# Impact of UV pre-treatment on the Longissimus thoracis et lumborum muscle proteomes of dry-aged beef cuts: A characterisation within two sampling locations

Sara Álvarez, Carlos Álvarez, Anne Maria Mullen, Eileen O'Neill, Mohammed Gagaoua

► To cite this version:

Sara Álvarez, Carlos Álvarez, Anne Maria Mullen, Eileen O'Neill, Mohammed Gagaoua. Impact of UV pre-treatment on the Longissimus thoracis et lumborum muscle proteomes of dry-aged beef cuts: A characterisation within two sampling locations. Meat Science, 2025, 221, pp.109729. 10.1016/j.meatsci.2024.109729. hal-04832467

HAL Id: hal-04832467

https://hal.inrae.fr/hal-04832467v1

Submitted on 12 Dec 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Impact of UV pre-treatment on the *Longissimus thoracis et lumborum* muscle proteomes of dry-aged beef cuts: A characterisation within two sampling locations

Sara Álvarez a,b, Carlos Álvarez a,*, Anne Maria Mullen a, Eileen O'Neill b and Mohammed Gagaoua c,*

## Abstract

This research aimed to explore the changes in two sampling locations (internal and external) of the *Longissimus thoracis et lumborum* (LTL) beef muscle proteomes subjected to ultraviolet light before dry-aging. It further compared the biological processes and associated proteins at play at external locations of UV pre-treated and control dry-aged samples. Before dry-aging, proteins related to external stimuli were differentially abundant between both locations possibly due to the early *post-mortem* energy metabolism attempting to compensate for energy deficiencies and stress derived from slaughter and processing. The biochemical status of muscle during chilling and hanging of the carcasses and the impact of the UV pre-treatment may have also influenced the abundance of these proteins before dry-aging. Proteins associated to muscle structure, energy and fatty acids metabolism were differentially abundant between locations after 21 days of dry-aging. These dynamic changes in the meat proteome and related biological processes suggested that both evolved differently between the two sampling locations during dry-aging, and these may underlie the development of dry-aged beef properties. The proteome of the external locations sampled from UV pre-treated beef loins was compared to control counterparts during dry-aging. The results show that aging time appeared to outweigh the effect of UV since the differentially abundant proteins between both groups

<footnote>a Dept. of Food Quality and Sensory Science, Teagasc Food Research Centre Ashtown, Dublin, D15 DY05, Ireland</footnote>

<footnote>b School of Food and Nutritional Sciences, University College, Cork, Western Road, Cork T12 YN60, Ireland</footnote>

<footnote>c PEGASE, INRAE, Institut Agro, 35590 Saint-Gilles, France</footnote>

<footnote>* Corresponding authors:</footnote>

<footnote>Dr. Carlos Alvarez, carlos.alvarez@teagasc.ie;</footnote>

<footnote>Dr. Mohammed Gagaoua, mohammed.gagaoua@inrae.fr</footnote>
decreased as dry-aging progressed. These proteins were associated with mRNA stabilization, the matrisome, energy pathways and HSPs. Further research is warranted to better understand the role of these proteins in the production of dry-aged beef and their relation to the UV pre-treatment.

**Keywords:** Dry-aging; Ultraviolet; Shotgun proteomics; Enrichment analysis; SWATH-MS; Muscle proteins.
# 1. Introduction

Traditional dry-aging of beef is a *post-mortem* intervention where meat is stored without packaging under controlled refrigerated conditions for several weeks in order to improve certain quality traits such as tenderness and flavour (Álvarez et al., 2021; Terjung et al., 2021). Due to the absence of a protective packaging, the meat dehydrates throughout the aging period, resulting in relative high weight and trim losses (Ribeiro et al., 2024; Smith et al., 2008). Nonetheless, dehydration together with proteolysis, protein oxidation and lipid peroxidation contribute to the generation of the unique dry-aged beef flavour which has been extensively characterised in the recent literature (Li et al., 2021; Setyabrata et al., 2021; Zhang et al., 2022).

A limited number of studies have investigated the combined effect of *post-mortem* interventions together with dry-aging on quality attributes of the final dry-aged product and/or improvement of production efficiency (Hwang & Hong, 2020; Jose et al., 2020; Mungure et al., 2020). UV treatment is a non-thermal disinfection method (Tran & Farid, 2004) and it has been widely applied for equipment surfaces, water and air decontamination (Soro, Shokri, et al., 2023). In food production applications, UV light has attracted the interest due to the absence of toxic by-products, its lower impact on organoleptic properties, and reduced energy requirements in comparison to other interventions such as thermal pasteurization (Riganakos et al., 2017). Ultraviolet treatment may represent a promising *post-mortem* intervention that can be applied in the meat industry for enhancing safety with little or no significant impact on quality traits (Söbeli et al., 2021; Soro, Ekhlas, et al., 2023; Soro et al., 2022; Wang et al., 2023). However, UV can promote meat discoloration (Bertelsen & Skibsted, 1987), increase protein oxidation (Pattison et al., 2012; Zainudin et al., 2019), and impact protein structure by the generation of reactive species (free radicals and excited states species) and subsequent chain reactions (Lund et al., 2011). Changes in protein structure may include protein-crosslinking, protein fragmentation, and amino acid side chain modifications (Zhang et al., 2013). There are
a few studies which explored the use of UV treatment in the production of dry aged beef. For example, recent studies investigated the impact of UV treatment on the quality traits (Setyabrata, Xue, et al., 2022) and the generation and liberation of flavour precursors (Setyabrata, Vierck, et al., 2022) in dry-aged bovine loins (*M. longissimus lumborum*). Another study by Capouya and co-wrokers investigated the microbial communities on dry-aged beef subjected to ultraviolet light treatment in a commercial meat processing facility (Capouya et al., 2020). However, there are no studies investigating the consequences of UV treatment on the *post-mortem* muscle proteome of dry-aged beef.

The available literature on the changes occurring in the proteome during dry-aging of beef is scarce (Álvarez, Mullen, et al., 2023). Therefore, proteomics research is warranted to deepen our knowledge of the changes in the proteins and associated pathways during dry-aging, which may ultimately further our understanding of the impact of UV treatment on the molecular changes involved in the development of dry-aged products. In a recent study, we investigated the proteome of dry-aged beef produced under conventional conditions (Álvarez, Mullen, et al., 2023). The current study aimed for the first time to explore the influence of a UV pre-treatment on the changes in protein abundance and related biological processes between external and internal locations of beef loins during dry-aging. In addition, this work investigated the proteome evolution during aging within the external location of UV pre-treated loins in comparison to the external location of control beef loins during dry-aging.

## 2. Material and Methods

### 2.1. UV pre-treatment, dry-aging conditions and sampling

For this trial, a total of 12 striploins (*M. longissimus thoracis et lumborum*) were collected from the 10th rib of both sides of six cross breed bovine carcasses (Hereford x Limousin, 4 steers and 2 heifers with no significant gender effect on proteomic profiles in LTL muscle
(Gagaoua, Troy, et al., 2021)). Specification were as follows: conformation of U= to O=; fat score 2= to 4=; age: 24-36 months; and average weight of 180.6 kg ± 19.8 kg). Samples were supplied by a local slaughterhouse (Kepak, Ireland) at 2 days *post-mortem*. At 3 days *post-mortem*, the muscles were divided into four equal sections obtaining 8 sections per carcass. Each section was randomly allocated to one of the six treatment groups: treatment (control, UV) x dry-aging time (0, 21, and 28 days; which are equivalent to 3, 24, and 31 days *post-mortem*). Two of the eight sections were used for another experiment (not relevant to the present study). UV treatment (18.91 ± 0.99 W/m², 254 nm, and static conveyor belt) was performed before dry-aging using a UV Torpedo Conveyor (JenActUV, Hampshire, UK). Sections were exposed to UV for 10 minutes, and subsequently turned over to repeat the UV treatment for additional 10 minutes (approximately dose of 2.27 J/cm²). After the UV treatment, both control and UV-treated sections assigned to day 0 were immediately processed for quality and physicochemical evaluation. Sections assigned to day 21 and 28 were placed inside the DRY AGER DX 1000® (DRY AGER®, Germany) and dry-aged at a temperature of 2.0 °C, 75% relative humidity and air flow of 0.5-2.0 m/s. Racks were randomly rotated every week to avoid any location effect inside the dry-aging cabinet. At each sampling time point, the individual sections were divided into steaks of 1 inch thickness. At this time, muscle samples (approx. 2 g) were excised under aseptic conditions for further proteomic analysis from both internal and external locations (i.e. lean surface prior to dry-aging, and crust as aging progressed at not more than 1 cm into the core of the meat). Samples were stored at -80 °C for further analysis.

## 2.2. *Extraction and quantification of muscle proteins*

The frozen samples prepared in the previous section (150-200 mg), were used for protein extraction, including sarcoplasmic and myofibrillar proteins. This amount was homogenised in 3 mL of fresh extraction buffer consisting of 8.3 M urea, 2 M thiourea, 1% DL-Dithiothreitol
(DTT), 2% CHAPS, and Pharmalyte® 3-10 (Bouley et al., 2004). Samples were handled in wet-ice during the extraction process, and homogenisation was carried out using a T18 digital ULTRA-TURRAX® (IKA®, Staufen, Germany) at 20,000 rpm for 60 s (2 x 30 s). The resultant homogenates were kept in wet-ice for 30 minutes, and then, centrifuged for 30 min at 10000 x g and 4 °C. The supernatants were transferred into Eppendorf tubes and stored at -80 °C until protein quantification.

Quantification of protein concentrations in the protein extracts was performed, in duplicate, based on the Bradford method (Bradford, 1976) using the Bio-Rad protein assay kit (Bio-Rad Laboratories, Hercules, CA, USA). BSA (Thermo Scientific, Rockford, IL, USA) was employed as a standard, and protein concentrations were determined by measuring the absorbance at 595 nm using a spectrophotometer (UV-1700, Pharmaspec, SHIMADZU).

