# Sequential window acquisition of all theoretical mass spectra (SWATH-MS) as an emerging proteomics approach for the discovery of dark-cutting beef biomarkers

Laura González-Blanco, Mamen Oliván, Yolanda Diñeiro, Susana Bravo, Verónica Sierra, Mohammed Gagaoua

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## To cite this version:

Laura González-Blanco, Mamen Oliván, Yolanda Diñeiro, Susana Bravo, Verónica Sierra, et al. Sequential window acquisition of all theoretical mass spectra (SWATH-MS) as an emerging proteomics approach for the discovery of dark-cutting beef biomarkers. Meat Science, 2024, 217, pp.109618. 10.1016/j.meatsci.2024.109618. hal-04666195

HAL Id: hal-04666195

https://hal.inrae.fr/hal-04666195v1

Submitted on 1 Aug 2024

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# Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) as an emerging proteomics approach for the discovery of dark-cutting beef biomarkers

Laura González-Blanco<fnref n="1" />, Mamen Oliván<fnref n="1" />, Yolanda Diñeiro<fnref n="1" />, Susana B. Bravo<fnref n="2" />, Verónica Sierra<fnref n="1" />* and Mohammed Gagaoua<fnref n="3" />*

**\* Corresponding authors:**

Verónica Sierra: veroniss@serida.org

Mohammed Gagaoua: mohammed.gagaoua@inrae.fr

**Abstract**

Recent advances in “omics” technologies have enabled the identification of new beef quality biomarkers and have also allowed for the early detection of quality defects such as dark-cutting beef, also known as DFD (dark, firm, and dry) beef. However, most of the studies conducted were carried out on a small number of animals and mostly applied gel-based proteomics. The present study proposes for the first time a Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) proteomics approach to characterize and comprehensively quantify the *post-mortem* muscle proteomes of DFD ($pH_{24} \ge 6.2$) and CONTROL beef samples ($5.4 \le pH_{24} \le 5.6$) within the largest database of DFD/CONTROL beef samples to date (26 pairs of samples of the young Asturiana de los Valles bulls, n=52). The pairwise comparison yielded 35 proteins that significantly differed in their abundances between the DFD and CONTROL samples. Chemometrics methods using PLS-DA and OPLS-DA revealed 31 and 36 proteins with VIP > 2, respectively. The combination of different statistical methods identified 16 proteins as good candidate biomarkers of DFD beef. These proteins are associated with interconnected biochemical pathways related to energy metabolism (DHRS7B and CYB5R3), binding and signaling (RABGGTA, MIA3, BPIFA2B, CAP2, APOBEC2, UBE2V1, KIR2DL1), muscle contraction, structure and associated proteins (DMD, PFN2), proteolysis, hydrolases, and activity regulation (AGT, C4A, GLB1, CAND2), and calcium homeostasis (ANXA6). These results evidenced the potential of SWATH-MS and chemometrics to accurately identify novel biomarkers for meat quality defects, providing a deeper understanding of the molecular mechanisms underlying dark-cutting beef condition.

**Keywords:** Beef quality defects; Biological mechanisms; Dark-cutting; Protein biomarkers; Proteome; Shotgun proteomics.

<footnote n="1">¹ Área de Sistemas de Producción Animal, Servicio Regional de Investigación y Desarrollo Agroalimentario (SERIDA), Ctra. AS-267, PK 19, 33300 Villaviciosa, Spain</footnote>

<footnote n="2">² Proteomic Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain</footnote>

<footnote n="3">³ PEGASE, INRAE, Institut Agro, 35590 Saint-Gilles, France</footnote>
# 1. Introduction

Quality defects such as dark-cutting beef condition, also known as dark, firm, and dry (DFD) beef, cause significant economic losses to the meat industry due to the poor quality of affected products, characterized by high ultimate pH, unappealing dark color, undesirable alterations in tenderness during aging, and higher spoilage (Ponnampalam et al., 2017; Loudon et al., 2018; Mahmood et al., 2018; González-Blanco et al., 2023). The occurrence of DFD meat has been related to various pre-slaughter factors including adverse weather events, stress during handling, transport and lairage, thirst, and fatigue (Ferguson & Warner, 2008; Muchenje et al., 2009). In addition, muscle metabolism and final meat quality are also determined by many physiological factors, including how each animal responds to stress during slaughter (Terlouw et al., 2021). DFD beef is a common problem in many countries, with high incidence rates reported in China (10.1%), Germany (6.2%), Brazil (4.5%), Belgium (3.6%), USA (3.2%), France (3.4-1.1%) and Canada (1.3%) (Mounier et al., 2006; Moore et al., 2012; Holdstock et al., 2014; Rosa et al., 2016; Mahmood et al., 2017; Gagaoua, Picard, et al., 2018). Despite the lack of information on this problem in Spain, the average incidences of meat pH<sub>24</sub> greater than 5.8 and 6.0 were 13.89% and 4.02%, respectively (Mach et al., 2008), resulting in a 30-60% reduction in the commercial price of the carcass.

The underlying mechanisms explaining this quality defect are complex, multifactorial, and not fully understood. Gagaoua et al. (2021) extensively discussed the main biological pathways underlying this quality defect thanks to a recent integromics meta-analysis from published dark-cutting beef proteomics studies. Despite the complexity of this process and significant variability among the proteomics studies, the research identified certain trends. These included a likely increase in mitochondrial respiration, a decrease in glycolysis, and an elevated use of alternative energy metabolic pathways as key factors contributing to the occurrence of DFD defects. However, in this meta-analysis, a significant disparity between proteins identified in different studies was found, with only ten proteins identified as potential biomarkers in more than one study. These proteins were associated with muscle contraction, the regulation of apoptosis, protein folding, and carbohydrate biosynthetic processes. The notable disparity (weak overlap) in the number of redundant proteins observed among studies may be attributed to differences in the proteomic approaches and to the limited number of samples employed, including in general 4 to 8 high ultimate pH samples (DFD) and 6 to 14 control samples (Gagaoua et al., 2021). Moreover, very few studies used untargeted label-free quantitation of proteins in DFD muscles (Poleti et al., 2018; Wu et al., 2020; Kiyimba et al., 2021; Kiyimba et al., 2023; Patinho et al.,
2024a), which is not enough to fully cover the entire underlying mechanisms of this complex quality defect. In fact, most of the proteomics studies applied on DFD beef samples used conventional methods such as mono or 2-DE electrophoresis coupled with mass spectrometry, MALDI-TOF, LC-MS/MS, liquid isoelectric focusing (OFFGEL) and nanoESI-HDMSE technology (Franco et al., 2015; Fuente-García et al., 2019; Fuente-García et al., 2020; Hughes et al., 2019; Mahmood et al., 2018; Mato et al., 2019). Recent developments in mass spectrometry-based proteomics techniques using data-independent acquisition (DIA) such as sequential window acquisition of all theoretical mass spectra (SWATH-MS) offer a promising approach to improve the accuracy of the results and propose valuable meat quality biomarkers. To the best of our knowledge, only a few studies have applied SWATH-MS in meat proteomics research (Tang et al., 2015; Wu et al., 2019; López-Pedrouso et al., 2021; Álvarez et al., 2023; Alessandroni et al., 2024), and none yet looked for proteins biomarkers of DFD meat condition with the SWATH-MS proteomics approach.

In this study, SWATH-MS, within the frame of shotgun proteomics, was employed for the first time to thoroughly characterize and quantify the *post-mortem* muscle proteome of representative and extreme DFD and normal-pH beef samples, using the largest existing database of this quality defect condition to date. The study further applied multivariate and chemometrics statistical approaches to refine the list of putative biomarkers to propose a list of candidates with high accuracy. This study marks a significant step towards 1) a deeper understanding of the molecular mechanisms associated with the occurrence of DFD beef and 2) the discovery of a reliable list of protein biomarkers.

## 2. Materials and Methods

### 2.1. *Meat sample collection from young Asturiana de los Valles bulls*

In this study, 2421 young bulls of the indigenous breed "Asturiana de los Valles" were monitored. The animals, aged between 14 and 18 months, underwent handling and slaughter at various commercial abattoirs in Asturias (Spain), adhering to safety and welfare guidelines as outlined by European Union regulations (Council Regulation (EC) No 1099/2009).

