td>-2.15</td>
    <td>1.89</td>
  </tr>
  <tr>
    <td>O77784</td>
    <td>Isocitrate dehydrogenase [NAD] subunit beta, mitochondrial</td>
    <td>IDH3B</td>
    <td>Down</td>
    <td>-1.23</td>
    <td>2.72</td>
  </tr>
  <tr>
    <td>A6H7I0</td>
    <td>KBTBD5 protein</td>
    <td>KBTBD5</td>
    <td>Down</td>
    <td>-1.17</td>
    <td>2.09</td>
  </tr>
  <tr>
    <td>F1MD77</td>
    <td>Laminin subunit gamma-1</td>
    <td>LAMC1</td>
    <td>Down</td>
    <td>-0.98</td>
    <td>1.63</td>
  </tr>
  <tr>
    <td>G3N3C9</td>
    <td>LIM domain-binding protein 3</td>
    <td>LDB3</td>
    <td>Down</td>
    <td>-0.59</td>
    <td>1.64</td>
  </tr>
  <tr>
    <td>Q17QE2</td>
    <td>LIM and cysteine-rich domains protein 1</td>
    <td>LMCD1</td>
    <td>Down</td>
    <td>-0.53</td>
    <td>1.50</td>
  </tr>
  <tr>
    <td>F1MYG5</td>
    <td>Prelamin-A/C</td>
    <td>LMNA</td>
    <td>Down</td>
    <td>-0.36</td>
    <td>1.46</td>
  </tr>
  <tr>
    <td>F1N0E2</td>
    <td>UPI0000F341DF</td>
    <td>LOC520317</td>
    <td>Down</td>
    <td>-0.71</td>
    <td>1.98</td>
  </tr>
  <tr>
    <td>Q5E9X2</td>
    <td>Dual specificity mitogen-activated protein kinase kinase 6</td>
    <td>MAP2K6</td>
    <td>Down</td>
    <td>-0.88</td>
    <td>2.42</td>
  </tr>
  <tr>
    <td>P02192</td>
    <td>Myoglobin</td>
    <td>MB</td>
    <td>Down</td>
    <td>-0.99</td>
    <td>1.92</td>
  </tr>
  <tr>
    <td>A4FUD0</td>
    <td>MTHFD1 protein</td>
    <td>MTHFD1</td>
    <td>Down</td>
    <td>-1.37</td>
    <td>1.85</td>
  </tr>
  <tr>
    <td>A6QP89</td>
    <td>MYBPC1 protein</td>
    <td>MYBPC1</td>
    <td>Down</td>
    <td>-0.57</td>
    <td>1.69</td>
  </tr>
</table>
<table>
  <tr>
    <td>E1BF23</td>
    <td>Myomesin-2</td>
    <td>MYOM2</td>
    <td>Down</td>
    <td>-0.51</td>
    <td>1.67</td>
  </tr>
  <tr>
    <td>F1MPU<br/>4</td>
    <td>Myotilin</td>
    <td>MYOT</td>
    <td>Down</td>
    <td>-0.39</td>
    <td>1.75</td>
  </tr>
  <tr>
    <td>Q05752</td>
    <td>NADH dehydrogenase [ubiquinone] 1 alpha<br/>subcomplex subunit 7</td>
    <td>NDUFA<br/>7</td>
    <td>Down</td>
    <td>-1.79</td>
    <td>2.75</td>
  </tr>
  <tr>
    <td>P42029</td>
    <td>NADH dehydrogenase [ubiquinone] 1 alpha<br/>subcomplex subunit 8</td>
    <td>NDUFA<br/>8</td>
    <td>Down</td>
    <td>-0.85</td>
    <td>1.52</td>
  </tr>
  <tr>
    <td>Q02367</td>
    <td>NADH dehydrogenase [ubiquinone] 1 beta<br/>subcomplex subunit 6</td>
    <td>NDUFB<br/>6</td>
    <td>Down</td>
    <td>-1.22</td>
    <td>1.64</td>
  </tr>
  <tr>
    <td>F1MPL4</td>
    <td>Nucleoside diphosphate kinase B</td>
    <td>NME2</td>
    <td>Down</td>
    <td>-1.31</td>
    <td>2.18</td>
  </tr>
  <tr>
    <td>Q3SZC4</td>
    <td>NSFL1 cofactor p47</td>
    <td>NSFL1C</td>
    <td>Down</td>
    <td>-1.00</td>
    <td>1.95</td>
  </tr>
  <tr>
    <td>F1N184</td>
    <td>NXPE family member 4</td>
    <td>NXPE4</td>
    <td>Down</td>
    <td>-0.49</td>
    <td>1.57</td>
  </tr>
  <tr>
    <td>E1BK71</td>
    <td>Protein kinase C and casein kinase II<br/>substrate protein 3</td>
    <td>PACSIN<br/>3</td>
    <td>Down</td>
    <td>-0.59</td>
    <td>1.53</td>
  </tr>
  <tr>
    <td>F1MJ02</td>
    <td>Pre-B-cell leukemia transcription factor 1</td>