## 2.3. Preparation of proteins bands for shotgun proteomics using a one dimensional (1D) Sodium Dodecyl Sulphate-Polyacrylamide (SDS-PAGE) gel electrophoresis

Specific volumes of MilliQ water were added to all protein extract to ensure all of them had the same concentration, then, were mixed with an equal volume 1:1 (v/v) of Laemmli buffer (Sigma-Aldrich, St. Louis, MO, USA) giving as a result a final protein content of 40 µg in each well. The protein extracts were heated in a standard block heater (SBH130D/3, Bibby Scientific Limited, Staffordshire, UK) at 80 °C for 15 minutes. Samples were loaded in freshly prepared 12% separation and 4% stacking gels for 1D SDS-PAGE using a Mini-PROTEAN® Tetra Cell System (Bio-Rad, Hercules, CA, USA). Electrophoresis was performed for approximately 15 minutes at 4 Watts aiming to concentrate the proteins in one single and thin band for shotgun proteomics. Gels were washed several times with Milli-Q water, and were subsequently stained for few minutes with EZ Blue Gel staining reagent (Sigma-Aldrich, Saint Louis, USA).
Protein bands for shotgun proteomics were prepared as described by Lamri et al. (2023). Briefly, bands were excised from the gels using aseptic conditions and single-use scalpel, and subsequently placed into Eppendorf tubes for reduction, alkylation, distaining, and drying. The prepared bands were stored at -20 °C before SWATH-MS (Sequential Window Acquisition of all Theoretical Mass Spectra) analysis. The dried bands were digested with a modified porcine trypsin (Promega, Madison, WI, USA) at a concentration of 20 ng/µL in 20 mM ammonium bicarbonate at 37 °C for 16h, following the protocol of Chantada-Vázquez et al. (2021). The obtained peptides were extracted using 60% acetonitrile in 0.5% formic acid. The solutions were finally vacuum-concentrated in a SpeedVac and stored at -20 °C for downstream analysis as previously described (Álvarez, Mullen, et al., 2023).

## 2.4. Reference library and protein quantification

The reference library was generated using a shotgun data-dependent acquisition (DDA) approach (Álvarez, Mullen, et al., 2023). Using a data-independent acquisition method (IDA), also known as SWATH-MS method, the identification and relative quantification of peptides and proteins from each one of the samples generated were analysed. A volume of 4 µL of peptides from each sample were injected into a micro-LC system Ekspert nLC425 (Eksigen, Dublin, CA, USA) using an Eksigent C18 150 × 0.30 mm, 3 mm particle size and 120 Å pore size (Eksigent, Sciex). Water and acetonitrile, both containing 0.1% formic acid, were used as solvents A and B, respectively, at a flow of 5µL/min. Peptides were eluted using a gradient ranging from 5 to 95% mobile phase B for 40 minutes. The peptides coming out from the column were directly injected into a hybrid quadrupole-TOF mass spectrometer coupled with a Triple TOF® 6600 (Sciex, Redwood City, CA, USA) and analysed using the SWATH-MS acquisition method (Chantada-Vázquez et al., 2021).
PeakView v.2.2 (Sciex, Framingham, MA, USA) software was used for data extraction from the SWATH runs was handled by means of the SWATH Acquisition MicroApp v.2.0; based on the spectral library created. Up to 10 peptides/protein and 7 fragments/peptide were selected, only peptides with a False Discovery Rate (FDR) below 1% were used for protein quantification. Protein quantification was calculated by summing the peak areas of the corresponding peptides.

## 2.5. Statistical analysis

A total of 684 proteins were quantified and identified at an FDR of 1%, 2 unique peptides and 7 transitions for each peptide. Data processing and statistical analysis were performed using the R-based open platform MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/). Missing values were computed using the KNN (k-nearest neighbour) algorithm. Normalization of the data was done following a log₁₀ transformation and Pareto scaling approach. For pair-wise comparisons, volcano plots were used to identify the differentially abundant proteins (DAPs), with a 1.5-fold change (FC) and *P*-value of 0.05, between the two groups within aging time, treatment or sampling location. The data were further analysed by means of ANOVA to assess the interaction and main effects of UV pre-treatment, location, and dry-aging days on the proteome of dry-aged beef loins. Mean comparisons were made using Tukey's Test at the 0.05 level.

## 2.6. Bioinformatics analysis

Before bioinformatics analysis, the bovine Uniprot IDs of the identified proteins were converted into the orthologous human EntrezGeneID as previously described (Gagaoua, Terlouw, et al., 2021) using the UniprotKB database (https://www.uniprot.org/). Gene Ontology (GO) enrichment analysis was performed to investigate the most significant and enriched GO terms using Metascape® (https://metascape.org/), and thus, explore the functions
and biological processes related to the DAPs. Networks layouts based on GO enriched terms were generated with a minimum overlap of 2 gene entries, an enrichment factor > 1.5, and P-value < 0.05. The GO representative terms, based on their membership similarities with a similarity score > 0.3, were clustered together. Cytoscape v.3.9.1 was used to better investigate the enriched networks and identify the proteins contributing to each molecular function. Venn diagrams were generated to highlight the overlapping proteins among the DAPs lists.

## 3. Results

In the subsequent sections, first, we investigated the impact of the UV pre-treatment on the differentially abundant proteins between external and internal locations of dry-aged beef loins at three different aging times: 0, 21 and 28 (following a period of 3 days *post-mortem*). Secondly, we studied the effect of the UV pre-treatment on the muscle proteome at external locations of dry-aged beef loins at 0, 21, and 28 days of dry-aging in comparison to their control counterparts (i.e. non-UV pre-treated). In addition, we used all the identified proteins to explore the potential molecular and biological processes in which they are normally involved. Heatmaps were employed to better depict the abundancies of the top 30 proteins across the different comparisons.

### 3.1. DAPs between external and internal sampling locations of UV pre-treated beef loins during dry-aging

#### 3.1.1. DAPs of UV pre-treated beef loins at day 0 of dry-aging

Eighteen DAPs were identified to be differentially abundant between both sampling locations of UV pre-treated beef loins at day 0 of dry-aging (Figure 1A and Table S1 –for full protein names). In the external location seven proteins were found in higher abundance: TIMD4, SYNPO2, A2M, DYNLL1, LRP2, SERPINA3-7, and TNC. Eleven proteins were
more abundant in the internal location: PDHX, JSRP1, GLOD4, VASN, SOD1, HINT2, GP1BA, WFDC18, NID2, ARSA, and SYNCRIP. Based on the DAPs results, the pathways and cluster enrichment analysis resulted in 6 significant enriched GO terms (Figure 1B): reproductive structure development (GO: 0048608), negative regulation of response to external stimulus (GO:0032102), response to prostaglandin (GO:0034694), acute-phase response (GO:0006953), regulation of sequestering of calcium ion (GO:0051282), and negative regulation of transmembrane receptor protein serine/threonine kinase signalling pathway (GO:0090101). Table 1 further shows the proteins which are involved in the enrichment of these significant GO terms. The heatmap (Figure 1C) represents the abundancies of the top 30 proteins allowing a better visualisation of the individual differences and a clear separation of the two sampling locations at day 0 of dry-aging.

### 3.1.2. DAPs of UV pre-treated beef loins at day 21 of dry-aging

A total of 31 DAPs were identified to be differentially abundant between sampling locations of UV pre-treated beef loins at day 21 of dry-aging (Figure 1D and Table S2—for full protein names). Thirteen proteins were more abundant in the external location: IDH3G, LGALS4, SGCD, IPO5, FLNA, NBL1, ETFA, AKR1C3, UBE2U, CADM4, MYOC, CD14, and PPP1CB. Eighteen proteins were more abundant in the internal location: GINM1, JCHAIN, SIRPB1, CAVIN1, PDIA6, O11993, PSMA7, TUBA1A, NT5C1A, SERPINA7, GSTA1, DBI, PRNP, HGFAC, MADCAM1, ACTN3, SUCLG2, and GRM4. The bioinformatics enrichment analysis based on the DAPs revealed 11 significant enriched GO terms (Figure 1E): skeletal muscle adaptation (GO:0043501), energy derivation by oxidation of organic compounds (GO:0015980), fatty acid metabolic process (GO:0006631), response to metal ion (GO:0010038), positive regulation of intracellular protein transport (GO:0090316), positive regulation of substrate adhesion-dependent cell spreading (GO:1900026), antibacterial humoral response (GO:0019731), pattern recognition receptor signalling pathway
(GO:0002221), regulation of type II interferon production (GO:0032649), regulation of mononuclear cell migration (GO:0071675), and regulation of Ras protein signal transduction (GO:0046578). **Table 1** shows these GO terms and the proteins contributing to their enrichment. The heatmap depictures the abundancies of the top 30 proteins across the external and internal locations, and shows an evident separation of the locations after 21 days of dry-aging (**Figure 1F**).

### 3.1.3. DAPs of UV pre-treated beef loins at day 28 of dry-aging

A total of 25 DAPs were identified to be differentially abundant between external and internal sampling locations of UV pre-treated beef loins at day 28 of dry-aging (Figure 1G and Table S3—for full protein names). Four proteins were more abundant in the external location: CMYA5, PLIN4, LDB3, and SCUBE1. Twenty-one proteins were more abundant in the internal location: VTN, C2, PTGDS, CD74, EFNB1, BTNL2, BPIFA1, CDH2, C9, ANXA1, VSIG4, FCGR3A, PGA5, CD55, ALDH9A1, PTGFRN, APOH, CPN2, EDIP, PDHB, and KRT14. Based on the DAPs, the bioinformatics enrichment analysis revealed 12 significant enriched GO terms (Figure 1H): regulation of lymphocyte proliferation (GO:0050670), immune effector process (GO:0002252), positive regulation of wound healing (GO:0090303), regulation of interleukin-2 production (GO:0032663), complement activation, alternative pathway (GO:0006957), prostaglandin biosynthetic process (GO:0001516), regeneration (GO:0031099), myeloid leukocyte activation (GO:0002274), stem cell differentiation (GO:0048863), positive regulation of endocytosis (GO:0045807), purine-containing compound biosynthetic process (GO:0072522), and immune response-regulating cell surface receptor signalling pathway (GO:0002768). Table 1 shows these GO terms and the proteins contributing to their enrichment. The heatmap (Figure 1I) shows the abundancies of the top 30 proteins across the external and internal locations after 28 days of dry-aging.
The previous results have been gathered together and further compared as depicted in **Figure 2**. The **Figure 2A** compared the Top enriched GO terms and pathways related to the three DAPs lists obtained from the comparison of external and internal locations of UV pre-treated loins during dry-aging. The **Figure 2B** revealed the interconnectedness of the GO terms, which are represented as nodes in the functional enrichment network. The **Figure 2C** highlighted the overlap among the changing proteins with a focus on the number of common ones among the comparisons. The results clearly highlighted that no proteins were common among the comparisons, but the pathways can be shared as shown in **Figure 2A**.

In addition to the previous overlap analysis, the interaction and main effects of location and aging day were explored using the entire protein database from the UV-treated loins. A total of 22 proteins showed significant interactions (See Table S7 in supplementary material), and from these, 17 proteins showed an interaction effect without any overlap for the main effects (location, aging day) and 5 proteins showed an interaction effect with an overlap (1 with both effects, 3 with location effect, and 1 with aging day effect). Taken all together, four proteins (VTN, SYNPO2, C2 and C9) showing an interaction effect and overlap with one of the main effects may be highlighted, from which VTN (Vitronectin) deserves further attention as it was significant in the three cases (interaction, location, and aging day).