After the slaughtering process, the carcasses were transferred to 3 °C chill room. Subsequently, at 24 h *post-mortem*, the ultimate pH ($pH_{24}$) was measured at the 13th rib level using a portable adapted pH meter (InLab Solids Go-ISM, Mettler-Toledo S.A.E., Barcelona, Spain) equipped with an insertion electrode and an automatic temperature compensation probe.
pH meter calibration was made with standard buffer solutions of pH 4.0 and pH 7.0, specifically Hamilton DuraCal™ (Hamilton, Bonaduz AG, Switzerland). pH₂₄ allowed the first screening of the carcasses and their categorization into extreme DFD when the pH₂₄ was over 6.2. Typically, the standard pH₂₄ threshold in the loin for DFD carcasses is set at ≥ 6; however, in this study, it was fine-tuned to 6.2 to guarantee an unequivocal categorization of defective meat. For each identified DFD carcass (n = 26), a corresponding carcass from the same farm and normal pH₂₄ (range 5.4-5.5) was carefully chosen for the CONTROL group (n=26), ensuring an alignment in key characteristics such as age, transport, weight, and slaughter batch. Approximately 20 grams of the *Longissimus thoracis et lumborum* (LTL) muscle were obtained 24 h *post-mortem* from the left-side carcass of each animal, taken at the 13th rib level. Following this, the samples were snap-frozen in liquid nitrogen and stored at -80 °C.

To further assess beef quality traits and confirm that carcasses were darker, a section of the striploin (LTL muscle), covering the 6th to 13th ribs, was removed from the left-half carcass at 48 h *post-mortem*. This section was immediately transported to the laboratory, where it was cut into 2.5 cm steaks (unless otherwise specified) for subsequent analysis. Color and water activity traits were assessed in fresh beef samples at 48 h *post-mortem*. Vacuum bags made of polyamide 20 µm/polyethylene 70 µm were used to vacuum-pack the steaks destined for meat toughness (3.5 cm thick). They were then aged in darkness at 4°C ± 1°C for different *post-mortem* aging times (3, 7, and 14 days), followed by freezing and storage at -20°C.

## 2.2. *Meat quality measurements*

Meat color was measured using a portable Minolta CM-2300d spectrophotometer (Konica Minolta Inc., Osaka, Japan) at three random locations on the exposed cut surface from the 6th rib steak of the LTL at 48 h *post-mortem*. An aperture size of 8 mm with a D65 illuminant and a 10° standard observer was consistently applied throughout the experiment. The CIE lightness (L*), redness (a*), yellowness (b*) were recorded after 60 min of blooming at room temperature (20 °C). Chroma (C* = $\sqrt{(a^{*2} + b^{*2})}$), related to intensity, and Hue angle (h° = arctan(b*/a*)), representing the transition from red to yellow color were also determined. The mean value of three measurements was used (King et al., 2022).

The water holding capacity of fresh meat was assessed by measuring the drip loss from duplicate fresh samples (1.5 g each) collected at 48 h *post-mortem*. This was done using a modified version of the centrifugal method originally described by Jauregui, Regenstein, and Baker (1981) with little modifications (González-Blanco et al., 2023).
Meat toughness was evaluated using the Warner-Bratzler shear force (WBSF) test on 2.5 cm steaks aged for 3, 7, and 14 days *post-mortem*. Before testing, the steaks were thawed at 4 °C for 24 h and then cooked in a water bath at 75 °C for 30 min. As soon as the steaks cooled, eight cores (1 cm² in cross-section, with muscle fibers aligned parallel to the sample's longitudinal axis) were sliced perpendicularly from each steak using the TA.XT Plus instrument (Stable Micro Systems, London, UK) equipped with a Warner-Bratzler blade set HDP/WBV with a 'V' slot. The load cell was 30 kg, and the crosshead speed was set at 1 mm/s. The maximum load needed for complete cutting was recorded. Results were expressed as the average WBSF maximum load (N) value for each steak.

## 2.3. *Extraction of Muscle proteins*

Frozen muscle tissue samples (150 mg) were initially homogenized in 3.5 mL of 8.3 M urea, 2 M thiourea, 1% dithiothreitol, 2% 3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate, 2% immobilized pH gradient (IPG) buffer pH 3-10 using an Ultra-Turrax following the procedure outlined by Bouley, Chambon, & Picard, (2004). The protein homogenates were then incubated for 30 min at 4 °C followed by centrifugation at 10,000 ×g for 30 minutes (Alessandroni et al., 2024). The protein content of the resulting supernatant, which contains total proteins (both sarcoplasmic and myofibrillar proteins), was determined using the Bradford method (Bradford, 1976). These protein extracts were subsequently stored at -80°C until further analysis by LC-MS/MS analyses.

## 2.4. *SWATH-MS proteomics*

### 2.4.1. One Dimensional SDS-PAGE and protein bands preparation

The protein extract was combined in a 1:1 ratio with Laemmli sample buffer (Bio-Rad Laboratories, Deeside, UK), and then concentrated on 1D stacking gel of SDS-PAGE using 12% polyacrylamide gels. Each gel lane was loaded in duplicate with 40 µg of frozen protein extracts, and the electrophoresis was conducted for approximately 15 min at 4 watts to concentrate the proteins (Zhu, Gagaoua, Mullen, Viala, et al., 2021; Lamri et al., 2023a). After three rinses with Milli-Q water, the gels were stained with EZ Blue Gel staining reagent (Sigma, Saint Louis, MO, USA) with gentle agitation for 2 hours. A sterile scalpel was used to cut out the protein bands from the washed gels and they were transferred to Eppendorf tubes. Discoloration and reduction-alkylation were carried out following the method described by Gagaoua, Troy, & Mullen, (2021). Subsequently, the dried protein bands were stored at -20 °C until SWATH-MS analysis.
2.4.2. Creation of the reference spectral library and protein quantification by SWATH-MS

After protein reduction and alkylation, the samples underwent enzymatic digestion with 20 ng/µL Trypsin (Promega, Madison, WI, USA) in 20 mM ammonium bicarbonate, followed by 16 h incubation at 37 °C. The resulting peptides were extracted through a 20 min incubation in a solution of 60% acetonitrile and 0.5% formic acid, followed by vacuum concentration, and stored at -20 °C. To ensure a good representation of the peptides and proteins across all samples, pooled vials for extreme DFD and CONTROL groups were prepared by using equal mixtures of the original samples and were subjected to shotgun data-dependent acquisition (DDA) using micro-LC-MS/MS. Four micrograms of samples were separated by reversed-phase chromatography. A gradient of 95 min was created using a micro-LC system (Eksigent Technologies nanoLC 400, Sciex) coupled to a high-speed Triple TOF 6600 mass spectrometer (Sciex) with a microflow source. Chromatographic separation utilized an Eksigent C18 150 × 0.30 mm, 3 mm particle size, and 120 Å pore size silica-based reversed-phase column (Eksigent, Sciex) coupled with a YMC-TRIART C18 trap column (YMC Technologies, Teknokroma) with a 3 mm particle size and 120 Å pore size, switched online with the analytical column. A gradient elution system, utilizing 0.1% formic acid in water as mobile phase A and 0.1% formic acid in acetonitrile as mobile phase B, facilitated a 40-minute gradient from 2% to 40% mobile phase B.

The eluted peptides were analyzed using a hybrid quadrupole-TOF mass spectrometer connected to a Triple TOF 6600 (Sciex, Redwood City, CA, USA), operating with a data-dependent acquisition system in positive ion mode. Peptide and protein identification from the MS raw files was performed using Protein Pilot software (v5.0.1, Sciex) along with a bovine-specific Uniprot database (https://www.uniprot.org/). A false discovery rate (FDR) of 1% was applied to both peptides and proteins. Then, the MS2 spectra (MS/MS spectra) of the identified peptides were utilized to create a spectral library for SWATH-MS peak extraction using the add-in for PeakView software (V.2.2, Sciex) MS/MSALL with SWATH Acquisition MicroApp (V.2, Sciex) as described by Alessandroni et al. (2024). Peptides with a confidence score exceeding 99% (as obtained from the Protein Pilot database search) were integrated into the spectral library.

Relative quantification on each sample was conducted using a data-independent acquisition method (DIA), specifically SWATH-MS, on a TripleTOF 6600 LC-MS/MS system (Sciex, Framinghan, MA, USA). A total of 52 samples (comprising twenty-six pairs of extreme DFD and CONTROL) were individually analyzed (4 micrograms/sample) using a SWATH-MS method comprising 100 overlapping windows. The LC-MS equipment and the previously
described LC gradient were employed for the analysis. Data extraction was conducted using
PeakView v2.2 (Sciex, Framingham, MA, USA) with the SWATH-MS Acquisition MicroApp
v2.0. The application processed the data using the created spectral library and applied the
following parameters: ten peptides per protein and seven fragments per peptide (excluding
shared and modified peptides) with an FDR below 1%. Protein quantification was determined
by summing the peak areas of the given peptides.

## 2.5. Statistical analyses

The effect of sample type (CONTROL vs DFD), post-mortem time (3, 7 and 14 days) and its interaction on the meat quality attributes was analyzed by Analysis of Variance (ANOVA) followed by Tukey's post hoc test using the statistical software XLSTAT 2022.4.1 (AddinSoft, Paris, France). Differences were considered statistically significant with a *P* < 0.05.