    <td>PBX1</td>
    <td>Down</td>
    <td>-0.84</td>
    <td>2.87</td>
  </tr>
  <tr>
    <td>F1N2K1</td>
    <td>Prenylcysteine oxidase 1</td>
    <td>PCYOX<br/>1</td>
    <td>Down</td>
    <td>-0.78</td>
    <td>1.55</td>
  </tr>
  <tr>
    <td>P22439</td>
    <td>Pyruvate dehydrogenase protein X<br/>component</td>
    <td>PDHX</td>
    <td>Down</td>
    <td>-1.23</td>
    <td>1.46</td>
  </tr>
  <tr>
    <td>Q3ZBU<br/>0</td>
    <td>PDZ and LIM domain 5</td>
    <td>PDLIM5</td>
    <td>Down</td>
    <td>-0.54</td>
    <td>1.91</td>
  </tr>
  <tr>
    <td>Q3SX40</td>
    <td>PDZ and LIM domain protein 7</td>
    <td>PDLIM7</td>
    <td>Down</td>
    <td>-0.57</td>
    <td>1.47</td>
  </tr>
  <tr>
    <td>Q09430</td>
    <td>Profilin-2</td>
    <td>PFN2</td>
    <td>Down</td>
    <td>-1.69</td>
    <td>1.78</td>
  </tr>
  <tr>
    <td>A5PJQ6</td>
    <td>PGRMC2 protein</td>
    <td>PGRMC<br/>2</td>
    <td>Down</td>
    <td>-1.63</td>
    <td>2.97</td>
  </tr>
  <tr>
    <td>Q32PA4</td>
    <td>14 kDa phosphohistidine phosphatase</td>
    <td>PHPT1</td>
    <td>Down</td>
    <td>-1.88</td>
    <td>2.65</td>
  </tr>
  <tr>
    <td>A5D984</td>
    <td>Pyruvate kinase</td>
    <td>PKM2</td>
    <td>Down</td>
    <td>-0.61</td>
    <td>1.60</td>
  </tr>
  <tr>
    <td>E1BLN7</td>
    <td>Protein phosphatase 1 regulatory subunit<br/>3A</td>
    <td>PPP1R3<br/>A</td>
    <td>Down</td>
    <td>-0.68</td>
    <td>1.35</td>
  </tr>
  <tr>
    <td>P35705</td>
    <td>Thioredoxin-dependent peroxide reductase,<br/>mitochondrial</td>
    <td>PRDX3</td>
    <td>Down</td>
    <td>-0.62</td>
    <td>1.74</td>
  </tr>
  <tr>
    <td>F1MQV<br/>7</td>
    <td>Non-specific serine/threonine protein kinase</td>
    <td>PRKAA<br/>2</td>
    <td>Down</td>
    <td>-0.76</td>
    <td>1.34</td>
  </tr>
  <tr>
    <td>Q1LZE9</td>
    <td>Serine protease 23</td>
    <td>PRSS23</td>
    <td>Down</td>
    <td>-0.80</td>
    <td>1.57</td>
  </tr>
  <tr>
    <td>Q3T0X5</td>
    <td>Proteasome subunit alpha type-1</td>
    <td>PSMA1</td>
    <td>Down</td>
    <td>-1.16</td>
    <td>1.75</td>
  </tr>
  <tr>
    <td>Q32KL2</td>
    <td>Proteasome subunit beta type-5</td>
    <td>PSMB5</td>
    <td>Down</td>
    <td>-0.67</td>
    <td>1.40</td>
  </tr>
  <tr>
    <td>Q2KJ25</td>
    <td>26S proteasome non-ATPase regulatory<br/>subunit 12</td>
    <td>PSMD12</td>
    <td>Down</td>
    <td>-1.48</td>
    <td>2.55</td>
  </tr>
  <tr>
    <td>Q3ZBD<br/>0</td>
    <td>26S proteasome non-ATPase regulatory<br/>subunit 7</td>
    <td>PSMD7</td>
    <td>Down</td>
    <td>-0.85</td>
    <td>1.78</td>
  </tr>
  <tr>
    <td>P61585</td>
    <td>Transforming protein RhoA</td>
    <td>RHOA</td>
    <td>Down</td>
    <td>-1.72</td>
    <td>1.93</td>
  </tr>
  <tr>
    <td>G3MYE<br/>2</td>
    <td>60S ribosomal protein L5</td>
    <td>RPL5</td>
    <td>Down</td>
    <td>-1.51</td>
    <td>2.84</td>
  </tr>
  <tr>
    <td>Q3ZBH<br/>8</td>
    <td>40S ribosomal protein S20</td>
    <td>RPS20</td>
    <td>Down</td>
    <td>-0.89</td>
    <td>1.50</td>
  </tr>
  <tr>
    <td>F1MZW<br/>8</td>
    <td>Ribosomal protein S6 kinase</td>
    <td>RPS6KA<br/>3</td>
    <td>Down</td>
    <td>-1.23</td>
    <td>2.01</td>
  </tr>
  <tr>
    <td>Q5E9T9</td>