## 3.2. DAPs between the external sampling locations of UV pre-treated and control beef loins during dry-aging

### 3.2.1. DAPs in the external sampling locations at day 0 of dry-aging

A total of 24 DAPs were differentially abundant between the external locations of beef loins subjected to the UV pre-treatment and those of controls loins at 0 days of dry-aging (Figure 3A and Table S4 –for full protein names). Eight proteins were more abundant in the UV pre-treated samples: IGSF5, PGA5, CD14, OXCT1, HNRNPD, CAVIN1, SMYD1, and CRYL1.
Sixteen proteins were abundant in the control samples: ARSA, CD27, HEXB, AKR1B1, SYNCRIP, SERPINA3-7, LGALS3, UFD1, GRHPR, TXNDC17, SERPINA5, CCT2, LRRC15, SPARCL1, GLB1, and JSRP1. The pathways and cluster enrichment analysis based on the DAPs evidenced 7 significant enriched GO terms (Figure 3B): CRD-mediated mRNA stabilization (GO:0070934), response to alcohol (GO:0097305), small molecule catabolic process (GO:0044282), regulation of T cell apoptotic process (GO:0070232), monocarboxylic acid metabolic process (GO:0032787), negative regulation of hydrolase activity (GO:0051346), and neuromuscular process (GO:0050905). Table 2 contains the proteins contributing to the enrichment of these GO terms. The heatmap (Figure 3C) illustrates the abundancies of the top 30 proteins across the external locations of both UV pre-treated and control samples prior dry-aging, and shows the differences which determine the separation of both treatments.

### 3.2.2. DAPs in the external sampling locations at day 21 of dry-aging

A total of 20 DAPs were differentially abundant between the external locations of beef loins subjected to the UV pre-treatment and those of controls loins at 21 days of dry-aging (Figure 3D and Table S5—for full protein names). Eleven proteins were more abundant in the UV pre-treated samples: KIRREL1, NBL1, ANXA7, RAC1, FBLN5, UFD1, PDIA3, NID2, LYZF5, PITPNA, and ADAM12. Nine proteins were abundant in the control samples: SERPINC1, PGK1_1, GINM1, PTPRG, PGK1_2, CAVIN1, ANXA4, SUCLG2, and DBI. The bioinformatics enrichment analysis based on the DAPs revealed 7 significant enriched GO terms (Figure 3E): cell-matrix adhesion (GO:0007160), positive regulation of protein polymerization (GO:0032273), negative regulation of cytokine production (GO:0001818), response to wounding (GO:0009611), protein localization to extracellular region (GO:0071692), epithelial cell differentiation (GO:0030855), and negative regulation of protein modification process (GO:0031400). Table 2 contains these GO terms and the proteins
contributing to their enrichment. The heatmap (Figure 3F) shows the abundancies of the top 30 proteins facilitating a better visualisation of the differences between the external locations of the two groups after 21 days of dry-aging.

### 3.2.3. DAPs in the external sampling locations at day 28 of dry-aging

A total of 11 DAPs were differentially abundant after 28 days of dry-aging when the external locations of UV pre-treated and controls loins were compared (Figure 3G and Table S6—for full protein names). Four proteins were more abundant in the UV pre-treated samples: UBE2V1, LRRC15, PCYOX1, and SUCLG2. Seven proteins were abundant in the control samples: EFNB1, CCT8, FBLN2, DLAT, GARS1, DPP7, and CCT7. The bioinformatics enrichment analysis based on the DAPs revealed 4 significant enriched GO terms (Figure 3H): positive regulation of establishment of protein localization to telomere (GO:1904851), tricarboxylic acid cycle (GO:0006099), positive regulation of cell adhesion (GO:0045785), and protein catabolic process (GO:0030163). Table 2 contains the proteins contributing to these GO terms enrichment. The heatmap (Figure 3I) shows the abundancies of the top 30 proteins across the external locations of the two groups after 28 days of dry-aging.

The previous results have been collected and compared together as showed in **Figure 4**. The **Figure 4A** compared the Top enriched GO terms and pathways associated to the three DAPs lists obtained from the comparison of the external locations of both UV pre-treated and control loins during dry-aging. The **Figure 4B** shows the interconnectedness of the GO terms, which are represented as the nodes in a functional enrichment network. The **Figure 4C** represents the overlap analysis of the common proteins among the comparisons. Two proteins (CAVIN1 and UFD1) were in common between days 0 and 21, one protein (SUCLG2) between days 21 and 28, and one protein (LRRC15) between days 0 and 28. The interaction and main effects of treatment and ageing day was explored utilising the entire protein database obtained from the
external locations of dry-aged loins. The results showed that one protein (ARSA: Arylsulfatase A) showed an interaction effect without any overlap for the main effects.

**4. Discussion**

This study aimed to investigate the differences between the proteome at external and internal locations of UV pre-treated beef loins during dry-aging. It further investigated the effect of dry-aging time on the proteome at the external location of UV pre-treated beef loins in comparison to control counterparts. Differences in the abundance of proteins allowed exploration of the biological pathways at play and hence provide new insights on the biological mechanisms underlying the dry-aging process of beef loins after a UV pre-treatment. In the following sections, we discuss the major findings revealed in this study.

4.1. *Effect of sampling location on the proteome of UV pre-treated beef loins during dry-aging*

The comparison of external and internal locations evidenced differences in the proteome of UV pre-treated beef loins at each dry-aging time. Prior to dry-aging, proteins related to structure development and response to external stimuli were differentially abundant between sampling locations such as SOD1 (Superoxide dismutase [Cu-Zn]), A2M (Alpha-2-macroglobulin), TNC (Tenascin C), and WFDC18 (WAP four-disulfide core domain protein 18). We hypothesise that in early *post-mortem* muscle, with still potential to partly compensate for oxygen and energy deficiencies (England et al., 2018; Ouali et al., 2013), specific cellular response processes are activated in an attempt to protect the muscle cells from external stresses derived from the processing conditions (Álvarez, Mullen, et al., 2023; England et al., 2018). In addition, the management of the carcasses, such as the chilling and the hanging method, impacts on the dynamic biochemical status of muscle (Bjarnadóttir et al., 2011; Cai et al., 2023; Zhang et al., 2018). The UV treatment before dry-aging (at 3 days *post-mortem*) may have also promoted protein oxidation (Domínguez et al., 2021) and caused proteins structural changes
(protein fragmentation, protein crosslinking, modifications of the amino acid side chains)
(Davies, 2016; Lund et al., 2011; Pattison & Davies, 2006), thus, possibly contributing to the
proteome differences between sampling locations prior dry-aging. Among the proteins
identified, SOD1 is an enzyme that catalyses the conversion of superoxide to oxygen and
hydrogen peroxide, limiting the intracellular damage of the reactive oxygen species (ROS) (Lin
et al., 2013). The identification of SOD1 (higher in internal) supports that the early *post-mortem*
cells try to protect themselves from the chain reaction of ROS, which are potentially derived
from the impairment of mitochondrial energy production and subsequent accumulation of ROS
under hypoxic conditions (Longo et al., 2015; Ouali et al., 2006). In the present study, the
impact of external stimuli after slaughter may have also influenced the abundance of the
proteins A2M (higher in external), TNC (higher in external), and WFDC18 (higher in internal)
at both sampling locations. A2M is a serum glycoprotein which inhibits plasma proteinases by
trapping the proteinase inside the “bait region” of the A2M protein (Harwood et al., 2021).
A2M has been recently reported by our group to be positively correlated with beef flavour (Zhu
et al., 2023). TNC is an extracellular matrix glycoprotein which is synthesised as a response to
damage repair and inflammatory processes such as angiogenesis during wound healing (Wang
et al., 2022). Similarly, WFDC18 has been also associated to regulation of angiogenesis,
inflammation processes and infections in live tissues (Zhu et al., 2021). In a previous study,
the secretory protein WFDC2, related to host defence, was differentially abundant between the
external and internal locations of dry-aged beef loins (non UV pre-treated) after 21 of dry-
aging (Álvarez, Mullen, et al., 2023).

During dry-aging, surface dehydration occurs and leads to lower moisture content and water
activity at external locations compared to internal locations (Álvarez, Álvarez, et al., 2023). In
addition, oxidation of lipids and proteins increases during dry-aging, and microorganisms on
the surface contribute to proteolysis and lipolysis through the action of exogenous enzymes
(e.g. proteases, aminopeptidases, and lipases) (Zhang et al., 2022). These events together with the application of the UV pre-treatment may contribute to the differences in the proteome between external and internal locations after 21 days of dry-aging and the significant enrichment of GO terms related to skeletal muscle adaptation, energy and fatty acids metabolisms. The disintegration of key myofibrillar and cytoskeletal proteins by endogenous proteolytic systems during aging results in the destabilisation of the muscle structures and subsequent tenderisation (Ertbjerg, 2022; Gagaoua, Terlouw, et al., 2021). The identification of ACTN3 (Alpha-actinin-3; higher in internal) and MYOC (Myocilin; higher in external) at both locations may be related to the breakdown of the muscle structure. Indeed, ACTN3 is a structural component of the Z-line which anchors actin-containing thin filaments (Mills et al., 2001) and the degradation of the Z-disk with aging is associated with the tenderisation of beef (Huff Lonergan et al., 2010; Taylor et al., 1995). The destabilisation of the cytoskeleton structure may influence the concentration of MYOC across both locations, as this glycoprotein is involved in cell-matrix adhesion, cell migration and cytoskeleton organization (Cai et al., 2021).