The SWATH-MS proteomics data were processed using both the MetaboAnalyst 6.0 platform (https://www.metaboanalyst.ca/) XLSTAT 2022.4.1 (AddinSoft, Paris, France). First, the raw data were scrutinized for data entry errors, missing data or outliers. Proteins with >50% missing quantitative values were excluded from the analysis. For the remaining proteins, missing values were estimated using the k-nearest neighbour (KNN) algorithm. Following this, data normalization was conducted through a log₁₀ transformation, followed by Pareto scaling as described by Lamri et al. (2023b). This transformation pretends to reduce the relative importance of high-value data within each variable (protein), ensuring the integrity of the dataset and enabling the capture of biological differences between both beef quality groups. A Volcano plot was conducted to identify the differentially abundant proteins (DAPs) between CONTROL and DFD beef. Significance was considered at a 1.2-fold change (FC) threshold and p-value of 0.01.

Within the objectives of discriminating the two quality groups with protein biomarkers from
the quantified proteome database, chemometrics analyses, namely Partial Least Squares-
Discriminant Analysis (PLS-DA) and its extension Orthogonal Partial Least Squares-
Discriminant Analysis (OPLS-DA) were conducted. OPLS-DA integrates orthogonal signal
correction (OSC) with PLS-DA to eliminate systematic variance in the dataset unrelated to
sample class (Bylesjö et al., 2006). These algorithms correlate muscle proteome and category
classes (CONTROL vs DFD) and allow the identification and ranking of putative biomarkers.
Based on their importance in discriminating CONTROL and DFD beef, proteins were ranked
using the variable importance in projection (VIP). In this study, only proteins with very high VIP
values (≥ 2.0) were considered as top discriminators (Gagaoua, Monteils, & Picard (2018).
Principal Component Analysis (PCA) was further carried out to examine the relationships among the commonly identified DAPs across the different statistical approaches using the statistical software XLSTAT 2022.4.1 (AddinSoft, Paris, France).

To study the relationships between meat quality traits and the protein abundance Pearson correlation analyses between all the proteins quantified by SWATH-MS and quality traits (pH, drip loss, L*, a*, b*, C*, hº and WBSF at 3, 7, 14 days) were calculated for the entire sample datasets (CONTROL and DFD together) or independently for CONTROL and DFD samples, using XLSTAT 2022.4.1 (AddinSoft, Paris, France). In addition, multiple linear regression models were performed using XLSTAT 2022.4.1 to create the best models (Maximal adjusted R²) for pH₂₄ (variable used to classify CONTROL and DFD samples) (as dependent variable) using the whole protein dataset (as independent variables) for the entire sample set (CONTROL and DFD) or independently for CONTROL and DFD samples following the criteria described by (Gagaoua et al., 2017). Partial R-Squares, regression coefficients, t-values, and significance of each retained variable are given for the models. A PCA analysis was also performed using the meat quality traits to highlight the degree of separation of CONTROL and DFD.

## 2.6. Bioinformatics analyses

Bioinformatics on the DAPs identified in the LTL muscles began with an examination using the STRING v.11.0 database (https://string-db.org/) to construct the Protein-Protein Interaction (PPI) network, setting a minimum required interaction value of 0.150. In addition, Metascape® (https://metascape.org/), an open-source web service tool, was used to pinpoint the predominant enriched Gene Ontology (GO) terms and pathways among the protein lists, following the methodology outlined by Gagaoua et al. (2021).

# 3. Results

## 3.1. Meat quality traits

**Table 1** presents a comparison of meat quality characteristics between the CONTROL and DFD beef samples. DFD meat displayed a darker, brownish, and more saturated color, evidenced by lower L* and a* values (*P* < 0.001), along with reduced b* (*P* < 0.01), and C* (*P* < 0.001) and lower drip loss (*P* < 0.001). Interestingly, hue angle did not seem to be a color trait allowing to distinguish dark-cutting beef from normal color beef meat (Table 1). In addition, DFD samples consistently showed stable meat toughness values throughout meat aging, significantly lower
than those of CONTROL samples at 3 (*P* < 0.001), 7 (*P* < 0.001), and 14 days (*P* < 0.01) *post-mortem*. In contrast, CONTROL samples showed a typical *post-mortem* tenderization pattern, with a continuous and significant (*P* < 0.001) decrease of meat toughness during aging (**Fig. 1**). In support of these differences, the separation of the two groups using all the meat quality traits given in **Table 1** was evident from the PCA depicted in **Fig. 2**.

## 3.2. *Quest for differentially abundant proteins between CONTROL and DFD meat*

In this study, SWATH-MS proteomics led to the identification and quantification of 758 proteins at an FDR of 1%, 10 peptides/protein and 7 transitions of the fragments/peptide. The comparison with the Volcano analysis (Fig. 3A) yielded 35 differentially abundant proteins (DAPs) with significant differences (*P* < 0.01) in abundance between CONTROL and DFD meats (Table 2), exhibiting a minimum fold change of 1.2. Among them, 21 were overabundant in DFD meat, while 14 exhibited higher abundance in CONTROL (Table 2). Through manual annotation using both in-house expertise and Uniprot KB, the 35 proteins were categorized into six major biological pathways: (i) energy metabolism (n = 12; 34%); (ii) binding and signaling (n = 10; 29%); (iii) muscle contraction, structure, and associated proteins (n = 4; 11%); (iv) proteolysis, hydrolase, and activity regulation (n = 4; 11%); calcium homeostasis (n = 3; 9%); and (vi) heat shock proteins (n = 2; 6%).

## 3.3. *Pathway and process enrichment analysis*

Based on the Metascape® analysis, 10 highly enriched terms were validated (Fig. 3B), allowing the creation of a unique process enrichment network (Fig. 3C). The top two enriched term clusters were as expected “carbohydrate biosynthetic process (GO:0016051)” and “nucleobase-containing small molecule metabolic process (GO:0055086)”, followed by “lipoprotein transport (GO:0042953)”, “response to muscle activity (GO:0014850)”, “negative regulation of plasma membrane bounded cell projection assembly (GO:0120033)”, “wound healing (GO:0042060)”, “synapse organization (GO:0050808)”, “striated muscle contraction (GO:0006941)”, “response to unfolded protein (GO:0006986)”, and “inflammatory response (GO:0006954)”.

## 3.4. *Protein-Protein Interactions*

The protein-protein interaction (PPI) network built using String database highlighted the significance of the energy metabolism pathway in the muscle-to-meat conversion process (Fig.
4), hence allowing to revealed a strong interconnectedness in the 6 biochemical pathways described in **Table 2**. Within this network, Malate dehydrogenase (MDH2) showed significant involvement in the energy metabolism pathway and exhibited the highest number of interactions with other proteins (n = 12). Conversely, the 10kDa Heat shock protein (HSPE1) exhibited the highest number of interactions with other pathways, including energy metabolism, muscle contraction, and structure, binding and signaling, proteolysis, hydrolase and activity regulation, calcium homeostasis, and the other small heat shock protein HSPB2. The subsequent dominant pathway comprised binding and signaling proteins, which revealed multiple interactions among them.

### 3.5. Chemometrics analysis using Partial Least Squares Discriminant analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) to discriminate dark-cutting from normal beef

Results obtained with the PLS-DA (Fig. 5A,B) and the OPLS-DA (Fig. 5C,D) enabled clear discrimination between CONTROL and DFD samples. The key proteins with the highest VIP scores (≥ 2) contributing to this differentiation are given in **Table S1** and **Table S2**. The overlap analysis on the relevant proteins common to both chemometric analyses (Fig. 5E) yielded 17 proteins: PFN2, RABGGTA, C4A, MIA3, GLB1, KIR2DL1, AGT, CYB5R3, CAND2, DMD, CAP2, APOBEC2, UBE2V1, BPIFA2B, DHRS7B, TRA, and ANXA6). When combining these results with those obtained through Volcano plot analysis (depicted in Fig. 3A), it was observed that 16 of these 17 proteins (all except TRA) are robust candidate biomarkers of DFD beef, irrespective of the statistical approach used (Fig. 5F). TRA showed VIP values over 2 in both PLS-DA and OPLS-DA (Table S1 and S2), but it was not one of the key proteins identified by the Volcano Plot. Therefore, it was not considered as a robust biomarker as it was not significant regardless of the different statistical analysis we performed.