    <td>tRNA-splicing ligase RtcB homolog</td>
    <td>RTCB</td>
    <td>Down</td>
    <td>-1.08</td>
    <td>1.93</td>
  </tr>
  <tr>
    <td>F6QK60</td>
    <td>NAD-dependent protein deacetylase Sirt2</td>
    <td>SIRT2</td>
    <td>Down</td>
    <td>-1.77</td>
    <td>1.66</td>
  </tr>
  <tr>
    <td>P12234</td>
    <td>Phosphate carrier protein, mitochondrial</td>
    <td>SLC25A<br/>3</td>
    <td>Down</td>
    <td>-0.83</td>
    <td>2.24</td>
  </tr>
  <tr>
    <td>E1BPV6</td>
    <td>Smoothelin-like protein 1</td>
    <td>SMTNL<br/>1</td>
    <td>Down</td>
    <td>-3.35</td>
    <td>3.14</td>
  </tr>
</table>
<table>
  <tr>
    <td>F1MNQ<br>4</td>
    <td>Superoxide dismutase [Cu-Zn]</td>
    <td>SOD1</td>
    <td>Down</td>
    <td>-0.84</td>
    <td>1.88</td>
  </tr>
  <tr>
    <td>F1MZ38</td>
    <td>Succinate--CoA ligase [ADP/GDP-forming] subunit alpha, mitochondrial</td>
    <td>SUCLG1</td>
    <td>Down</td>
    <td>-0.56</td>
    <td>2.42</td>
  </tr>
  <tr>
    <td>E1BIS6</td>
    <td>Synemin</td>
    <td>SYNM</td>
    <td>Down</td>
    <td>-0.77</td>
    <td>1.39</td>
  </tr>
  <tr>
    <td>A4IFK4</td>
    <td>SYNPO2 protein</td>
    <td>SYNPO2</td>
    <td>Down</td>
    <td>-0.55</td>
    <td>1.65</td>
  </tr>
  <tr>
    <td>E1B7Z0</td>
    <td>TGF-beta-activated kinase 1 and MAP3K7-binding protein 2</td>
    <td>TAB2</td>
    <td>Down</td>
    <td>-1.91</td>
    <td>1.41</td>
  </tr>
  <tr>
    <td>A0JNC0</td>
    <td>Tropomodulin-1</td>
    <td>TMOD1</td>
    <td>Down</td>
    <td>-0.59</td>
    <td>1.45</td>
  </tr>
  <tr>
    <td>E1BE77</td>
    <td>Tripartite motif-containing protein 72</td>
    <td>TRIM72</td>
    <td>Down</td>
    <td>-0.57</td>
    <td>1.35</td>
  </tr>
  <tr>
    <td>P81947</td>
    <td>Tubulin alpha-1B chain</td>
    <td>TUBA1<br>B</td>
    <td>Down</td>
    <td>-0.69</td>
    <td>1.37</td>
  </tr>
  <tr>
    <td>F1MFL6</td>
    <td>Ubiquitin-conjugating enzyme E2 D4</td>
    <td>UBE2D4</td>
    <td>Down</td>
    <td>-0.81</td>
    <td>1.60</td>
  </tr>
  <tr>
    <td>Q0P5K3</td>
    <td>Ubiquitin-conjugating enzyme E2 N</td>
    <td>UBE2N</td>
    <td>Down</td>
    <td>-0.93</td>
    <td>2.45</td>
  </tr>
  <tr>
    <td>Q3SZ43</td>
    <td>Ubiquitin-conjugating enzyme E2 variant 2</td>
    <td>UBE2V2</td>
    <td>Down</td>
    <td>-1.11</td>
    <td>1.47</td>
  </tr>
  <tr>
    <td>F1MFZ5</td>
    <td>Protein unc-45 homolog B</td>
    <td>UNC45B</td>
    <td>Down</td>
    <td>-0.38</td>
    <td>1.48</td>
  </tr>
  <tr>
    <td>G1K1X<br>0</td>
    <td>Cytochrome b-c1 complex subunit 1, mitochondrial</td>
    <td>UQCRC<br>1</td>
    <td>Down</td>
    <td>-0.59</td>
    <td>2.22</td>
  </tr>
  <tr>
    <td>P23004</td>
    <td>Cytochrome b-c1 complex subunit 2, mitochondrial</td>
    <td>UQCRC<br>2</td>
    <td>Down</td>
    <td>-0.36</td>
    <td>1.33</td>
  </tr>
  <tr>
    <td>F1N789</td>
    <td>Vinculin</td>
    <td>VCL<br>VDAC5<br>P</td>
    <td>Down</td>
    <td>-0.52</td>
    <td>2.18</td>
  </tr>
  <tr>
    <td>A6H783</td>
    <td>VDAC5P protein</td>
    <td>WDR1</td>
    <td>Down</td>
    <td>-0.55</td>
    <td>2.82</td>
  </tr>
  <tr>
    <td>F1MTP5<br>G3MZY<br>7</td>
    <td>WD repeat-containing protein 1</td>
    <td>WDR1</td>
    <td>Down</td>