Several energy metabolism related proteins were also differentially abundant between the external and internal locations at 21 days of dry-aging. It is interesting to note that recent proteomics studies have linked *post-mortem* muscle degradation to energy metabolism pathways due to the presence of glycolytic and oxidative enzymes (or their fragments) during aging (López-Pedrouso et al., 2021; Purslow et al., 2021; Sierra et al., 2021). As mentioned earlier, after animal bleeding, the oxygen levels decrease in the muscle cells and the energy metabolism pathway switches from aerobic to anaerobic glycolysis to respond to the energy demand, although tricarboxylic acid cycle (TCA) and oxidative phosphorylation pathways remain active to some extent early *post-mortem* (England et al., 2018; Gagaoua, Terlouw, et al., 2021; Jia et al., 2007; Ouali et al., 2013). We hypothesise that the level of activity of the
early *post-mortem* energy metabolism may influence the concentrations of ATP and its derived products as well as the abundance of energy metabolic enzymes across the external and internal locations in the final dry-aged beef. Nevertheless, this would depend on the type of production system and characteristics related to the animals, which warrant investigation. The abundance of enzymes related to energy metabolism at both sampling locations after 21 days of dry-aging may also be influenced by their release from their cell compartments and increasing extractability due the breakdown of muscle cells with aging (della Malva et al., 2022; Laville et al., 2009). The identification of enzymes such as EFTA (Electron transfer flavoprotein subunit alpha; higher in external), IDH3G (Isocitrate dehydrogenase [NAD] subunit; higher in external) and SUCLG2 (Succinate-CoA ligase subunit beta; higher in internal) may support the contribution of the *post-mortem* energy metabolism in the production of dry-aged beef. EFTA is one of the two subunits of an electron-transferring flavoprotein, a mitochondrial matrix enzyme that shuttles electron from various primary dehydrogenases to the main mitochondrial respiratory chain (Coppe et al., 2012). Both IDH3G and SUCLG2 are involved in the TCA. IDH3G is the gamma subunit of a mitochondrial isocitrate dehydrogenase that catalyses (on a NAD (+) - dependent manner) the oxidative decarboxylation of isocitrate to ketoglutarate and CO2 in the TCA (Soundar et al., 2006); whereas SUCLG2 is the beta subunit of the mitochondrial succinate-CoA synthase which catalyses the hydrolysis of succinyl-CoA and the production of GTP (Hasan et al., 2022). As stated above, the proteome of dry-aged beef may be influenced by how the early *post-mortem* metabolism develops. In the present study, this may be further evidenced by the identification of DBI (Acyl-CoA-binding protein; higher in the internal), which was differentially abundant across locations after 21 days of dry-aging. This protein is involved in the lipid metabolic and cellular signalling pathways by coordinating the intracellular flux and regulatory properties of the long-chain fatty acyl-CoA esters (Neess et al., 2015). The present study reveals that skeletal muscle, energy and fatty acids pathways
are key biological processes contributing to the development of dry-aged beef. Research
investigating the proteome of intermediate dry-aging days (e.g. 7 or 14 days) is warranted to
better understand the proteomics changes occurring between external and internal sampling
locations from day 0 to day 21 of dry-aging.

Unexpected results were observed after 28 days of dry-aging where proteins related to response
to external stimulus were more abundant in the internal location than in the external sampling
location after 28 days of dry-ageing (31 days *post-mortem*). More research is warranted to
explain these results and understand the factors contributing to the higher abundance of these
DAPs at the internal location. In addition, the internal location showed overall higher number
of DAPs in comparison to external location (21 and 4, respectively). Among the DAPs,
perforin-like protein complement component 9 (C9) is the major component of the membrane
attack complex (MAC), which is a multi-protein complex that forms pores in the membrane of
targets pathogenic cells (Dudkina et al., 2016). VSIG4 (V-set and immunoglobulin domain
containing 4) is a transmembrane receptor exclusively expressed on the surface of a subset of
tissue-resident macrophages, which plays a key role in eliminating pathogens and their by-
products (phagocytosis) from the blood circulation, and in regulating the activity of T cells and
the complement pathways (Liu et al., 2023). Other proteins involved in immune response such
as C2 (Complement component 2), CD74 (HLA Class II Histocompatibility Antigen γ Chain),
and CD55 (Complement decay-accelerating factor) were also more abundant in the inner
portions of dry-aged loins after 28 days of dry-aging.

Overall, the differential conditions to which external and internal locations are subjected before
and during dry-aging may influence the abundance of the proteins across both sampling
locations up to and beyond 21 days of dry-aging. This, in turn, may contribute to generate a
different metabolome across sampling locations in the final dry-aged product, as recently
evidenced by Bischof et al. (2023). These authors confirmed that the metabolome of dry-aged
beef is different between external and internal locations as aging progressed. Higher levels of IMP and inosine and lower levels of amino acids were observed in the surface compared to the inner part of 28 days dry-aged beef. Bischof and co-authors suggested that proteolysis and the energy metabolic pathways were partially inhibited in the external tissues due to the drying of the meat surface and the denaturation of enzymes during aging. We postulate that a multi-omics approach, integrating both metabolomics and proteomics would help to better characterise the interacting pathways and their synergic contributions to the development of dry-aged beef. This approach may also elucidate the revalorisation potential of the crust, which is traditionally removed from dry-aged beef and discarded before consumption, as a source of metabolites such as amino acids and bioactive peptides, and may have interest for its application as flavour enhancers, food ingredients, and/or animal feed as proposed by Park et al. (2018) and Xue et al. (2021).

## 4.2. Effect of dry-aging time on the proteome of the external location of UV pre-treated beef loins compared to control beef loins

UV application is considered to be a surface treatment due to its limited penetrative capacity (Koutchma, 2019). However, aging appeared to outweigh the effect of UV pre-treatment as the number of differentially abundant proteins between the external locations of UV pre-treated and control dry-aged loins decreased with dry-aging time. Nonetheless, several RNA-binding proteins related to the mRNA stabilization process were differently abundant between the external locations of the UV pre-treated and control samples prior dry-aging. HNRNPD (Heterogeneous nuclear ribonucleoprotein D; higher in UV) and SYNCRIP (Synaptotagmin binding cytoplasmic RNA interacting protein; higher in control) are heterogeneous nuclear ribonucleoproteins which form complex with heterogeneous nuclear RNAs (hnRNAs) to assist in their maturation into messenger RNAs (mRNAs), stabilise these mRNAs during transport and control their translation (Geuens et al., 2016). Thus, we speculate that the proteins
HNRNPD and SYNCRIP may regulate the synthesis of other proteins early *post-mortem* (and prior dry-aging) and be involved in the attempt of maintaining a normal cell activity and protect cell from apoptosis (Ouali et al., 2013). Further targeted studies on these proteins are needed to better confirm these results and understand the mechanisms by which they contribute to the development of dry-aged beef and their relation to the UV pre-treatment we applied.

As dry-aging progressed, proteins involved in cell-matrix adhesion were differently abundant between the external locations of both UV pre-treated and control samples after 21 days. The extracellular matrix is a cellular component that provides mechanical and chemical signals that coordinate cell functions and organisation, and the alteration of its composition and metabolism in live muscle can cause numerous pathologies (Naba et al., 2016). The extracellular matrix macromolecules, such as proteoglycans, are degraded during aging, disintegrating the structure of intramuscular connective tissues in meat (Listrat, Gagaoua, Andueza, et al., 2020; Listrat, Gagaoua, Normand, et al., 2020; Nishimura, 2015). Thus, the alteration of the proteins related to extracellular matrix during aging may contribute to the development of dry-aged beef products, and these changes may be further influenced by the UV exposure in this study. The disintegration of the extracellular matrix may influence the abundance of RAC1 (Ras-related C3 botulinum toxin substrate 1; higher in UV), FBLN5 (Fibulin-5; higher in UV), and NID2 (Nidogen 2; higher in UV) at the external locations of UV pre-treated and control samples after 21 days of dry-aging. From the proteins identified, the protein RAC1, member of the Rho family of GTPases, regulates various cellular events such as cell growth, cytoskeletal reorganization, and transcription (Bosco et al., 2008). FBLN5 is an extracellular matrix protein involved in the assembly of endothelial cells and therefore, elastic fibre formation (Jeong et al., 2010); whereas NID2 is a cell-adhesion protein that binds collagen I and IV and laminin playing a role in cell-extracellular matrix interactions (Kohfeldt et al., 1998).
In the present study, chaperone-related proteins were differentially abundant between the external locations of UV pre-treated and control beef loins after 28 days of dry-aging. Heat shock proteins (HSPs) are a family of chaperone proteins that are expressed as a response to stressful conditions, such as oxidation, and can hinder the action of caspases on target proteins, refold denature proteins, and induce cell apoptosis if the stress is high (Lomiwes et al., 2014). HSPs may be further expressed in muscles early *post-mortem* possibly to protect muscle cells integrity, assist in the refolding of denatured proteins, maintain mitochondrial membrane potential and thus, cell homeostasis (Gagaoua et al., 2017; Gagaoua, Terlouw, et al., 2021; Ouali et al., 2013). Their presence in dry-aged beef may be a consequence of their synthesis in the first hours after slaughter and their concentration later *post-mortem*. These observations may be supported by the presence of chaperone proteins CCT7 (T-complex protein 1 subunit eta; higher in the control) and CCT8 (T-complex protein 1 subunit theta; higher in the control) in this study. CCT7 and CCT8 are subunits of the chaperonin-containing T-complex (TRiC), which is an ATP-dependent chaperone complex that assists the folding of proteins involved in important cellular processes, such as, actin and tubulin (Chagoyen et al., 2014). In addition, the current study showed that various energy related proteins were differentially abundant between the external locations of UV pre-treated and control loins after 28 days of dry-aging. The appearance of proteins (and fragments) related to energy metabolism has been linked to the tenderisation of meat during aging (Gagaoua, Troy, et al., 2021; Laville et al., 2009; López-Pedrouso et al., 2021). In this study, the identification of energy enzymes such as DLAT (Acetyltransferase component of pyruvate dehydrogenase complex; higher in the control) and SUCLG2 (Succinate-CoA ligase subunit beta; higher in UV) may be linked to the progression of aging in dry-aged beef. DLAT is a subunit of the pyruvate dehydrogenase complex which converts pyruvate into acetyl-CoA, liking the glycolytic pathway to the tricarboxylic cycle (Goh et al., 2015). As mentioned earlier, SUCLG2 corresponds to the GTP-specific beta
subunit of succinyl-CoA synthase, which catalyses the reversible reaction of succinate and CoA to succinyl-CoA – the reverse direction occurs in the TCA cycle (Johnson et al., 1998; Miller et al., 2011). In the present study, proteins and pathways related to the extracellular matrix, cell survival, and energy metabolism were involved in determining the individual differences between the proteomes of the external locations of UV pre-treated and control samples. Further studies are warranted to better understand the molecular mechanisms by which the UV pre-treatment affects the proteome in the external locations, and if this treatment promotes the liberation of taste-related compounds and bioactive peptides in the crust.