The PCA using the 16 proteins allowed to confirm the clear separation of the two groups (Fig. 6). Twelve proteins (C4A, DMD, AGT, CAP2, GLB1, PFN2, CYB5R3, KIR2DL1, DHRS7B, RABGGTA, ANXA6, and APOBEC2) were more abundant at 24 h post-mortem in DFD beef, while four putative biomarkers (BPIFA1, UBE2V1, MIA3 and CAND2T) were more abundant in the CONTROL samples (Fig. 6). We further built other PCA combining the meat quality traits and the 16 putative protein biomarkers of DFD beef validated using the three statistical methods (Fig. 7). The two PCA considering the PC1 and PC2 (Fig. 7A,B) or PC1 and PC3 (Fig. 7C,D),
clearly showing the separation of DFD from CONTROL samples and highlighting the
associations that might exist among the meat quality traits and the putative protein biomarkers.

3.6. Pearson correlations analysis among the protein biomarkers and beef quality traits

Pearson correlation coefficients were calculated between the quality traits and the quantified proteins, both for the full database and separately for the DFD and CONTROL datasets (Table S3). Regarding pH<sub>24</sub>, significant correlations were found with four proteins (CUBN, DMD, RABGGTA, and HSP90AA5P). DMD and RABGGTA are among the 16 proteins recurrently appearing as potential biomarkers in the different analyses, showing significant positive correlation with pH<sub>24</sub> when examined in the DFD samples (r = 0.523 and r = 0.398 respectively for P < 0.05). On the other hand, CUBN exhibited a negative correlation of r = -0.411 (P < 0.05) for DFD samples, and HSP90AA5P exhibited a significant negative correlation (r = -0.578 and r = -0.427, P < 0.05) for CONTROL and DFD samples respectively.

Drip loss was correlated with CAND2, YWHAG, and SERPINC1. In the case of YWHAG, the correlation with drip loss was significant and positive for the complete data matrix (r = 0.51), as well as for the CONTROL samples (r = 0.426) and the DFD samples (r = 0.429) separately (P < 0.05). An intriguing phenomenon was noted for CAND2 and SERPINC1: while a positive correlation with drip loss was evident (r = 0.332 and r = 0.276, P < 0.05) when analyzing the full dataset, this relationship reversed to negative when focusing only on the DFD samples, being significant in the case of SERPINC1 (r = -0.418, P < 0.05).

The different color attributes showed correlations with several proteins, with the highest
correlation coefficients obtained for *a*\* with MDH2 (r = 0.586, P < 0.001) for the full database
and TNNT1 for both the full database (r = 0.545) and the CONTROL dataset (r = 0.611) (P <
0.001). Regarding WBSF, the highest correlations were obtained at 3 days *post-mortem* for ECH
(r = 0.508, P < 0.001) in the full dataset, and for the WBSF at 7 days *post-mortem* and PNF2 (r
= 0.516, P < 0.01) for the DFD samples individually.

3.6. Protein biomarkers to build first regression equations of dark-cutting and normal beef

The multiple linear regression models, using pH<sub>24</sub> as the dependent variable and protein abundances as independent variables, revealed significant relationships (Table 3; Fig. 8). For the full database (CONTROL and DFD, n = 52), 38% of the variability (P < 0.001) was explained by DHRS7B (positively), ESD, and WFDC18 (both negatively). The CONTROL model explained 55% of the variability (P < 0.001), including two proteins: SERPINA3-7 (positively)
and GSTM4 (negatively). The third model performed only with data from DFD samples explained 60% of the variability (*P* < 0.001) using two proteins: MYL2 (negatively) and TRA (positively).

## 4. Discussion

DFD beef is a major concern for the beef industry and retailers as this defect generates an abnormal appearance and reduces the product's shelf life (Ponnampalam et al., 2017). It is well known that animals raised under similar conditions can produce beef with significant differences in quality, showing variations in the final pH related to various pre-slaughter stressors, including the physiological state of the animal and its individual susceptibility to stress. In this study, among 2,421 commercial carcasses from the Asturiana de los Valles breed that were monitored for pH<sub>24</sub>, 26 exhibited a pH<sub>24</sub> ≥ 6.2, leading to DFD quality defects, indicating an incidence of approximately 1%. This incidence is lower than that previously reported by Mach et al. (2008). This discrepancy may be due to the extreme pH<sub>24</sub> threshold considered in the present study (>6.2), the docile temperament of the Asturiana de los Valles breed, which has undergone selective breeding to reduce aggressiveness and improve livestock performance (Eusebi et al., 2020), and the traditional management practices for rearing, transport, and slaughter followed in the northern of Spain.

When comparing the quality attributes of these carcasses with normal-pH<sub>24</sub> samples, significant differences (*P* < 0.01) were found for color, drip loss, and beef tenderness. Meat with high pH<sub>24</sub> was darker and exhibited lower drip loss and better tenderness than CONTROL, consistent with previous studies in different breeds and from diverse production systems (Franco et al., 2015; Poleti et al., 2018; Hughes et al., 2019; Kiyimba et al., 2023).

Although the detrimental effects caused by this beef quality defect are substantially characterized, the exact causes driving its occurrence remain multiple and not fully understood. Indeed, this issue is multifactorial and complex, and currently, no reliable biomarkers enable its early detection in the production chain. Several proteomics technologies have been employed to elucidate the muscle biology and biochemistry of DFD beef (for review: Gagaoua et al. (2021). In this study, a novel quantitative proteomics approach, SWATH-MS, has been successfully applied to identify and quantify the protein expression differences that could explain beef quality variations between dark-cutting and normal beef. Using SWATH-MS, 735 proteins across the 52 samples analyzed have been identified and successfully quantified following the filtering criteria given above. Subsequent analysis combining multivariate and chemometric methods
enabled the discovery of potential putative protein biomarkers. From the first pairwise
comparison that yielded 35 DAPs, 7 biomarkers these being DHRS7B, GYG1, MDH2, PGAM2,
EPM2A, ADSL, and MYOM3 were previously suggested as potential DFD biomarkers
(Gagaoua et al., 2021). The chemometrics analyses, allowed to confirm the interconnectedness
of the molecular pathways at interplay. Likely, the DAPs belong to different interconnected
molecular functions related to the energy metabolism (DHRS7B and CYB5R3), binding and
signaling (RABGGTA, MIA3, BPIFA2B, CAP2, APOBEC2, UBE2V1, KIR2DL1), muscle
contraction, structure and associated proteins (DMD, PFN2), proteolysis, hydrolases, and
activity regulation (AGT, C4A, GLB1, CAND2), and calcium homeostasis (ANXA6). Notably,
among these candidates, only DHRS7B had previously been linked to the occurrence of DFD
meats (Wu et al., 2020) while the remaining proteins are identified for the first time as potential
biomarkers of DFD condition, so these results provide new insights for a better understanding of
DFD beef determination using one of the largest databases to date.

4.1. *Putative protein biomarkers from the energy metabolism pathway*

DHRS7B, a member of the SDR family, is an oxidoreductase located in the endoplasmic reticulum (ER) membrane that exhibits reducing activity towards several substrates containing carbonyl groups, such as steroid hormones, xenobiotics, and other compounds (Stambergova et al., 2014). This oxidoreductase protein utilizes FAD/NADP/NAD+ as substrates to generate FADH/NADPH/NADH, thus explaining its role in DFD development. FADH/NADPH/NADH reducers are known to play a pivotal role in the electron transport chain, which facilitates mitochondrial respiration and metmyoglobin reduction (Tang et al., 2005), thereby influencing beef color. Mitochondria, responsible for the tricarboxylic acid cycle and oxidative phosphorylation, play a crucial role in *post-mortem* muscle metabolism, pH decline, beef color, and tenderness due to their involvement in oxygen consumption and reducing capacity (Holdstock et al., 2014; Picard & Gagaoua, 2020). CYB5R3 catalyzes the reduction of two molecules of Cytochrome b5 using NADH as the electron donor being involved in various oxidation and reduction reactions (de Cabo et al., 2010), possibly also contributing to the reduction of metmyoglobin. Wu et al. (2020) reported higher expression of energy metabolic related enzymes such as DHRS7B and MDH2 in dark-cutting beef due to the higher energy utilization rate, compared with normal pH beef. For example, Kiyimba et al. (2021) identified several proteins involved in the mitochondrial respiratory chain and oxidation-reduction processes that were upregulated in dark-cutting beef compared to normal-pH beef. This has been further confirmed in the meta-analysis of the previously published dark-cutting beef proteomics
studies (Gagaoua et al., 2021), highlighting increased production of NADH, a precursor playing a role in the reduction of metmyoglobin and oxygen consumption. However, in the present study, MDH2, which emerged as a relevant biomarker validated using the three statistical methods, was downregulated in DFD beef. The difference between our results and those of Wu et al. (2020) could be due to the fact that they targeted the mitochondrial muscle proteome after a specific mitochondrial extraction, while total extracts from muscle were used in this work. It can be pointed out that a higher pH than normal, NADH can protect the integrity of the mitochondria in dark cutting beef compared to normal beef. Therefore, using a total protein extract could underestimate the concentration of mitochondrial proteins in studying the DFD condition, but it more advantageous in terms of exploring in a holistic manner the underlying molecular pathways.