    <td>-0.44</td>
    <td>1.36</td>
  </tr>
  <tr>
    <td></td>
    <td>UPI0002263C8B</td>
    <td></td>
    <td>Down</td>
    <td>0.70</td>
    <td>1.46</td>
  </tr>
  <tr>
    <td>P12763</td>
    <td>Alpha-2-HS-glycoprotein</td>
    <td>AHSG</td>
    <td>Up</td>
    <td>1.89</td>
    <td>1.89</td>
  </tr>
  <tr>
    <td>P20000</td>
    <td>Aldehyde dehydrogenase, mitochondrial</td>
    <td>ALDH2</td>
    <td>Up</td>
    <td>0.89</td>
    <td>1.43</td>
  </tr>
  <tr>
    <td>F6QVC<br>9</td>
    <td>Annexin</td>
    <td>ANXA5</td>
    <td>Up</td>
    <td>1.81</td>
    <td>1.41</td>
  </tr>
  <tr>
    <td>E1BC10</td>
    <td>Amine oxidase</td>
    <td>AOC3</td>
    <td>Up</td>
    <td>2.92</td>
    <td>1.32</td>
  </tr>
  <tr>
    <td>A5PKI0</td>
    <td>APPL1 protein</td>
    <td>APPL1</td>
    <td>Up</td>
    <td>2.33</td>
    <td>2.26</td>
  </tr>
  <tr>
    <td>F1MY2<br>7</td>
    <td>BCL2/adenovirus E1B 19 kDa protein-interacting protein 3</td>
    <td>BNIP3</td>
    <td>Up</td>
    <td>0.38</td>
    <td>1.67</td>
  </tr>
  <tr>
    <td>G3MZ0<br>7</td>
    <td>Voltage-dependent L-type calcium channel subunit alpha</td>
    <td>CACNA<br>1S</td>
    <td>Up</td>
    <td>3.10</td>
    <td>2.11</td>
  </tr>
  <tr>
    <td>A5D9F0</td>
    <td>Calcium/calmodulin-dependent protein kinase II delta</td>
    <td>CAMK2<br>D</td>
    <td>Up</td>
    <td>1.64</td>
    <td>1.45</td>
  </tr>
  <tr>
    <td>P13135</td>
    <td>Calpain small subunit 1</td>
    <td>CAPNS1</td>
    <td>Up</td>
    <td>1.31</td>
    <td>1.38</td>
  </tr>
  <tr>
    <td>Q5E997</td>
    <td>F-actin-capping protein subunit alpha-2</td>
    <td>CAPZA2</td>
    <td>Up</td>
    <td>0.58</td>
    <td>1.39</td>
  </tr>
  <tr>
    <td>P79136</td>
    <td>F-actin-capping protein subunit beta</td>
    <td>CAPZB</td>
    <td>Up</td>
    <td>1.31</td>
    <td>1.50</td>
  </tr>
  <tr>
    <td>A4FV37</td>
    <td>Caveolae-associated protein 3</td>
    <td>CAVIN3</td>
    <td>Up</td>
    <td>1.63</td>
    <td>1.35</td>
  </tr>
  <tr>
    <td>F1MAX<br>3</td>
    <td>Centrosomal protein of 192 kDa</td>
    <td>CEP192</td>
    <td>Up</td>
    <td>1.40</td>
    <td>2.88</td>
  </tr>
  <tr>
    <td>E3W9A<br>2</td>
    <td>CAP-Gly domain-containing linker protein 1</td>
    <td>CLIP1</td>
    <td>Up</td>
    <td>2.43</td>
    <td>1.65</td>
  </tr>
  <tr>
    <td>F1ME62</td>
    <td>Cardiomyopathy-associated protein 5</td>
    <td>CMYA5</td>
    <td>Up</td>
    <td>3.06</td>
    <td>1.80</td>
  </tr>
  <tr>
    <td>Q6QTG<br>5</td>
    <td>Cytochrome c oxidase subunit 3</td>
    <td>COX3</td>
    <td>Up</td>
    <td>2.51</td>
    <td>1.52</td>
  </tr>
  <tr>
    <td>O97764</td>
    <td>Zeta-crystallin</td>
    <td>CRYZ</td>
    <td>Up</td>
    <td>3.07</td>
    <td>1.43</td>
  </tr>
  <tr>
    <td>E1BH82</td>
    <td>Casein kinase II subunit alpha</td>
    <td>CSNK2</td>
    <td>Up</td>
    <td>3.17</td>
    <td>1.97</td>
  </tr>
</table>