**5. Conclusions**

Differential changes occur between the muscle proteomes when comparing different locations of UV pre-treated beef striploins during dry-aging. Prior dry-aging, the differential abundant proteins between sampling locations appeared to be mainly associated with structure development and response to external stimuli. The difference between both locations at day 0 of dry-aging may have been influenced by the activation of cell survival mechanisms in the first hours after slaughter to maintain physiological homeostasis and cope with stresses derived from the slaughter and the processing conditions. The biochemical status of the muscle during chilling and hanging of the carcasses, and the UV treatment applied to the meat before dry-aging may have further impacted the protein abundance at both external and internal locations before dry-aging. UV pre-treatment may have caused oxidation and structural changes of proteins. Thus, UV pre-treatment together with proteolysis, microbial activity and drying during dry-aging may have contributed to the differences in the proteome between external and internal locations after 21 and 28 days of dry-aging. Proteins related to skeletal muscle, energy, and fatty acids metabolisms were identified at 21 days of dry-aging, evidencing that these proteins and the associated pathways may play a role in the production of dry-aged beef.
The surface of the meat dehydrates during dry-aging forming a crust that is trimmed off before
consumption. This crust has been reported in the literature to have a different metabolome and
proteome than the internal tissues of dry-aged beef loins. Due to the increasing interest of re-
using this crust, the current work further compared the proteome of the external locations of
UV pre-treated dry-aged beef loins to control dry-aged beef loins. Overall, it was observed that
aging may have overweighed the impact of the UV pre-treatment as the number of
differentially abundant proteins on the external locations between UV pre-treated and control
dry-aged loins decreased with dry-aging time. Nonetheless, proteins related to mRNA
stabilization, the matrisome, energy pathways and HSPs were differentially abundant between
the external locations of UV pre-treated and controls loins.

Our current study was specifically designed to focus on the proteomics dimension to address
the knowledge gap in this area. However, integrating proteomics data with basic biochemical
evaluations could provide a comprehensive perspective in future studies. Moreover, future
studies combining multiple omics methods, likely metabolomics, proteomics, peptidomics and
volatiles through volatolomics along of sensory analyses are warranted to understand the
molecular mechanisms by which these proteins contribute to the dry-aged beef development
and their relation to the treatment applied. Insights gained may further support the
revalorisation of the crust.

Acknowledgements

This work was funded as part of the Strategic Partnership Agreement co-funded by INIA
(FPTA-373) and the Walsh Scholarship program (2019041) supported by Teagasc project
number 0821. The authors acknowledge Dr. Susana B. Bravo for the Sequential Window
Acquisition of all Theoretical Mass Spectra (SWATH-MS) analysis.

**Declaration of interest**
No potential conflict of interest was reported by the authors.

## References

Álvarez, S., Álvarez, C., M. Hamill, R., O'Neill, E., & Mullen, A. M. (2023). Influence of meat sample geometry on dehydration dynamics during dry-aging of beef. *Meat Science, 202*, 109216.
https://doi.org/10.1016/j.meatsci.2023.109216

Álvarez, S., Mullen, A. M., Álvarez, C., Hamill, R. M., O'Neill, E., & Gagaoua, M. (2023). Impact of sampling location and aging on the Longissimus thoracis et lumborum muscle proteome of dry-aged beef. *Meat Science, 205*, 109315. https://doi.org/10.1016/j.meatsci.2023.109315

Álvarez, S., Mullen, A. M., Hamill, R., O'Neill, E., & Álvarez, C. (2021). Chapter Three - Dry-aging of beef as a tool to improve meat quality. Impact of processing conditions on the technical and organoleptic meat properties. In F. Toldrá (Ed.), *Advances in Food and Nutrition Research* (Vol. 95, pp. 97-130). Academic Press. https://doi.org/10.1016/bs.afnr.2020.10.001

Bertelsen, G., & Skibsted, L. H. (1987). Photooxidation of oxymyoglobin. Wavelength dependence of quantum yields in relation to light discoloration of meat. *Meat Science, 19*(4), 243-251.
https://doi.org/10.1016/0309-1740(87)90070-2

Bischof, G., Witte, F., Sieksmeyer, T., Januschweski, E., Terjung, N., Hertel, C., Heinz, V., Juadjur, A., & Gibis, M. (2023). Metabolic and microbial analyses of the surface and inner part of wet-aged and dry-aged beef. *Journal of Food Science, n/a (n/a)*. https://doi.org/10.1111/1750-3841.16761

Bjarnadóttir, S. G., Hollung, K., Høy, M., & Veiseth-Kent, E. (2011). Proteome changes in the insoluble protein fraction of bovine Longissimus dorsi muscle as a result of low-voltage electrical stimulation. *Meat Science, 89*(2), 143-149. https://doi.org/10.1016/j.meatsci.2011.04.002

Bosco, E. E., Mulloy, J. C., & Zheng, Y. (2008). Rac1 GTPase: A "Rac" of All Trades. *Cellular and Molecular Life Sciences, 66*(3), 370. https://doi.org/10.1007/s00018-008-8552-x

Bouley, J., Chambon, C., & Picard, B. (2004). Mapping of bovine skeletal muscle proteins using two-dimensional gel electrophoresis and mass spectrometry. *PROTEOMICS, 4*(6), 1811-1824.
https://doi.org/10.1002/pmic.200300688

Bradford, M. M. (1976). A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. *Analytical Biochemistry, 72*(1), 248-254. https://doi.org/10.1016/0003-2697(76)90527-3

Cai, W., Wang, Z., Wen, K., Li, H., Qian, C., Che, L., Zhang, H., Zhang, Y., Li, J., & Li, H. (2023). Proteome changes in Neck-arm restraint and Achilles tendon suspensions during early post-mortem aging in cattle. *Livestock Science, 274*, 105288.
https://doi.org/10.1016/j.livsci.2023.105288

Cai, W., Wen, K., Che, L., Zhang, H., Zhang, Y., Li, J., & Li, H. (2021). Effects of Neck-Arm Restraint Suspension of Beef Carcasses on Meat Quality and Proteome of Different Muscles During Post-mortem Aging [Original Research]. *Frontiers in Nutrition, 8*.
https://doi.org/10.3389/fnut.2021.774529

Capouya, R., Mitchell, T., Clark, D. I., Clark, D. L., Bass, P., Capouya, R. D., & Bass, P. D. (2020). A survey of microbial communities on dry-aged beef in commercial meat processing facilities. *Meat and Muscle Biology, 4*(1). https://doi.org/10.22175/mmb.10373

Chagoyen, M., Carrascosa, J. L., Pazos, F., & Valpuesta, J. M. (2014). Molecular determinants of the ATP hydrolysis asymmetry of the CCT chaperonin complex. *Proteins: Structure, Function, and Bioinformatics, 82*(5), 703-707. https://doi.org/10.1002/prot.24510

Chantada-Vázquez, M. d. P., García Vence, M., Serna, A., Núñez, C., & Bravo, S. B. (2021). SWATH-MS Protocols in Human Diseases. In M. Carrera & J. Mateos (Eds.), *Shotgun Proteomics: Methods and Protocols* (pp. 105-141). Springer US. https://doi.org/10.1007/978-1-0716-1178-4_7
Coppe, A., Agostini, C., Marino, I. A. M., Zane, L., Bargelloni, L., Bortoluzzi, S., & Patarnello, T. (2012). Genome Evolution in the Cold: Antarctic Icefish Muscle Transcriptome Reveals Selective Duplications Increasing Mitochondrial Function. *Genome Biology and Evolution*, 5(1), 45-60.
https://doi.org/10.1093/gbe/evs108

Davies, Michael J. (2016). Protein oxidation and peroxidation. *Biochemical Journal*, 473(7), 805-825.
https://doi.org/10.1042/bj20151227

della Malva, A., Gagaoua, M., Santillo, A., De Palo, P., Sevi, A., & Albenzio, M. (2022). First insights about the underlying mechanisms of Martina Franca donkey meat tenderization during aging: A proteomic approach. *Meat Science*, 193, 108925.
https://doi.org/10.1016/j.meatsci.2022.108925

Domínguez, R., Pateiro, M., Munekata, P. E. S., Zhang, W., Garcia-Oliveira, P., Carpena, M., Prieto, M. A., Bohrer, B., & Lorenzo, J. M. (2021). Protein Oxidation in Muscle Foods: A Comprehensive Review. *Antioxidants (Basel)*, 11(1). https://doi.org/10.3390/antiox11010060

Dudkina, N. V., Spicer, B. A., Reboul, C. F., Conroy, P. J., Lukoyanova, N., Elmlund, H., Law, R. H. P., Ekkel, S. M., Kondos, S. C., Goode, R. J. A., Ramm, G., Whisstock, J. C., Saibil, H. R., & Dunstone, M. A. (2016). Structure of the poly-C9 component of the complement membrane attack complex. *Nature Communications*, 7(1), 10588.
https://doi.org/10.1038/ncomms10588

England, E. M., Matarneh, S. K., Mitacek, R. M., Abraham, A., Ramanathan, R., Wicks, J. C., Shi, H., Scheffler, T. L., Oliver, E. M., Helm, E. T., & Gerrard, D. E. (2018). Presence of oxygen and mitochondria in skeletal muscle early postmortem. *Meat Science*, 139, 97-106.
https://doi.org/10.1016/j.meatsci.2017.12.008

Ertbjerg, P. (2022). Chapter 5 - Current understanding on the role of proteolysis on meat quality. In P. Purslow (Ed.), *New Aspects of Meat Quality (Second Edition)* (pp. 95-114). Woodhead Publishing. https://doi.org/10.1016/B978-0-323-85879-3.00022-2

Gagaoua, M., Monteils, V., Couvreur, S., & Picard, B. (2017). Identification of Biomarkers Associated with the Rearing Practices, Carcass Characteristics, and Beef Quality: An Integrative Approach. *Journal of Agricultural and Food Chemistry*, 65(37), 8264-8278.
https://doi.org/10.1021/acs.jafc.7b03239

Gagaoua, M., Terlouw, E. M. C., Mullen, A. M., Franco, D., Warner, R. D., Lorenzo, J. M., Purslow, P. P., Gerrard, D., Hopkins, D. L., Troy, D., & Picard, B. (2021). Molecular signatures of beef tenderness: Underlying mechanisms based on integromics of protein biomarkers from multi-platform proteomics studies. *Meat Science*, 172, 108311.
https://doi.org/10.1016/j.meatsci.2020.108311

Gagaoua, M., Troy, D., & Mullen, A. M. (2021). The Extent and Rate of the Appearance of the Major 110 and 30 kDa Proteolytic Fragments during Post-Mortem Aging of Beef Depend on the Glycolysing Rate of the Muscle and Aging Time: An LC-MS/MS Approach to Decipher Their Proteome and Associated Pathways. *Journal of Agricultural and Food Chemistry*, 69(1), 602-614.
https://doi.org/10.1021/acs.jafc.0c06485

Geuens, T., Bouhy, D., & Timmerman, V. (2016). The hnRNP family: insights into their role in health and disease. *Human Genetics*, 135(8), 851-867. https://doi.org/10.1007/s00439-016-1683-5

Goh, W. Q., Ow, G. S., Kuznetsov, V. A., Chong, S., & Lim, Y. P. (2015). DLAT subunit of the pyruvate dehydrogenase complex is upregulated in gastric cancer-implications in cancer therapy. *Am J Transl Res*, 7(6), 1140-1151.