Additional putative protein biomarkers from the energy metabolism pathway were also identified. While not consistently present across all analyses, their relevance was acknowledged in some and in previous studies, affirming their potential as biomarkers for DFD beef. For instance, a higher abundance of GYG1 and PGAM2 was found in dark-cutting beef, consistent with previous studies that also found a positive relationship with DFD meat (Mahmood et al., 2018; Hughes et al., 2019). GYG1 is a protein primer for glycogen synthesis in muscle and liver that has been examined as a potential determinant of maximum glycogen storage capacity (Alonso & Whelan, 1995). Reduced glycogen in DFD could be ascribed to increased oxidative metabolism, which are prone to glycogen depletion under stress conditions (Lacourt & Tarrant, 1985). Increased GYG1 in DFD could be an adaptive response to low glycogen levels (Kraniou et al., 2000). Regarding PGAM2, it is a protein whose influence on carcass traits in livestock has already been described. For example, PGAM2 has been linked to beef color (Canto et al., 2015) and has been found to be negatively correlated with metmyoglobin-reducing activity and the ratio of reflectance at 630 nm and 580 nm, an indicator of color stability, in the *Semimembranosus* muscle (Nair et al., 2016). This protein plays an important role in coordinating energy production with the generation of reducing power and the biosynthesis of nucleotide and amino acids precursors. It catalyzes the conversion of 3-phosphoglycerate into 2-phosphoglycerate and releases energy during glycolysis in muscle tissues, hence partly explaining its greater abundance at 24 h *post-mortem* in DFD meat more likely due to the earlier shift from aerobic to anaerobic metabolism in these animals. ADSL and EPM2A showed lower abundance in DFD beef, consistent with a recent study (Kiyimba et al., 2021). ADSL is a crucial component of the purine nucleotide cycle as it converts adenylosuccinate to AMP and fumarate
(Bulusu et al., 2009). The downregulation of ADSL in DFD implies decreased IMP/AMP levels, which may result in lower activation of the enzyme phosphofructokinase, thereby potentially decreasing glycolysis and lactate formation *post-mortem* (England et al., 2015; Gagaoua et al., 2020). The molecular function of EPM2A is associated with glycogen binding and synthase activity. This protein was found to be less abundant in dark colored beef, even when the pH<sub>24</sub> was only slightly elevated (between 5.6 and 5.8), compared to normal-pH beef (Kiyimba et al., 2023).

Stress-susceptible animals have been shown to have low glycogen levels at slaughter and increased mitochondrial proteins and respiration rate as an adaptive response to restore depleted cellular energy levels (Kiyimba et al., 2021). However, the upregulation of mitochondrial electron transport proteins in DFD beef could enhance oxygen consumption, leading to a reduction in the amount of cellular oxygen available for proper muscle blooming, preventing the conversion of deoxymyoglobin to oxymyoglobin which ends in dark-colored muscles (English et al., 2016).

## 4.2. Putative protein biomarkers associated with protein binding and signaling

The biological pathway with the highest number of common putative protein biomarkers among the different statistical methods belongs to protein binding and signalling. Interestingly, none of these proteins had been previously associated with DFD meat. KIR2DL1, RABGGTA, CAP2, and APOBEC2 showed higher abundance while BPIFA2B, MIA3 and UBE2V1 were downregulated in DFD meat. Post-slaughter hypoxia induces oxidative stress, leading to excessive ROS accumulation causing damage in macromolecules (DNA, proteins and lipids) and mitochondria, hence altering calcium homeostasis. All these cellular alterations may trigger cell death processes such as apoptosis, leading to cytoskeleton degradation, significantly affecting the muscle-to-meat conversion and therefore, the meat quality traits such as WHC, color and tenderness (For review: Ouali et al., 2013; Purslow et al., 2021).

It has been described that KIR2DL1 is upregulated under hypoxic conditions to decrease cytotoxic activities (Pegram et al., 2011). Consistent with this, its expression is increased in DFD meat, which has been shown to have higher oxidative stress (Díaz-Luis et al., 2020; González-Blanco et al., 2024). KIR2DL1 showed the highest discriminant potential in the PLS-DA. Additionally, it was ranked among the top three relevant putative biomarkers in the OPLS-DA and by fold change in the Volcano plot analysis. CAP2 was overexpressed in DFD beef, exhibiting the highest fold change according to the Volcano plot and ranking second in
importance (VIP = 3.445) in PLS-DA. Cyclase-associated proteins (CAPs) are evolutionarily conserved proteins with roles in regulating the actin cytoskeleton and in signal transduction. CAP2 is a component of myofibrillar M-bands. Although its precise function remains unclear, it has been implicated in skeletal muscle development, particularly in facilitating the exchange of α-actin isoforms during myofibril differentiation (Kepser et al., 2019). Our findings align with those of Schulte et al. (2023), who found increased abundance of CAP2 in *Longissimus thoracis* muscles from carcasses with high pH (pH > 5.84) at 6 hours *post-mortem*. We suggest that this protein would play a role through the light scattering of the muscle (Purslow et al., 2020).

UBE2V1, associated with ubiquitin modification, was the third common protein of this pathway, being the most relevant among the DAPs with lower abundance in DFD beef. UBE2V1 plays a role in the error-free DNA repair pathway contributing to the survival of cells after DNA damage. It has been found to be downregulated in DFD, which is consistent with the higher macromolecules damage observed in DFD (González-Blanco et al., 2021). Other common DAPs from this pathway were RABGGTA and MIA3, both related to sarcoplasmic reticulum (SR) homeostasis, recently suggested as a key organelle for the discovery of meat quality biomarkers (González-Blanco et al., 2023). RABGGTA, a protein involved in cellular transport that plays a crucial role in calcium release from the SR, was found to be overexpressed and positively correlated with pH<sub>24</sub> (r= 0.40, *P* < 0.05) in DFD samples. The transport and Golgi organization protein, MIA3, serves as a selective receptor for large cargo proteins, such as apolipoproteins (Santos et al., 2016). Suppression of MIA has been associated with defects in SR morphology and induces SR stress (Ríos-Barrera et al., 2017). MIA exhibited lower abundance in DFD beef, which aligns with our previous results González-Blanco et al. (2023) showing higher oxidative stress and a greater response to unfolded proteins in the SR of DFD beef. The overabundance of RABGGTA may be related to an attempt to prevent alterations at the SR level in DFD muscles. Finally, the other two common DAPs involved in binding and signaling found in this study were APOBEC2, a member of the apoB mRNA editing enzyme, catalytic polypeptide-like family, that was more abundant in DFD beef and BPIFA, a lipid transfer/lipopolysaccharide binding protein that was less abundant in DFD. The high abundance of APOBEC2 in tender DFD beef samples, is in line with its positive correlation with beef tenderness recently reported for young Limousin bulls (Zhu, Gagaoua, Mullen, Kelly, et al., 2021). Further research is needed to elucidate the pathways and roles that these proteins would play as biomarkers of DFD meat and potentially other meat quality traits.
Apart from the aforementioned proteins that were consistently identified across all the analyses, other proteins related to protein binding and signaling, such as SERPINC1, MCTS1 and OLA1, emerged as relevant in some of the analyses performed in this study. SERPINC1, in particular, was found to be less abundant in DFD beef. However, a recent study by Patinho et al. (2024a) reported that SERPINC1 was upregulated in DFD beef from young Nellore bulls at both 9 and 44 h *post-mortem*. This protein seems to have a role in the control of thrombin activity in muscle tissue. SERPINC1, also known as antithrombin-III, belongs to the serine protease inhibitors superfamily (serpins), and some of them are strong caspase inhibitors, playing a pivotal role in the conversion of muscle into meat (Zamora et al., 2005; Gagaoua et al., 2015). Among the bovine SERPINA3 members, also known as alpha-1-antichymotrypsin, it has been found that SERPINA3-7 can inhibit initiator (caspase 8 and caspase 9) and effector caspases (caspases 3, 6, and 7). In this study, SERPINA3-7 was positively retained in the regression model of the CONTROL beef, suggesting a role in the regulation of apoptosis. Another member of bovine SERPINA3 (SERPINA3-2) was also found in the recent study by Patinho *et al.* (2024a) to be up-regulated at 9 and 44 h *post-mortem*.

OLA1, which hydrolyzes ATP along of GTP with lower efficiency, exhibited greater abundance in DFD beef. It has been previously described as a potential biomarker of high intramuscular fat muscles, which are related to more oxidative pathways representative of DFD beef (Bazile et al., 2019). Additionally, OLA1 has been associated with beef color stability (Yu et al., 2017). Moreover, OLA1 stabilizes HSP70, resulting in its upregulation and increased stability during heat shock (Mao et al., 2013). This agrees with previous studies from our group, where greater expression of HSP70 was found in DFD beef (González-Blanco et al., 2021).