<table><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>F1N7D7</td><td>Dystroglycan</td><td>A1</td><td>DAG1</td><td>Up</td><td>4.61</td><td>2.08</td></tr><tr><td>E1BIK7</td><td>Density-regulated protein</td><td></td><td>DENR</td><td>Up</td><td>3.03</td><td>1.69</td></tr><tr><td>Q2HJ94</td><td>DnaJ homolog subfamily A member 2</td><td></td><td>DNAJA2</td><td>Up</td><td>3.65</td><td>1.88</td></tr><tr><td>F1N062</td><td>Dual specificity phosphatase 27</td><td></td><td>DUSP27</td><td>Up</td><td>3.19</td><td>1.48</td></tr><tr><td>Q56JZ5</td><td>Eukaryotic translation initiation factor 3 subunit H</td><td></td><td>EIF3H</td><td>Up</td><td>2.64</td><td>1.82</td></tr><tr><td>Q2KIG0</td><td>Electron transfer flavoprotein-ubiquinone oxidoreductase, mitochondrial</td><td></td><td>ETFDH</td><td>Up</td><td>1.88</td><td>2.03</td></tr><tr><td>Q2KJI3</td><td>Protein FAM49B</td><td></td><td>FAM49B</td><td>Up</td><td>2.66</td><td>1.87</td></tr><tr><td>F1MR86</td><td>Four and a half LIM domains protein 1</td><td></td><td>FHL1</td><td>Up</td><td>0.81</td><td>1.39</td></tr><tr><td>Q3ZBI6</td><td>Four and a half LIM domains protein 3</td><td></td><td>FHL3</td><td>Up</td><td>2.79</td><td>2.26</td></tr><tr><td>Q2KJ17</td><td>GTPase activating protein (SH3 domain) binding protein 2</td><td></td><td>G3BP2</td><td>Up</td><td>4.72</td><td>2.19</td></tr><tr><td>Q2KJE5</td><td>Glyceraldehyde-3-phosphate dehydrogenase, testis-specific</td><td></td><td>GAPDH S</td><td>Up</td><td>1.80</td><td>1.80</td></tr><tr><td>A6QLU 1</td><td>Glycerol-3-phosphate dehydrogenase, mitochondrial</td><td></td><td>GPD2</td><td>Up</td><td>3.54</td><td>1.38</td></tr><tr><td>F6Q751</td><td>Glutathione S-transferase Mu 2</td><td></td><td>GSTM2</td><td>Up</td><td>0.53</td><td>2.04</td></tr><tr><td>F1MMU 4</td><td>Histone H1x</td><td></td><td>H1FX</td><td>Up</td><td>8.41</td><td>1.34</td></tr><tr><td>F1MK9 1</td><td>Protein-cysteine N-palmitoyltransferase HHAT-like protein</td><td></td><td>HHATL</td><td>Up</td><td>1.54</td><td>1.46</td></tr><tr><td>E1BGN 3</td><td>Histone H3</td><td></td><td>HIST2H 3D</td><td>Up</td><td>2.91</td><td>1.47</td></tr><tr><td>Q3SZV7</td><td>Hemopexin</td><td></td><td>HPX</td><td>Up</td><td>1.18</td><td>1.74</td></tr><tr><td>Q0VCX 2</td><td>78 kDa glucose-regulated protein</td><td></td><td>HSPA5</td><td>Up</td><td>0.41</td><td>1.61</td></tr><tr><td>F1MBR 1</td><td>Heat shock 27kDa protein 2</td><td></td><td>HSPB2</td><td>Up</td><td>0.78</td><td>1.91</td></tr><tr><td>F1MUZ 9</td><td>60 kDa heat shock protein, mitochondrial</td><td></td><td>HSPD1</td><td>Up</td><td>1.01</td><td>1.33</td></tr><tr><td>Q58CP0</td><td>Isocitrate dehydrogenase [NAD] subunit gamma, mitochondrial</td><td></td><td>IDH3G</td><td>Up</td><td>3.42</td><td>1.40</td></tr><tr><td>G5E5B8</td><td>Junctophilin-1</td><td></td><td>JPH1</td><td>Up</td><td>2.20</td><td>1.81</td></tr><tr><td>F1MZL 0</td><td>Junctional sarcoplasmic reticulum protein 1</td><td></td><td>JSRP1</td><td>Up</td><td>3.97</td><td>1.49</td></tr><tr><td>F1MCF 8</td><td>Ig-like domain-containing protein</td><td></td><td>LOC100 297192</td><td>Up</td><td>1.25</td><td>1.39</td></tr><tr><td>Q2KHU 5</td><td>O-acetyl-ADP-ribose deacetylase MACROD1</td><td></td><td>MACRO D1</td><td>Up</td><td>0.69</td><td>1.70</td></tr><tr><td>F1MAZ 1</td><td>Microtubule-associated protein</td><td></td><td>MAP4</td><td>Up</td><td>1.50</td><td>2.12</td></tr><tr><td>F6RF62</td><td>Myosin light chain 4</td><td></td><td>MYL4</td><td>Up</td><td>3.03</td><td>2.16</td></tr><tr><td>A4IFM7</td><td>Myosin light chain kinase 