Harwood, S. L., Lyngsø, J., Zarantonello, A., Kjøge, K., Nielsen, P. K., Andersen, G. R., Pedersen, J. S., & Enghild, J. J. (2021). Structural Investigations of Human A2M Identify a Hollow Native Conformation That Underlies Its Distinctive Protease-Trapping Mechanism. *Molecular & Cellular Proteomics*, 20, 100090. https://doi.org/10.1016/j.mcpro.2021.100090

Hasan, M. M., Rashid, M.-u. P. S., Surendranath, & Perreault, H. P., Jitendra Rodas-González, Argenis (2022). Tandem Mass Tag Labeling-Based Analysis to Characterize Muscle-Specific Proteome Changes During Postmortem Aging of Bison Longissimus Lumborum and Psoas Major
Muscles. *Meat and Muscle Biology*, 6(1).

https://www.iastatedigitalpress.com/mmb/article/id/13055/

Huff Lonergan, E., Zhang, W., & Lonergan, S. M. (2010). Biochemistry of postmortem muscle — Lessons on mechanisms of meat tenderization. *Meat Science*, 86(1), 184-195.

https://doi.org/10.1016/j.meatsci.2010.05.004

Hwang, S.-I., & Hong, G.-P. (2020). Effects of high pressure in combination with the type of aging on the eating quality and biochemical changes in pork loin. *Meat Science*, 162, 108028.

https://doi.org/10.1016/j.meatsci.2019.108028

Jeong, M. S., Kang, C. S., Han, Y. S., & Bang, I. S. (2010). Expression, purification, and characterization of recombinant fibulin-5 in a prokaryote expression system. *The Journal of Microbiology*,

48(5), 695-700. https://doi.org/10.1007/s12275-010-0320-6

Jia, X., Ekman, M., Grove, H., Færgestad, E. M., Aass, L., Hildrum, K. I., & Hollung, K. (2007). Proteome Changes in Bovine Longissimus Thoracis Muscle During the Early Postmortem Storage Period. *Journal of Proteome Research*, 6(7), 2720-2731.

https://doi.org/10.1021/pr070173o

Johnson, J. D., Mehus, J. G., Tews, K., Milavetz, B. I., & Lambeth, D. O. (1998). Genetic Evidence for the Expression of ATP- and GTP-specific Succinyl-CoA Synthetases in Multicellular Eucaryotes
*Journal of Biological Chemistry*, 273(42), 27580-27586.

https://doi.org/10.1074/jbc.273.42.27580

Jose, C. G., Jacob, R. H., & Gardner, G. E. (2020). Alternative cutting methods and dry aging reduce the shear force of hot boned beef striploin in Bos indicus cattle. *Meat Science*, 163, 108036.

https://doi.org/10.1016/j.meatsci.2019.108036

Kohfeldt, E., Sasaki, T., Göhring, W., & Timpl, R. (1998). Nidogen-2: a new basement membrane protein with diverse binding properties11
*Edited by I. B. Holland. Journal of Molecular Biology*, 282(1), 99-109.

https://doi.org/10.1006/jmbi.1998.2004

Koutchma, T. (2019). *Ultraviolet Light in Food Technology: Principles and Applications* (2nd ed.). CRC Press.

https://doi.org/10.1201/9780429244414

Lamri, M., della Malva, A., Djenane, D., López-Pedrouso, M., Franco, D., Albenzio, M., Lorenzo, J. M., & Gagaoua, M. (2023). Towards the discovery of goat meat quality biomarkers using label-free proteomics. *Journal of Proteomics*, 278, 104868.

https://doi.org/10.1016/j.jprot.2023.104868

Laville, E., Sayd, T., Morzel, M., Blinet, S., Chambon, C., Lepetit, J., Renand, G., & Hocquette, J. F. (2009). Proteome changes during meat aging in tough and tender beef suggest the importance of apoptosis and protein solubility for beef aging and tenderization. *Journal of Agricultural and Food Chemistry*, 57(22), 10755-10764.

https://doi.org/10.1021/jf901949r

Li, Z., Ha, M., Frank, D., McGilchrist, P., & Warner, R. D. (2021). Volatile Profile of Dry and Wet Aged Beef Loin and Its Relationship with Consumer Flavour Liking. *Foods*, 10(12), 3113.

https://www.mdpi.com/2304-8158/10/12/3113

Lin, Z.-F., Xu, H.-B., Wang, J.-Y., Lin, Q., Ruan, Z., Liu, F.-B., Jin, W., Huang, H.-H., & Chen, X. (2013). SIRT5 desuccinylates and activates SOD1 to eliminate ROS. *Biochemical and Biophysical Research Communications*, 441(1), 191-195.

https://doi.org/10.1016/j.bbrc.2013.10.033

Listrat, A., Gagaoua, M., Andueza, D., Gruffat, D., Normand, J., Mairesse, G., Picard, B., & Hocquette, J.-F. (2020). What are the drivers of beef sensory quality using metadata of intramuscular connective tissue, fatty acids and muscle fiber characteristics? *Livestock Science*, 240,

104209. https://doi.org/10.1016/j.livsci.2020.104209

Listrat, A., Gagaoua, M., Normand, J., Gruffat, D., Andueza, D., Mairesse, G., Mourot, B.-P., Chesneau, G., Gobert, C., & Picard, B. (2020). Contribution of connective tissue components, muscle fibres and marbling to beef tenderness variability in longissimus thoracis, rectus abdominis, semimembranosus and semitendinosus muscles. *Journal of the Science of Food and Agriculture*, 100(6), 2502-2511.

https://doi.org/10.1002/jsfa.10275
Liu, B., Cheng, L., Gao, H., Zhang, J., Dong, Y., Gao, W., Yuan, S., Gong, T., & Huang, W. (2023). The biology of VSIG4: Implications for the treatment of immune-mediated inflammatory diseases and cancer. *Cancer Letters*, 553, 215996. https://doi.org/10.1016/j.canlet.2022.215996

Lomiwes, D., Farouk, M. M., Wiklund, E., & Young, O. A. (2014). Small heat shock proteins and their role in meat tenderness: A review. *Meat Science*, 96(1), 26-40.
https://doi.org/10.1016/j.meatsci.2013.06.008

Longo, V., Lana, A., Bottero, M. T., & Zolla, L. (2015). Apoptosis in muscle-to-meat aging process: The omic witness. *Journal of Proteomics*, 125, 29-40. https://doi.org/10.1016/j.jprot.2015.04.023

López-Pedrouso, M., Lorenzo, J. M., Di Stasio, L., Brugiapaglia, A., & Franco, D. (2021). Quantitative proteomic analysis of beef tenderness of Piemontese young bulls by SWATH-MS. *Food Chemistry*, 356, 129711. https://doi.org/10.1016/j.foodchem.2021.129711

Lund, M. N., Heinonen, M., Baron, C. P., & Estévez, M. (2011). Protein oxidation in muscle foods: A review. *Molecular Nutrition & Food Research*, 55(1), 83-95.
https://doi.org/10.1002/mnfr.201000453

Miller, C., Wang, L., Ostergaard, E., Dan, P., & Saada, A. (2011). The interplay between SUCLA2, SUCLG2, and mitochondrial DNA depletion. *Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease*, 1812(5), 625-629. https://doi.org/10.1016/j.bbadis.2011.01.013

Mills, M., Yang, N., Weinberger, R., Vander Woude, D. L., Beggs, A. H., Easteal, S., & North, K. (2001). Differential expression of the actin-binding proteins, α-actinin-2 and -3, in different species: implications for the evolution of functional redundancy. *Human Molecular Genetics*, 10(13), 1335-1346. https://doi.org/10.1093/hmg/10.13.1335

Mungure, T. E., Farouk, M. M., Birch, E. J., Carne, A., Staincliffe, M., Stewart, I., & Bekhit, A. E.-D. A. (2020). Effect of PEF treatment on meat quality attributes, ultrastructure and metabolite profiles of wet and dry aged venison Longissimus dorsi muscle. *Innovative Food Science & Emerging Technologies*, 65, 102457. https://doi.org/10.1016/j.ifset.2020.102457

Naba, A., Clauser, K. R., Ding, H., Whittaker, C. A., Carr, S. A., & Hynes, R. O. (2016). The extracellular matrix: Tools and insights for the “omics” era. *Matrix Biology*, 49, 10-24.
https://doi.org/10.1016/j.matbio.2015.06.003

Neess, D., Bek, S., Engelsby, H., Gallego, S. F., & Færgeman, N. J. (2015). Long-chain acyl-CoA esters in metabolism and signaling: Role of acyl-CoA binding proteins. *Progress in Lipid Research*, 59, 1-25. https://doi.org/10.1016/j.plipres.2015.04.001

Nishimura, T. (2015). Role of extracellular matrix in development of skeletal muscle and postmortem aging of meat. *Meat Science*, 109, 48-55. https://doi.org/10.1016/j.meatsci.2015.05.015

Ouali, A., Gagaoua, M., Boudida, Y., Becila, S., Boudjellal, A., Herrera-Mendez, C. H., & Sentandreu, M. A. (2013). Biomarkers of meat tenderness: Present knowledge and perspectives in regards to our current understanding of the mechanisms involved. *Meat Science*, 95(4), 854-870. https://doi.org/10.1016/j.meatsci.2013.05.010

Ouali, A., Herrera-Mendez, C. H., Coulis, G., Becila, S., Boudjellal, A., Aubry, L., & Sentandreu, M. A. (2006). Revisiting the conversion of muscle into meat and the underlying mechanisms. *Meat Science*, 74(1), 44-58. https://doi.org/10.1016/j.meatsci.2006.05.010

Park, B., Yong, H. I., Choe, J., & Jo, C. (2018). Utilization of the Crust from Dry-aged Beef to Enhance Flavor of Beef Patties. *Korean journal for food science of animal resources*, 38(5), 1019-1028.
https://doi.org/10.5851/kosfa.2018.e35

Pattison, D. I., & Davies, M. J. (2006). Actions of ultraviolet light on cellular structures. *Cancer: cell structures, carcinogens and genomic instability*, 131-157.