### 4.3. Putative protein biomarkers associated with muscle contraction, structure, and associated proteins

The crucial role of biological pathways related to muscle structure integrity in the muscle-to-meat conversion has been widely described (Gagaoua et al., 2021). Thus, the breakdown and weakening of key myofibrillar and cytoskeletal proteins by endogenous proteolytic systems are fundamental for beef quality determination, including through the light scattering mechanisms and their impact on color determination (Purslow et al., 2021). DMD and PFN2, were identified as relevant in all multivariate analyses. None of them had previously been linked to meat quality defects. Dystrophin (DMD) showed significantly higher expression in DFD, being one of the most relevant DAPs found in this study, with a high fold change rate and VIP values exceeding
2.7 for both PLS-DA and OPLS-DA. DMD is essential in the formation of costameres, which maintain sarcolemmal stability and selective permeability. The decrease in muscle pH during early *post-mortem* may impact the intermediate filaments of muscle fibers and costameres, causing muscle fiber contraction (Huff-Lonergan & Lonergan, 2005; Bee et al., 2007). This contraction allows for the release of water into extra-myofibrillar spaces during *rigor mortis* (Huff-Lonergan & Lonergan, 2005), potentially explaining the higher drip loss in CONTROL samples (with lower pH₂₄) (P < 0.000; **Table 1**). Drip loss is also influenced by the degradation rate of cytoskeletal proteins, especially costameric proteins (Wojtysiak & Górska, 2018), as is the case with DMD. In this study, DFD meat with a higher pH₂₄, closer to the optimal pH for calpains, which are cytosolic enzymes that degrade myofibrillar and cytoskeletal proteins (Bhat et al., 2018), showed a positive correlation between DMD and pH (r = 0.523, *P* < 0.01). This suggests faster or more intense *post-mortem* proteolysis in DFD muscles, with higher pH from earlier stages, resulting in increased WHC that absorbs light instead of reflecting it from the surface, making the meat appear darker (Hughes et al., 2019; Purslow et al., 2020; Purslow et al., 2021). All these findings indicate the potential of DMD as a biomarker for DFD meat. PFN2 is an actin monomer-binding protein that regulates actin polymerization. In the presence of ATP, it promotes actin filament assembly at the barbed end and affects actin homeostasis during cell movement (Pernier et al., 2016). Laville et al. (2009), in a comparative study of *Longissimus dorsi* pork meat with high and low shear force values, found increased levels of PFN2 in the tender group. Similarly, Malheiros et al. (2019) reported positive regulation of PFN2 in tender Nellore beef. Consistent with these results, a greater abundance of PFN2 was found in DFD beef which exhibited abnormally low tenderness during maturation. However, it is important to note that a positive correlation between PFN2 and WBSF at 7 days *post-mortem* (r=0.52, *P* < 0.01) was found in DFD samples, whose WBSF values remained unaltered throughout meat aging. This positive correlation at 7 days could result from both the effect that PFN has on the cytoskeletal stability and to the lower activity of muscle proteolytic systems at this *post-mortem* time, compared to control beef, due to the high ultimate pH of this type of beef which is far from optimal.

In addition to these, other proteins with essential roles in muscle structure and organization
were also identified as relevant in some of the different analyses performed in this study. For
instance, myomesin-3 (MYOM3), involved in stabilizing the thick filament lattice and the
sarcomere structure, was previously linked positively with DFD beef (Kiyimba et al., 2021). In
the present study, it was also found to be upregulated in DFD beef, confirming its role as a DFD
biomarker. It is important to note that the two other isoforms of myomesin (MYOM1 and MYOM2), were found to be negatively related with DFD beef (Gagaoua et al., 2021). Additionally, MYL2 was retained negatively in the regression model of the DFD beef. Consistent with our results, Franco et al. (2015) observed lower abundance of MYL2 in DFD beef from "Rubia Gallega" using two-dimensional electrophoresis. These authors suggested that this difference could be due to an earlier and more intensive enzymatic degradation of the myofibrillar structure in DFD meats, which could also explain the differences found in WBSF, with very tender meat in DFD meat compared to the CONTROL samples from early post-mortem stages. Accordingly, many other studies, including those that used proteomics on divergent pH samples, have found higher tenderness in DFD beef from *Bos indicus* and *Bos Taurus* (Franco et al., 2015; Poleti et al., 2018; González-Blanco et al., 2023; Patinho et al., 2024a; Patinho et al., 2024b).

### 4.4. Putative protein biomarkers related to proteolysis, hydrolase, and activity regulation

Four common DAPs from this pathway were identified: GLB1, C4A and AGT showed high abundance, while CAND2 exhibited low abundance in DFD meat. GLB1 is an enzyme that catalyzes the hydrolysis of glycosidic bonds and plays a key role in metabolic and cellular signaling processes (Fig. 4). It could be involved in the degradation of complex carbohydrates present in muscle tissue, affecting meat tenderness, flavour, and WHC in DFD meat during storage (Matarneh et al., 2023). AGT is a precursor of angiotensin II, which controls fluid and electrolyte balance (Yahav et al., 1995); hence, its higher abundance could influence the water homeostasis of DFD muscles and, consequently, WHC and meat tenderness during the post-mortem process. Lastly, C4A is part of the cytotoxic defense system involved in the elimination of invading foreign cells and the initiation of inflammation. It can be also altered by excessive oxidative stress (Hart et al., 2004). The results suggest a low-grade inflammation in DFD carcasses, closely associated with oxidative stress and an excess of lipid peroxidation (Rull et al., 2010), which would increase the levels of C4A. CAND2 plays a significant role in the degradation of specific proteins within the cell through the proteasome-ubiquitin system (Bulatov & Ciulli, 2015), a vital process for regulating various cellular functions, including stress response, cell signaling, and cell cycle control. Its lower abundance in DFD could be related to the higher accumulation of oxidative stress and damage in the muscle cells. CAND2 showed opposite correlation with color parameters (*a*\* and *C*\*) between CONTROL and DFD beef. A positive correlation was found in CONTROL samples between CAND2 and *a*\* (r = 0.381) and *C*\* (r = 0.43), whereas a negative correlation (r = -0.355; and r = -0.432 for *a*\* and *C*\*
respectively) (*P* <0.05) was noted in DFD muscles. These differences can be explained by both the effect of higher pH on the redox state of myoglobin and the greater proteolysis affecting light scatter in the DFD meat (Gagaoua et al., 2021).

### 4.5. Miscellaneous proteins associated with other biochemical pathways

Some other proteins associated with calcium homeostasis and Heat shock proteins (HSPs) were found to be relevant in Volcano plot pairwise analysis. Calcium plays a crucial role in the muscle-to-meat conversion process and has significant consequences for meat quality (Purslow et al., 2021). Indeed, disruption in calcium homeostasis, leading to elevated levels of free calcium, has been linked to DFD beef (Torres-Burgos et al., 2019) and meat color (Purslow et al., 2021). Our findings revealed three putative protein biomarkers associated with calcium regulation: ANXA6, HMCN2 and CUBN. ANXA6 has previously been suggested as a potential tenderness biomarker (Bjarnadóttir et al., 2012; Gagaoua et al., 2021). A decrease in ANXA6 abundance in tender beef might accelerate calcium release and boost calpain activity (Purslow et al., 2021). CUBN plays a role in vitamin D metabolism, and the increase in dietary vitamin D supplementation in bovine has been associated with undesirable changes in meat color by reducing *a*\* and *C*\* values (Rafalska, 2017). In our study, the upregulation of the protein CUBN in DFD beef could contribute to the darker color of the meat.

Two DAPs belonging to the small HSPs family were identified in this study: HSPE1 and HSPB2. While their association with DFD had not been previously documented, both have been implicated in the variability of meat quality traits in previous research (Yu et al., 2017; Silva et al., 2020). Small HSPs protect cells against oxidative damage, thereby maintaining muscle structural integrity. HSPE1 exhibited higher abundance in dark-cutting beef and displayed significant interactions with other biological pathways (Fig. 4). This protein, induced by stress, is located in the mitochondrial matrix and plays a crucial role in maintaining normal mitochondrial function (Lau et al., 1997). Shan et al. (2003) found that its overexpression stabilized mitochondrial membrane potential by inhibiting caspase-3, thereby attenuating muscle cell death. Hence, the increased levels of HSPE1 in DFD meat represent a cellular attempt to repair protein damage and maintain viability by inhibiting apoptosis. On the other hand, HSPB2 was less abundant in dark-cutting beef. HSPB2 is critical for stabilizing myofibrillar proteins and protecting cytoskeletal proteins from fragmentation induced by stress (Pivovarova et al., 2005). Furthermore, chaperone proteins prevent protein aggregation and denaturation (Sayd et al., 2006), processes that occur during the conversion of muscle into meat and could compromise
myoglobin stability and meat color. Therefore, the overabundance of HSPB2 in CONTROL beef, along with its antiapoptotic and chaperone functions, could shield proteins from damage and contribute to meat color stability. Accordingly, positive correlations of HSPB2 with a* (r=0.491) and C* (r=0.532) values were found for CONTROL samples.