2, skeletal/cardiac muscle</td><td></td><td>MYLK2</td><td>Up</td><td>2.43</td><td>1.63</td></tr><tr><td>Q95KV 7</td><td>NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 13</td><td></td><td>NDUFA 13</td><td>Up</td><td>4.31</td><td>1.73</td></tr><tr><td>Q02369</td><td>NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 9</td><td></td><td>NDUFB 9</td><td>Up</td><td>0.45</td><td>2.36</td></tr><tr><td>Q3ZBH 2</td><td>NAD(P)H dehydrogenase, quinone 1</td><td></td><td>NQO1</td><td>Up</td><td>2.03</td><td>1.46</td></tr><tr><td>F1MMX</td><td>Nebulin-related-anchoring protein</td><td></td><td>NRAP</td><td>Up</td><td>5.36</td><td>1.75</td></tr></tbody></table>
2

<table><tr><td>F1MYV<br>1</td><td>NUT family member 1</td><td>NUTM1</td><td>Up</td><td>3.74</td><td>1.80</td></tr><tr><td>E1B959</td><td>Phosphoglycerate mutase</td><td>PGAM2</td><td>Up</td><td>1.70</td><td>1.89</td></tr><tr><td>F1MJ90</td><td>Phosphorylase b kinase regulatory subunit<br>beta</td><td>PHKB</td><td>Up</td><td>1.19</td><td>1.86</td></tr><tr><td>Q3T0U2</td><td>60S ribosomal protein L14</td><td>RPL14</td><td>Up</td><td>2.77</td><td>1.35</td></tr><tr><td>E1BK63</td><td>Ribosomal protein L15</td><td>RPL15</td><td>Up</td><td>1.21</td><td>1.77</td></tr><tr><td>Q5E973</td><td>60S ribosomal protein L18</td><td>RPL18</td><td>Up</td><td>4.30</td><td>1.36</td></tr><tr><td>G3MYC<br>1</td><td>60S ribosomal protein L26</td><td>RPL26</td><td>Up</td><td>4.37</td><td>1.38</td></tr><tr><td>F1MDN<br>4</td><td>60S acidic ribosomal protein P0</td><td>RPLP0</td><td>Up</td><td>1.51</td><td>1.33</td></tr><tr><td>F1N405</td><td>Reticulon</td><td>RTN4</td><td>Up</td><td>3.60</td><td>1.46</td></tr><tr><td>Q3SWZ<br>6</td><td>Ribosome maturation protein SBDS</td><td>SBDS</td><td>Up</td><td>5.61</td><td>1.65</td></tr><tr><td>G3MY6<br>7</td><td>Succinate dehydrogenase [ubiquinone]<br>flavoprotein subunit, mitochondrial</td><td>SDHA</td><td>Up</td><td>1.20</td><td>1.37</td></tr><tr><td>A2I7M9</td><td>Serpin A3-2</td><td>SERPIN<br>A3</td><td>Up</td><td>3.95</td><td>1.83</td></tr><tr><td>F1MSZ6</td><td>Antithrombin-III</td><td>SERPIN<br>C1</td><td>Up</td><td>5.57</td><td>1.53</td></tr><tr><td>F1MBL<br>6</td><td>Serine/threonine-protein kinase SMG1</td><td>SMG1</td><td>Up</td><td>1.08</td><td>1.64</td></tr><tr><td>F1MZS3</td><td>Histone-lysine N-methyltransferase<br>SMYD1</td><td>SMYD1</td><td>Up</td><td>1.35</td><td>1.66</td></tr><tr><td>F1MDV<br>3</td><td>Beta-1-syntrophin</td><td>SNTB1</td><td>Up</td><td>2.48</td><td>1.65</td></tr><tr><td>F1MQC<br>4</td><td>Spermatogenesis-associated protein 22</td><td>SPATA2<br>2</td><td>Up</td><td>0.98</td><td>1.54</td></tr><tr><td>E1BKF8</td><td>Signal peptide peptidase-like 2B</td><td>SPPL2B</td><td>Up</td><td>3.52</td><td>1.35</td></tr><tr><td>E1BA93</td><td>Synaptopodin</td><td>SYNPO</td><td>Up</td><td>0.84</td><td>1.39</td></tr><tr><td>E1BHE1<br>Q3ZCD<br>7</td><td>Serine/threonine-protein kinase TAO3<br>Very-long-chain enoyl-CoA reductase</td><td>TAOK3</td><td>Up</td><td>0.68</td><td>1.34</td></tr><tr><td>G3X6N<br>3</td><td>Serotransferrin</td><td>TECR</td><td>Up</td><td>0.92</td><td>1.85</td></tr><tr><td>F6Q3H3</td><td>TELO2-interacting protein 1 homolog</td><td>TTI1</td><td>Up</td><td>1.90</td><td>1.85</td></tr><tr><td>Q3MHP<br>1</td><td>Ubiquitin-conjugating enzyme E2 L3</td><td>UBE2L3</td><td>Up</td><td>1.37</td><td>1.87</td></tr><tr><td>E1BEW<br>9</td><td>WD repeat-containing protein 87</td><td>WDR87</td><td>Up</td><td>4.89</td><td>2.16</td></tr><tr><td>A0A140<br>T894</td><td>14-3-3 protein beta/alpha</td><td>YWHAB</td><td>Up</td><td>4.25</td><td>1.40</td></tr><tr><td>G3MX<br>M1</td><td>UPI00006157C1</td><td></td><td>Up</td><td>-0.86</td><td>1.60</td></tr></table>