Pattison, D. I., Rahmanto, A. S., & Davies, M. J. (2012). Photo-oxidation of proteins. *Photochemical & Photobiological Sciences*, 11(1), 38-53. https://doi.org/10.1039/c1pp05164d

Purslow, P. P., Gagaoua, M., & Warner, R. D. (2021). Insights on meat quality from combining traditional studies and proteomics. *Meat Science*, 174, 108423.
https://doi.org/10.1016/j.meatsci.2020.108423
Ribeiro, F. A., Lau, S. K., Furbeck, R. A., Herrera, N. J., Henriott, M. L., Bland, N. A., Fernando, S. C., Subbiah, J., Pflanzer, S. B., Dinh, T. T., Miller, R. K., Sullivan, G. A., & Calkins, C. R. (2024). Effects of relative humidity on dry-aged beef quality. *Meat Science*, 213, 109498.
https://doi.org/10.1016/j.meatsci.2024.109498

Riganakos, K. A., Karabagias, I. K., Gertzou, I., & Stahl, M. (2017). Comparison of UV-C and thermal treatments for the preservation of carrot juice. *Innovative Food Science & Emerging Technologies*, 42, 165-172. https://doi.org/10.1016/j.ifset.2017.06.015

Setyabrata, D., Cooper, B. R., Sobreira, T. J. P., Legako, J. F., Martini, S., & Kim, Y. H. B. (2021). Elucidating mechanisms involved in flavor generation of dry-aged beef loins using metabolomics approach. *Food Research International*, 139, 109969.
https://doi.org/10.1016/j.foodres.2020.109969

Setyabrata, D., Vierck, K., Sheets, T. R., Legako, J. F., Cooper, B. R., Johnson, T. A., & Kim, Y. H. B. (2022). Characterizing the Flavor Precursors and Liberation Mechanisms of Various Dry-Aging Methods in Cull Beef Loins Using Metabolomics and Microbiome Approaches. *Metabolites*, 12(6), 472. https://www.mdpi.com/2218-1989/12/6/472

Setyabrata, D., Xue, S., Cramer, T., Vierck, K., Legako, J., & Kim, Y. (2022). Impact of Various Dry-Aging Methods on Meat Quality and Palatability Attributes of Beef Loins (M. longissimus lumborum) from Cull Cow. *Meat and Muscle Biology*, 6(1), 1-15.
https://doi.org/10.22175/mmb.13025

Sierra, V., González-Blanco, L., Diñeiro, Y., Díaz, F., García-Espina, M. J., Coto-Montes, A., Gagaoua, M., & Oliván, M. (2021). New insights on the impact of cattle handling on post-mortem myofibrillar muscle proteome and meat tenderization. *Foods*, 10(12), 3115.
https://doi.org/10.3390/foods10123115

Smith, R., Nicholson, K., Nicholson, J., Harris, K., Miller, R., Griffin, D., & Savell, J. (2008). Dry versus wet aging of beef: Retail cutting yields and consumer palatability evaluations of steaks from US Choice and US Select short loins. *Meat Science*, 79(4), 631-639.
https://doi.org/10.1016/j.meatsci.2007.10.028

Söbeli, C., Uyarcan, M., & Kayaardi, S. (2021). Pulsed UV-C radiation of beef loin steaks: Effects on microbial inactivation, quality attributes and volatile compounds. *Innovative Food Science & Emerging Technologies*, 67, 102558. https://doi.org/10.1016/j.ifset.2020.102558

Soro, A. B., Ekhlas, D., Shokri, S., Yem, M. M., Li, R. C., Barroug, S., Hannon, S., Whyte, P., Bolton, D. J., Burgess, C. M., Bourke, P., & Tiwari, B. K. (2023). The efficiency of UV light-emitting diodes (UV-LED) in decontaminating Campylobacter and Salmonella and natural microbiota in chicken breast, compared to a UV pilot-plant scale device. *Food Microbiology*, 116, 104365.
https://doi.org/10.1016/j.fm.2023.104365

Soro, A. B., Harrison, S. M., Whyte, P., Bolton, D. J., & Tiwari, B. K. (2022). Impact of ultraviolet light and cold plasma on fatty acid profile of raw chicken and pork meat. *Journal of Food Composition and Analysis*, 114, 104872. https://doi.org/10.1016/j.jfca.2022.104872

Soro, A. B., Shokri, S., Nicolau-Lapeña, I., Ekhlas, D., Burgess, C. M., Whyte, P., Bolton, D. J., Bourke, P., & Tiwari, B. K. (2023). Current challenges in the application of the UV-LED technology for food decontamination. *Trends in Food Science & Technology*, 131, 264-276.
https://doi.org/10.1016/j.tifs.2022.12.003

Soundar, S., O'Hagan, M., Fomulu, K. S., & Colman, R. F. (2006). Identification of Mn2+-binding Aspartates from α, β, and γ Subunits of Human NAD-dependent Isocitrate Dehydrogenase*. *Journal of Biological Chemistry*, 281(30), 21073-21081.
https://doi.org/10.1074/jbc.M602956200

Taylor, R. G., Geesink, G. H., Thompson, V. F., Koohmaraie, M., & Goll, D. E. (1995). Is Z-disk degradation responsible for postmortem tenderization? *Journal of Animal Science*, 73(5), 1351-1367. https://doi.org/10.2527/1995.7351351x
Terjung, N., Witte, F., & Heinz, V. (2021). The dry aged beef paradox: Why dry aging is sometimes not better than wet aging. *Meat Science*, **172**, 108355.
https://doi.org/10.1016/j.meatsci.2020.108355

Tran, M. T. T., & Farid, M. (2004). Ultraviolet treatment of orange juice. *Innovative Food Science & Emerging Technologies*, **5**(4), 495-502. https://doi.org/10.1016/j.ifset.2004.08.002

Wang, J., Chen, J., Sun, Y., He, J., Zhou, C., Xia, Q., Dang, Y., Pan, D., & Du, L. (2023). Ultraviolet-radiation technology for preservation of meat and meat products: Recent advances and future trends. *Food Control*, **148**, 109684. https://doi.org/10.1016/j.foodcont.2023.109684

Wang, Y., Wang, G., & Liu, H. (2022). Tenascin-C: A Key Regulator in Angiogenesis during Wound Healing. *Biomolecules*, **12**(11). https://doi.org/10.3390/biom12111689

Xue, S., Setyabrata, D., Bonham, C. C., & Kim, Y. H. B. (2021). Evaluation of functional and chemical properties of crust from dry-aged beef loins as a novel food ingredient. *Meat Science*, **173**, 108403. https://doi.org/10.1016/j.meatsci.2020.108403

Zainudin, M. A. M., Poojary, M. M., Jongberg, S., & Lund, M. N. (2019). Light exposure accelerates oxidative protein polymerization in beef stored in high oxygen atmosphere. *Food Chemistry*, **299**, 125132. https://doi.org/10.1016/j.foodchem.2019.125132

Zhang, R., Yoo, M. J. Y., Ross, A. B., & Farouk, M. M. (2022). Mechanisms and strategies to tailor dry-aged meat flavour. *Trends in Food Science & Technology*, **119**, 400-411.
https://doi.org/10.1016/j.tifs.2021.12.023

Zhang, W., Xiao, S., & Ahn, D. U. (2013). Protein Oxidation: Basic Principles and Implications for Meat Quality. *Critical Reviews in Food Science and Nutrition*, **53**(11), 1191-1201.
https://doi.org/10.1080/10408398.2011.577540

Zhang, Y.-m., Zhang, X.-z., Wang, T.-t., Hopkins, D. L., Mao, Y.-w., Liang, R.-r., Yang, G.-f., Luo, X., & Zhu, L.-x. (2018). Implications of step-chilling on meat color investigated using proteome analysis of the sarcoplasmic protein fraction of beef longissimus lumborum muscle. *Journal of Integrative Agriculture*, **17**(9), 2118-2125. https://doi.org/10.1016/S2095-3119(18)62028-3

Zhu, S., Ye, L., Bennett, S., Xu, H., He, D., & Xu, J. (2021). Molecular structure, gene expression and functional role of WFDC1 in angiogenesis and cancer. *Cell Biochemistry and Function*, **39**(5), 588-595. https://doi.org/10.1002/cbf.3624

Zhu, Y., Hamill, R. M., Mullen, A. M., Kelly, A. L., & Gagaoua, M. (2023). Molecular mechanisms contributing to the development of beef sensory texture and flavour traits and related biomarkers: Insights from early post-mortem muscle using label-free proteomics. *Journal of Proteomics*, **286**, 104953. https://doi.org/10.1016/j.jprot.2023.104953
<figure><img src="image_2.png" /><figcaption>(A)</figcaption></figure>

External vs Internal, d0

<figure><img src="image_3.png" /><figcaption>(C)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(D)</figcaption></figure>

<figure><img src="image_5.png" /></figure>

External vs Internal, d21

<figure><img src="image_6.png" /><figcaption>(F)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(G)</figcaption></figure>

<figure><img src="image_8.png" /></figure>

External vs Internal, d28

<figure><img src="image_9.png" /><figcaption>(I)</figcaption></figure>

<figure><img src="image_10.png" /></figure>
**Figure 1.** Differentially abundant proteins (DAPs) and, pathways and process enrichment analysis of the DAPs as affected by location (external, internal) in UV pre-treated beef loins at 0, 21, and 28 days of dry-aging (i.e. 3, 24, and 31 days *post-mortem*). (A, D, G) Volcano plots showing the DAPs for each comparison. Red coloured proteins are more abundant in the external location, whereas blue coloured proteins are more abundant in the internal location (see proteins in supplementary tables 1, 2, and 3). (B, E, H) Heatmaps indicating the most representative enriched Gene Ontology (GO) terms using the DAPs from each comparison. GO terms are coloured by P-values using Metascape® (https://metascape.org); the darker the colour the more significant. (C, F, I) Heatmap analysis based on the top 30 proteins of each comparison. Each column corresponds to a sample whereas each row correspond to a protein. The red and green represents the higher and lower abundance of proteins, respectively. Two main clusters are obtained based on protein abundance in each location (violet = external; red = internal).

<figure><img src="image_11.png" /></figure>
<figure><img src="image_12.png" /></figure>

**Figure 2.** Bioinformatics analysis using the proteins lists obtained when investigating the differentially abundant proteins (DAPs) between external and internal locations in UV pre-treated beef loins at 0, 21, and 28 days of dry-aging (i.e. 3, 24, and 31 days *post-mortem*). (A) Hierarchical heatmap showing the Top 20 representative enriched Gene Ontology (GO) terms using the three DAPs lists. Grey colour indicates lack of significant association. (B) Functional enrichment network with nodes representing the top enriched GO terms using the three DAPs lists. Nodes that share the same cluster are close to each other and share a unique colour, which identifies the cluster. Each cluster is named after the node presenting the most enriched GO term, although, each node has a specific GO term. (C) Venn diagram showing the common proteins among these DAPs lists.
<figure><img src="image_13.png" /></figure>

UV vs Control, d0

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

UV vs Control, d21

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

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

<figure><img src="image_19.png" /></figure>

UV vs Control, d28

<figure><img src="image_20.png" /></figure>

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

**Figure 3.** Differentially abundant proteins (DAPs) and, pathways and process enrichment analysis of the DAPs as affected by treatment (control, ultraviolet) in the external location of beef loins at 0, 21,
and 28 days of dry-aging (i.e. 3, 24, and 31 days *post-mortem*). (A, D, G) Volcano plots showing the DAPs for each comparison. Red coloured proteins are more abundant in the external location of UV pre-treated samples, whereas blue coloured proteins are more abundant in the external location of the control samples (see proteins in supplementary tables 4, 5, and 6). (B, E, H) Heatmaps indicating the most representative enriched GO terms using the DAPs from each comparison. These terms are coloured based on *P*-values; the darkest colour is the most significant. (C, F, I) Heatmap analysis based on the top 30 proteins of each comparison. Each column corresponds to a sample whereas each row correspond to a protein. The red and green represents the higher and lower abundance of proteins, respectively. Two main clusters are obtained based on protein abundance for each treatment (orange = control; violet = ultraviolet).