**5. Conclusions**

In this study, a proteomic approach using SWATH-MS, combined with multivariate and chemometric statistical methods, was employed for the first time to identify biomarkers of DFD beef from the "Asturiana de los Valles" breed. This was made possible thanks to the large database we created, which surpasses the ones used in previous proteomics studies. Several putative protein biomarkers were found to be relevant within the different multivariate analyses; likewise, 16 seemed to be good candidate biomarkers. Among them, DHRS7B, had been previously identified as a DFD biomarker and validated in this experiment. Apart from these 16, some other proteins identified as relevant by at least one of the statistical analyses in this study, were previously found to be related to DFD beef such as GYG1, MDH2, PGAM2, ADSL and MYOM3. The identified protein biomarkers using SWATH-MS belong to different, but interconnected biological pathways corroborating the role of muscle structure, energy metabolism, binding and signaling mechanisms, calcium homeostasis, apoptosis and proteolysis in DFD meat development and determination. Hence, this study not only provides new insights into the underlying mechanisms of DFD, but also validates the association of certain protein biomarkers with this quality defect. It suggests new protein biomarker candidates using a larger database that will contribute to feed the pipeline of biomarker discovery for the evaluation of such quality condition using appropriate targeted proteomics. Once validated across different breeds and rearing systems, these putative biomarkers could be proposed for the early track of beef defects, thus reducing economic losses and food waste, hence contributing to the improvement of the profitability and sustainability of the beef sector.

**CRediT authorship contribution statement**

**Laura González-Blanco**: Methodology, investigation, data curation, formal analysis, writing–original draft, and visualization. **Mamen Oliván**: Conceptualization, writing-review and editing, supervision, and funding acquisition. **Yolanda Diñeiro**: Investigation. **Susana B. Bravo**: Methodology, writing-review and editing. **Verónica Sierra**: Conceptualization, Methodology, data curation, writing-review, editing, supervision, and funding acquisition.
**Mohammed Gagaoua:** Conceptualization, methodology, data curation, visualization and writing-review and editing.

**Declaration of competing interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Acknowledgments**

This research was funded by MICIN/AEI/10.13039/501100011033 and ERDF “a way making Europe” under project number PID2021-123933OR-C31. L.G-B. acknowledges her grant, number PRE2019-091053, funded by MCIN/AEI/10.13039/501100011033 and ESF “investing in your future”. The authors are members of the research team OSKAR, funded by FICYT and FEDER (IDI/2021/000033). We thank the staff of the Area of Livestock Production Systems from SERIDA, Matadero Central de Asturias S. L., Matadero de Gijón, Alimerka S. L., ASINCAR, and ASEAVA for their cooperation and skilled management of the animals and carcasses.

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## Figure captions

**Fig. 1.** Evolution *post-mortem* of Warner-Bratzler Shear Force (WBSF) values (N: Newton) depicting the meat tenderization patterns of CONTROL (red dotted line) and DFD (green dotted line) samples. Data are represented as the mean ± SEM. Asterisks indicate significant differences between CONTROL and DFD at the same storage time. *** *P* < 0.001; ** *P* < 0.01.

**Fig. 2.** First two principal components of the PCA analysis depicting both the relationships among the beef quality traits: pH 24h, color traits (L*, a*, b*, C* and hº), Warner-Bratzler Shear Force (WBSF) at different aging times and the separation of DFD (green circles) and CONTROL (CON; red circles) beef samples.

**Fig. 3.** Bioinformatics analysis of differentially abundant proteins (DAPs) between CONTROL and DFD samples from the LTL bovine muscle. (A) Volcano plot illustrating the DAPs with a total of 35 proteins that were significantly different between CONTROL and DFD meat. Red dots represent proteins overexpressed in DFD beef, while blue dots represent the overexpressed proteins in CONTROL beef. Grey dots represent proteins that were not significant. Fold change threshold ≥ 1.2; p-value <0.01. (B) Pathway and process enrichment cluster analysis networks based on the 35 differential proteins, generated using Metascape (https://metascape.org/). (C) Functional enrichment analysis based on the list of significant Gene Ontology (GO) terms, ranked by their p-value.

**Fig. 4.** Protein-protein interaction network built using the 35 differentially abundant proteins (DAPs). The interaction map was generated from the web-based search STRING database (https://string-db.org/). Note that KIR2DL1 was replaced by KIR2DL5A because the STRING database did not find any proteins with the former name in *B. taurus*. The red bubbles represent proteins overexpressed in DFD beef, while the blue bubbles represent proteins overexpressed in CONTROL beef.

**Fig. 5.** Multivariate Chemometrics analysis. **A)** Partial least squares-discriminant analysis (PLS-DA) and score plot discriminating between the two beef groups. **B)** Top 31 proteins allowing separation based on the variable importance in projection (VIP ≥ 2). **C)** Orthogonal partial least squares-discriminant analysis (OPLS-DA) and score plot discriminating between the two beef groups. **D)** Top 36 proteins allowing separation based on the variable importance in projection (VIP ≥ 2). **E)** Venn diagram comparing (overlap) the retained proteins between PLS-DA (VIP ≥ 2) and OPLS-DA (VIP ≥ 2). Seventeen common proteins were found irrespective of the statistical analysis approach. **F)** Venn diagram showing the common proteins between Volcano plot, PLS-DA (VIP ≥ 2) and OPLS-DA (VIP ≥ 2) analyses. Sixteen common proteins were identified regardless of the statistical analysis approach.

**Fig. 6.** Biplot of the Principal Component Analysis (PCA) depicting the separation of CONTROL (red circles) and DFD samples (green circles) with the 16 putative protein biomarkers of DFD beef validated using the three statistical methods (Fig. 5F).

**Fig. 7.** Principal Component Analysis (PCA) depicting the separation of CONTROL (red circles) and DFD samples (green circles) within the **A)** PC1 and PC2 and its related **B)** bi-plot; and within the **C)** PC1 and PC3 and its related **D)** bi-plot; using the meat quality traits and the 16 putative protein biomarkers of DFD beef validated using the three statistical methods (Fig. 5F). The quality traits are colored I blue to distinguish them from the putative protein biomarkers.

**Fig. 8.** Correlations between measured pH₂₄ and predicted pH₂₄ using the best models for: **A)** the entire sample set (CONTROL and DFD); **B)** CONTROL samples and **C)** DFD samples. Adjusted R-squares corresponding to the prediction equations given in Table 3 for each sample set are shown.
<figure><img src="image_3.png" /><figcaption>Fig. 1.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Fig. 2.</figcaption></figure>
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 3.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 4</figcaption></figure>
PLS-DA

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

OPLS-DA

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 5.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Fig. 6.</figcaption></figure>
<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>Fig. 7.</figcaption></figure>
<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /><figcaption>Fig. 8.</figcaption></figure>
<table><caption>**Table 1.** The effect of pH<sub>24</sub> sample type (CONTROL vs DFD) on drip loss and color parameters.</caption><thead><tr><th>Variables</th><th>Time post-mortem</th><th>CONTROL</th><th>DFD</th><th>SEM</th><th>P-value</th></tr></thead><tbody><tr><td>pH</td><td>24 h</td><td>5.54</td><td>6.46</td><td>0.03</td><td>0.000</td></tr><tr><td>Drip loss (%)</td><td>48 h</td><td>31.0</td><td>21.9</td><td>0.69</td><td>0.000</td></tr><tr><td>Meat Color</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L*</td><td>48 h</td><td>36.2</td><td>29.4</td><td>1.25</td><td>0.000</td></tr><tr><td>a*</td><td>48 h</td><td>11.0</td><td>7.5</td><td>0.46</td><td>0.000</td></tr><tr><td>b*</td><td>48 h</td><td>12.3</td><td>8.1</td><td>0.95</td><td>0.003</td></tr><tr><td>C*</td><td>48 h</td><td>16.5</td><td>11.4</td><td>0.93</td><td>0.000</td></tr><tr><td>h°</td><td>48 h</td><td>47.4</td><td>42.9</td><td>2.27</td><td>0.160</td></tr></tbody></table>