<table><caption>**Table 4.** List of the differentially abundant proteins (DAPs) between high and normal ultimate pH (pHu) beef at 44 h postmortem (n = 37).</caption><thead><tr><th>Uniprot ID</th><th>Full protein name</th><th>Gene name</th><th>Regulation in high pH group</th><th>Log2(FC) (high/normal)</th><th>Log10(P-value)</th></tr></thead><tbody><tr><td>A2VE25</td><td>ANK1 protein</td><td>ANK1</td><td>Down</td><td>-1.73</td><td>1.35</td></tr><tr><td>F1MIU2</td><td>BAG family molecular chaperone regulator 3</td><td>BAG3</td><td>Down</td><td>-1.69</td><td>1.73</td></tr><tr><td>F1N7T1</td><td>NADH-cytochrome b5 reductase</td><td>CYB5R3</td><td>Down</td><td>-0.70</td><td>1.37</td></tr><tr><td>F1MN23</td><td>Dehydrogenase/reductase SDR family member 7B</td><td>DHRS7B</td><td>Down</td><td>-0.41</td><td>1.33</td></tr><tr><td>P00125</td><td>Cytochrome c1, heme protein, mitochondrial</td><td>CYC1</td><td>Down</td><td>-0.31</td><td>1.36</td></tr><tr><td>Q1RMM6</td><td>Coenzyme Q10 homolog A</td><td>COQ10A</td><td>Down</td><td>-0.60</td><td>1.67</td></tr><tr><td>P01966</td><td>Hemoglobin subunit alpha</td><td>HBA</td><td>Down</td><td>-0.70</td><td>1.59</td></tr><tr><td>A6QLD3</td><td>LRRC20 protein</td><td>LRRC20</td><td>Down</td><td>-0.78</td><td>1.82</td></tr><tr><td>Q3SZH7</td><td>Leukotriene A-4 hydrolase</td><td>LTA4H</td><td>Down</td><td>-0.98</td><td>1.37</td></tr><tr><td>P02192</td><td>Myoglobin</td><td>MB</td><td>Down</td><td>-0.44</td><td>1.74</td></tr><tr><td>Q05752</td><td>NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 7</td><td>NDUF A7</td><td>Down</td><td>-0.69</td><td>1.41</td></tr><tr><td>P34943</td><td>NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 9, mitochondrial</td><td>NDUF A9</td><td>Down</td><td>-0.29</td><td>1.37</td></tr><tr><td>Q5E946</td><td>Protein DJ-1</td><td>PARK7</td><td>Down</td><td>-0.88</td><td>1.38</td></tr><tr><td>F1MNQ4</td><td>Superoxide dismutase [Cu-Zn]</td><td>SOD1</td><td>Down</td><td>-0.87</td><td>1.36</td></tr><tr><td>P41976</td><td>Superoxide dismutase [Mn], mitochondrial</td><td>SOD2</td><td>Down</td><td>-0.35</td><td>1.35</td></tr><tr><td>A0JNC0</td><td>Tropomodulin-1</td><td>TMO D1</td><td>Down</td><td>-0.31</td><td>1.32</td></tr><tr><td>P81947</td><td>Tubulin alpha-1B chain</td><td>TUBA1B</td><td>Down</td><td>-0.87</td><td>1.40</td></tr><tr><td>F1MFZ5</td><td>Protein unc-45 homolog B</td><td>UNC45B</td><td>Down</td><td>-0.77</td><td>1.58</td></tr><tr><td>Q0II26</td><td>Aminoacyl tRNA synthase complex-interacting multifunctional protein 2</td><td>AIMP2</td><td>Up</td><td>2.01</td><td>1.45</td></tr><tr><td>P20000</td><td>Aldehyde dehydrogenase, mitochondrial</td><td>ALDH2</td><td>Up</td><td>0.76</td><td>1.45</td></tr><tr><td>A2VDL6</td><td>Sodium/potassium-transporting ATPase subunit