<figure><img src="image_22.png" /></figure>
<figure><img src="image_23.png" /><figcaption>**Figure 4.** Bioinformatics analysis using the proteins lists obtained when investigating the differentially abundant proteins (DAPs) between the external locations of both UV pre-treated and control loins at 0, 21, and 28 days of dry-aging (i.e. 3, 24, and 31 days *post-mortem*). (A) Hierarchical heatmap showing the Top 20 representative enriched Gene Ontology (GO) terms using the three DAPs lists. Grey colour indicates lack of significant association (B) Functional enrichment network with nodes representing the top enriched GO terms using the three DAPs lists. Nodes that share the same cluster are close to each other and share a unique colour, which identifies the cluster. Each cluster is named after the node presenting the most enriched GO term, although, each node has a specific GO term. (C) Venn diagram showing the common proteins among the three DAPs lists.</figcaption></figure>
<table><caption>**Table 1.** Significant Gene Ontology (GO) terms computed using the list of differential abundant proteins (DAPs) obtained from the comparison between external and internal locations at 0, 21, and 28 days of dry-aging (only UV pre-treated samples). The tables includes the biological processes of the proteins that are involved in the enrichment of the significant GO terms. The enrichment analysis characterise a gene list by searching for genes that are over represented on this list and classifying them into different classes based on their biological function; this computation results in the association of proteins to GO terms.</caption><thead><tr><th>GO term</th><th>GO Biological Process</th><th>Proteins constituting the process (Gene names)</th><th>P-value</th></tr></thead><tbody><tr><td colspan="4"><strong>Ex vs In at day 0 (UV)</strong></td></tr><tr><td>GO:0048608</td><td>reproductive structure development</td><td>A2M|TNC|LRP2|SOD1</td><td>2.42E-05</td></tr><tr><td>GO:0032102</td><td>negative regulation of response to external stimulus</td><td>A2M|GP1BA|SOD1|WFDC1</td><td>1.57E-04</td></tr><tr><td>GO:0034694</td><td>response to prostaglandin</td><td>A2M|TNC</td><td>1.64E-04</td></tr><tr><td>GO:0006953</td><td>acute-phase response</td><td>A2M|SERPINA3</td><td>2.56E-04</td></tr><tr><td>GO:0051282</td><td>regulation of sequestering of calcium ion</td><td>GP1BA|JSRP1</td><td>3.05E-03</td></tr><tr><td>GO:0090101</td><td>negative regulation of transmembrane receptor protein serine/threonine kinase signalling pathway</td><td>LRP2|VASN</td><td>4.99E-03</td></tr><tr><td colspan="4"><strong>Ex vs In at day 21 (UV)</strong></td></tr><tr><td>GO:0043501</td><td>skeletal muscle adaptation</td><td>ACTN3|MYOC</td><td>6.21E-05</td></tr><tr><td>GO:0015980</td><td>energy derivation by oxidation of organic compounds</td><td>ETFA|IDH3G|PPP1CB|SUCLG2</td><td>1.29E-04</td></tr><tr><td>GO:0006631</td><td>fatty acid metabolic process</td><td>DBI|ETFA|GSTA1|AKR1C3</td><td>2.79E-04</td></tr><tr><td>GO:0010038</td><td>response to metal ion</td><td>CD14|PRNP|TUBA1A|AKR1C3</td><td>4.16E-04</td></tr></tbody></table>
<table><tr><td>GO:00903<br>16</td><td>positive regulation of intracellular protein transport</td><td>FLNA|IPO5|PRNP</td><td>5.09E-04</td></tr><tr><td>GO:19000<br>26</td><td>positive regulation of substrate adhesion-dependent cell spreading</td><td>FLNA|MYOC</td><td>7.96E-04</td></tr><tr><td>GO:00197<br>31</td><td>antibacterial humoral response</td><td>JCHAIN|LGALS4</td><td>1.84E-03</td></tr><tr><td>GO:00022<br>21</td><td>pattern recognition receptor signalling pathway</td><td>CD14|IPO5</td><td>5.99E-03</td></tr><tr><td>GO:00326<br>49</td><td>regulation of type II interferon production</td><td>CD14|PRNP</td><td>6.19E-03</td></tr><tr><td>GO:00716<br>75</td><td>regulation of mononuclear cell migration</td><td>NBL1|MADCAM1</td><td>6.81E-03</td></tr><tr><td>GO:00465<br>78</td><td>regulation of Ras protein signal transduction</td><td>MYOC|CADM4</td><td>1.41E-02</td></tr></table>

Ex vs In at day 28 (UV)

<table><tr><td>GO:00506<br>70</td><td>regulation of lymphocyte proliferation</td><td>ANXA1|CD74|CD55|EFNB1|FCGR3A|PNP|VSIG4</td><td>7.19E-10</td></tr><tr><td>GO:00022<br>52</td><td>immune effector process</td><td>C2|C9|CD74|CD55|FCGR3A|PTGDS|VSIG4</td><td>6.25E-08</td></tr><tr><td>GO:00903<br>03</td><td>positive regulation of wound healing</td><td>ANXA1|APOH|VTN</td><td>1.82E-05</td></tr><tr><td>GO:00326<br>63</td><td>regulation of interleukin-2 production</td><td>ANXA1|PNP|VSIG4</td><td>1.91E-05</td></tr><tr><td>GO:00069<br>57</td><td>complement activation, alternative pathway</td><td>C9|VSIG4</td><td>6.82E-05</td></tr><tr><td>GO:00015<br>16</td><td>prostaglandin biosynthetic process</td><td>CD74|PTGDS</td><td>1.36E-04</td></tr><tr><td>GO:00310<br>99</td><td>regeneration</td><td>ANXA1|PTGFRN|VTN</td><td>2.57E-04</td></tr></table>
<table><tr><td>GO:0002274</td><td>myeloid leukocyte activation</td><td>ANXA1|FCGR3A|PTGDS</td><td>2.68E-04</td></tr><tr><td>GO:0048863</td><td>stem cell differentiation</td><td>CDH2|EFNB1|KRT14</td><td>4.59E-04</td></tr><tr><td>GO:0045807</td><td>positive regulation of endocytosis</td><td>C2|VTN</td><td>7.22E-03</td></tr><tr><td>GO:0072522</td><td>purine-containing compound biosynthetic process</td><td>PNP|PDHB</td><td>1.62E-02</td></tr><tr><td>GO:0002768</td><td>immune response-regulating cell surface receptor signalling pathway</td><td>FCGR3A|BTNL2</td><td>2.09E-02</td></tr></table>
<table><caption>**Table 2.** Significant Gene Ontology (GO) terms computed using the list of differential abundant proteins (DAPs) obtained from the comparison between UV pre-treated and control samples at 0, 21, and 28 days of dry-aging (only external sampling location). The tables includes the biological processes of the proteins that are involved in the enrichment of the significant GO terms.</caption><thead><tr><th>GO term</th><th>GO Biological Process</th><th>Proteins constituting the process (Gene names)</th><th>P-value</th></tr></thead><tbody><tr><td colspan="4"><strong>UV vs CO at day 0 (External)</strong></td></tr><tr><td>GO:0070934</td><td>CRD-mediated mRNA stabilization</td><td>HNRNPD|SYNCRIP</td><td>3.29E-05</td></tr><tr><td>GO:0097305</td><td>response to alcohol</td><td>CD14|CD27|GLB1|HNRNPD</td><td>4.42E-05</td></tr><tr><td>GO:0044282</td><td>small molecule catabolic process</td><td>GLB1|HEXB|OXCT1|CRYL1</td><td>1.62E-04</td></tr><tr><td>GO:0070232</td><td>regulation of T cell apoptotic process</td><td>CD27|LGALS3</td><td>4.60E-04</td></tr><tr><td>GO:0032787</td><td>monocarboxylic acid metabolic process</td><td>AKR1B1|HEXB|GRHPR|CRYL1</td><td>6.64E-04</td></tr><tr><td>GO:0051346</td><td>negative regulation of hydrolase activity</td><td>SERPINA3|LGALS3|SERPINA5</td><td>6.65E-04</td></tr><tr><td>GO:0050905</td><td>neuromuscular process</td><td>HEXB|JSRP1</td><td>6.01E-03</td></tr><tr><td colspan="4"><strong>UV vs CO at day 21 (External)</strong></td></tr><tr><td>GO:0007160</td><td>cell-matrix adhesion</td><td>RAC1|FBLN5|NID2</td><td>8.13E-05</td></tr><tr><td>GO:0032273</td><td>positive regulation of protein polymerization</td><td>RAC1|KIRREL1</td><td>1.48E-03</td></tr><tr><td>GO:0001818</td><td>negative regulation of cytokine production</td><td>ANXA4|RAC1|UFD1</td><td>1.65E-03</td></tr><tr><td>GO:0009611</td><td>response to wounding</td><td>SERPINC1|PDIA3|RAC1</td><td>2.22E-03</td></tr><tr><td>GO:0071692</td><td>protein localization to extracellular region</td><td>NBL1|CAVIN1</td><td>3.11E-03</td></tr><tr><td>GO:0030855</td><td>epithelial cell differentiation</td><td>ANXA4|ANXA7|PGK1</td><td>6.64E-03</td></tr><tr><td>GO:0031400</td><td>negative regulation of protein modification process</td><td>DBI|KIRREL1</td><td>3.02E-02</td></tr><tr><td colspan="4"><strong>UV vs CO at day 28 (External)</strong></td></tr><tr><td>GO:1904851</td><td>positive regulation of establishment of protein localization to telomere</td><td>CCT7|CCT8</td><td>5.38E-06</td></tr><tr><td>GO:0006099</td><td>tricarboxylic acid cycle</td><td>DLAT|SUCLG2</td><td>6.29E-05</td></tr><tr><td>GO:0045785</td><td>positive regulation of cell adhesion</td><td>EFNB1|FBLN2</td><td>1.26E-02</td></tr><tr><td>GO:0030163</td><td>protein catabolic process</td><td>DPP7|PCYOX1</td><td>2.60E-02</td></tr></tbody></table>