SEM: Standard error of the mean.
<caption>**Table 2.** List of the 35 differentially abundant proteins (DAPs) from the Volcano plot results (given in **Fig. 3A**) organized by molecular function after manual and bioinformatics annotation.</caption>

1

<table><thead><tr><th>Uniprot ID</th><th>Protein name</th><th>Gene name</th><th>Fold Change</th><th>P-value (DFD vs CONTROL)</th></tr></thead><tbody><tr><td colspan="5"><strong>Energy metabolism proteins (n=12)</strong></td></tr><tr><td>Q3T0R4</td><td>Dehydrogenase/reductase SDR</td><td>DHRS7B *</td><td>0.72</td><td>0.000</td></tr><tr><td>Q29RN2</td><td>Glycogenin 1</td><td>GYG1 *</td><td>0.31</td><td>0.001</td></tr><tr><td>P07514</td><td>NADH-cytochrome b5 reductase</td><td>CYB5R3</td><td>0.61</td><td>0.002</td></tr><tr><td>Q32LG3</td><td>Malate dehydrogenase,</td><td>MDH2 *</td><td>-0.42</td><td>0.003</td></tr><tr><td>E1BJN3</td><td>Primary amine oxidase, liver</td><td>SAO</td><td>-0.42</td><td>0.003</td></tr><tr><td>E1BH79</td><td>Elongation factor-like GTPase 1</td><td>EFL1</td><td>-0.32</td><td>0.003</td></tr><tr><td>Q32KV0</td><td>Phosphoglycerate mutase 2</td><td>PGAM2 *</td><td>0.67</td><td>0.004</td></tr><tr><td>A5PK37</td><td>Laforin</td><td>EPM2A *</td><td>-0.55</td><td>0.004</td></tr><tr><td>A3KN12</td><td>Adenylosuccinate lyase</td><td>ADSL *</td><td>-0.46</td><td>0.005</td></tr><tr><td>A0A3Q1MBG9</td><td>Perilipin 4</td><td>PLIN4</td><td>-0.40</td><td>0.007</td></tr><tr><td>Q9TTK6</td><td>Membrane primary amine oxidase</td><td>AOC3</td><td>0.47</td><td>0.008</td></tr><tr><td>P22292</td><td>Mitochondrial 2-</td><td>SLC25A11</td><td>0.59</td><td>0.009</td></tr><tr><td colspan="5"><strong>Binding and signaling proteins (n=10)</strong></td></tr><tr><td>Q8SPQ5</td><td>Killer cell immunoglobulin-like</td><td>KIR2DL1</td><td>1.11</td><td>0.000</td></tr><tr><td>Q3SYR3</td><td>Probable C-&gt;U-editing enzyme</td><td>APOBEC2</td><td>0.46</td><td>0.000</td></tr><tr><td>F1N4C3</td><td>BPI fold containing family A,</td><td>BPIFA2B</td><td>-0.73</td><td>0.000</td></tr><tr><td>P41361</td><td>Antithrombin-III</td><td>SERPINC1</td><td>-0.35</td><td>0.001</td></tr><tr><td>Q0VC16</td><td>Transport and Golgi organization</td><td>MIA3</td><td>-0.60</td><td>0.001</td></tr><tr><td>F1N715</td><td>Adenyllyl cyclase-associated</td><td>CAP2</td><td>1.38</td><td>0.001</td></tr><tr><td>Q5EA80</td><td>Geranylgeranyl transferase type-2</td><td>RABGGTA</td><td>0.43</td><td>0.001</td></tr><tr><td>Q3SZ52</td><td>Ubiquitin-conjugating enzyme E2</td><td>UBE2V1</td><td>-0.78</td><td>0.002</td></tr><tr><td>Q2KIE4</td><td>Malignant T-cell-amplified</td><td>MCTS1</td><td>0.73</td><td>0.002</td></tr><tr><td>Q2HJ33</td><td>Obg-like ATPase 1</td><td>OLA1</td><td>0.38</td><td>0.009</td></tr><tr><td colspan="5"><strong>Muscle contraction, structure and associated proteins (n=4)</strong></td></tr><tr><td>A0A3Q1MDU8</td><td>Dystrophin</td><td>DMD</td><td>0.78</td><td>0.000</td></tr><tr><td>Q09430</td><td>Profilin-2</td><td>PFN2</td><td>0.68</td><td>0.001</td></tr><tr><td>E1BCU2</td><td>Myomesin 3</td><td>MYOM3 *</td><td>0.55</td><td>0.002</td></tr><tr><td>P36225</td><td>Microtubule-associated protein 4</td><td>MAP4</td><td>-0.74</td><td>0.007</td></tr><tr><td colspan="5"><strong>Proteolysis, hydrolase, and activity regulation proteins (n=4)</strong></td></tr><tr><td>Q58D55</td><td>Beta-galactosidase</td><td>GLB1</td><td>1.29</td><td>0.001</td></tr><tr><td>E1BNE2</td><td>Cullin-associated NEDD8-</td><td>CAND2</td><td>-0.52</td><td>0.002</td></tr><tr><td>P01030</td><td>Complement C4-A</td><td>C4A</td><td>0.73</td><td>0.002</td></tr><tr><td>P01017</td><td>Angiotensinogen</td><td>AGT</td><td>0.53</td><td>0.005</td></tr><tr><td colspan="5"><strong>Calcium homeostasis proteins (n=3)</strong></td></tr><tr><td>P79134</td><td>Annexin A6</td><td>ANXA6</td><td>0.45</td><td>0.002</td></tr><tr><td>E1B9K4</td><td>Hemicentin-2</td><td>HMCN2</td><td>-0.48</td><td>0.002</td></tr><tr><td>F1MKV7</td><td>Cubilin</td><td>CUBN</td><td>0.48</td><td>0.006</td></tr><tr><td colspan="5"><strong>Heat shock proteins (n = 2)</strong></td></tr><tr><td>P61603</td><td>10kDa heat shock protein,</td><td>HSPE1</td><td>0.56</td><td>0.008</td></tr><tr><td>V6F9A4</td><td>Heat shock protein beta-2</td><td>HSPB2</td><td>-0.37</td><td>0.010</td></tr></tbody></table>
¹ Several proteins may belong to more than pathway due to their multifunctional roles in the muscle.

*: Indicates differentially abundant proteins that overlap with the list of protein biomarkers reported in the integromics DFD beef study by Gagaoua et al., 2021.
<table><caption>**Table 3.** Regression equations using standardized data of pH<sub>24</sub> for the entire sample set (CONTROL and DFD samples, n=52), only CONTROL samples (CONTROL, n=26) and only DFD samples (DFD, n=26) using protein biomarkers.</caption><thead><tr><th>Depende<br>nt<br>Variable</th><th>Sample set</th><th>Adjusted<br>R<sup>2</sup><sup>a</sup></th><th>S.E.</th><th>Entered<br>independe<br>nt<br>Variable<sup>b</sup></th><th>Partia<br>l R<sup>2</sup></th><th>Regressio<br>n<br>coefficien<br>t</th><th>t-<br>Valu<br>e</th><th>P-<br>value</th></tr></thead><tbody><tr><td rowspan="3">pH<sub>24</sub></td><td rowspan="3">CONTROL+DF<br>D<br>(n = 52)</td><td rowspan="3">0.38***</td><td rowspan="3">0.14<br>9</td><td>DHRS7B</td><td>0.121</td><td>0.513</td><td>4.456</td><td>0.000</td></tr><tr><td>ESD</td><td>0.126</td><td>-0.453</td><td>-3.897</td><td>&lt;0.000<br>1</td></tr><tr><td>WFDC18</td><td>0.134</td><td>-0.382</td><td>-3.413</td><td>0.001</td></tr><tr><td rowspan="2">pH<sub>24</sub></td><td rowspan="2">CONTROL (n =<br>26)</td><td rowspan="2">0.55***</td><td rowspan="2">0.00<br>3</td><td>SERPINA3</td><td>0.384</td><td>0.802</td><td>5.593</td><td>&lt;0.000<br>1</td></tr><tr><td>GSTM4</td><td>0.168</td><td>-0.454</td><td>-3.162</td><td>0.004</td></tr><tr><td rowspan="2">pH<sub>24</sub></td><td rowspan="2">DFD (n = 26)</td><td rowspan="2">0.60***</td><td rowspan="2">0.01<br>8</td><td>MYL2</td><td>0.284</td><td>-0.570</td><td>-4.517</td><td>0.000</td></tr><tr><td>TRA</td><td>0.318</td><td>0.567</td><td>4.486</td><td>0.000</td></tr></tbody></table>

CON: CONTROL samples; DFD: DFD samples

<footnote><sup>a</sup> Significance of the models: *** P < 0.001.</footnote>

<footnote><sup>b</sup> Variables are shown in the order of their entrance in the prediction models.</footnote>