alpha-2</td><td>ATP1A2</td><td>Up</td><td>0.63</td><td>1.41</td></tr><tr><td>A5PJI6</td><td>Caveolae-associated protein 4</td><td>CAVI N4</td><td>Up</td><td>1.10</td><td>1.32</td></tr><tr><td>Q2HJ94</td><td>DnaJ homolog subfamily A member 2</td><td>DNAJ A2</td><td>Up</td><td>2.23</td><td>1.36</td></tr><tr><td>Q3T0U3</td><td>Es1 protein</td><td>ES1</td><td>Up</td><td>1.20</td><td>1.63</td></tr><tr><td>F1MR86</td><td>Four and a half LIM domains protein 1</td><td>FHL1</td><td>Up</td><td>0.55</td><td>1.35</td></tr><tr><td>Q2KJ</td><td>Glyceraldehyde-3-phosphate dehydrogenase,</td><td>GAPD</td><td>Up</td><td>0.91</td><td>1.35</td></tr></tbody></table>
<table><tr><td>E5</td><td>testis-specific</td><td>HS</td><td></td><td></td></tr><tr><td>F1MP10</td><td>COP9 signalosome complex subunit 1</td><td>GPS1</td><td>Up</td><td>1.31</td><td>2.24</td></tr><tr><td>F6Q751</td><td>Glutathione S-transferase Mu 2</td><td>GSTM2</td><td>Up</td><td>0.60</td><td>1.77</td></tr><tr><td>Q0IJJ2</td><td>Histone H1.0</td><td>H1F0</td><td>Up</td><td>2.14</td><td>1.31</td></tr><tr><td>Q2HJ</td><td>3-hydroxyisobutyrate dehydrogenase, mitochondrial</td><td>HIBA DH</td><td>Up</td><td>2.19</td><td>1.51</td></tr><tr><td>Q2K</td><td>Heat shock protein beta-3</td><td>HSPB3</td><td>Up</td><td>0.84</td><td>1.42</td></tr><tr><td>Q58C</td><td>Isocitrate dehydrogenase [NAD] subunit gamma, mitochondrial</td><td>IDH3 G</td><td>Up</td><td>1.69</td><td>1.37</td></tr><tr><td>F1MVX2</td><td>Glutathione S-transferase LANCL1</td><td>LANC1</td><td>Up</td><td>0.40</td><td>2.10</td></tr><tr><td>A2I7</td><td>Serpin A3-2</td><td>SERPI NA3</td><td>Up</td><td>2.01</td><td>1.58</td></tr><tr><td>F1MSZ6</td><td>Antithrombin-III</td><td>SERPI NC1</td><td>Up</td><td>2.63</td><td>1.33</td></tr><tr><td>Q0P5E6</td><td>Alpha-1-syntrophin</td><td>SNTA1</td><td>Up</td><td>0.56</td><td>1.49</td></tr><tr><td>Q3M</td><td>Ubiquitin-conjugating enzyme E2 L3</td><td>UBE2L3</td><td>Up</td><td>1.06</td><td>1.41</td></tr></table>
**Declaration of Competing Interest**

No potential conflict of interest was reported by the authors.
CRediT authorship contribution statement

**Iliani Patinho:** Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – original draft. **Daniel S. Antonelo:** Formal Analysis, Software, Visualization, Writing – review & editing. **Eduardo F. Delgado:** Writing – review & editing. **Laura Alessandroni:** Data curation, Formal Analysis, Visualization, Writing – review & editing. **Julio C.C. Balieiro:** Supervision, Writing – review & editing. **Carmen J. Contreras-Castillo:** Conceptualization, Funding acquisition, Supervision, Writing – review & editing. **Mohammed Gagaoua:** Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing
## Highlights

* In-depth proteomics exploration of dark-cutting beef condition using three muscle sampling times taken early *postmortem*

* 251 proteins were differentially expressed creating the most comprehensive interconnected pathways of high-pHu beef

* Energy metabolism, cellular stress response and oxidoreductase activity play pivotal roles

* Ribosomal proteins and histones play pivotal role in dark-cutting beef development

* Pathways are proposed to determine the biological mechanisms behind dark-cutting beef