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. 2025 Jun 25;41(7):230. doi: 10.1007/s11274-025-04397-5

Proteomic analysis reveals phage-driven metabolic shifts and biofilm disruption in methicillin-resistant Staphylococcus aureus (MRSA)

Khulood Hamid Dakheel 1,✉, Raha Abdul Rahim 2,3, Jameel R Al-Obaidi 4,5,✉, Nurhanani Razali 6,7, Vasantha Kumari Neela 8, Tan Geok Hun 9, Khatijah Yusoff 3,10,✉
PMCID: PMC12198068  PMID: 40560268

Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) biofilms pose a severe risk to public health, showing resistance to standard antibiotics, which drives the need for novel antibacterial strategies. Bacteriophages have emerged as potential agents against biofilms, especially through their phage-encoded enzymes that disrupt the biofilm matrix, enhancing bacterial susceptibility. In this study, two bacteriophages, UPMK_1 and UPMK_2, were propagated on MRSA strains t127/4 and t223/20, respectively. Biofilms formed by these strains were treated with phages at specified concentrations, followed by protein extraction and analysis. Comparative proteomic profiling was performed using one-dimensional and two-dimensional SDS-PAGE, with protein identification facilitated by MALDI-TOF/TOF MS spectrometry, to observe biofilm degradation effects. Proteomic analysis revealed that phage treatment induced significant changes in biofilm protein expression, particularly with upregulated ribosome-recycling factors and elongation factors linked to enhanced protein synthesis, reflecting a reactivation of amino acid metabolism in the treated biofilms. This was marked by upregulated intracellular proteases like CIpL, which play a role in protein refolding and degradation, critical for phage progeny production and biofilm disruption. Phage treatment demonstrated notable effects on the metabolic and protein synthesis pathways within MRSA biofilms, suggesting that phages can redirect bacterial cellular processes to favour biofilm breakdown. This indicates the potential of bacteriophages as a viable adjunct to traditional antimicrobial approaches, particularly in combating antibiotic-resistant infections like MRSA. The study underscores the efficacy of bacteriophages as anti-biofilm agents, offering a promising strategy to weaken biofilms and combat antibiotic resistance through targeted disruption of bacterial metabolic pathways and biofilm integrity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11274-025-04397-5.

Keywords: Biofilm disruption, Bacteriophages therapy, Phage-encoded enzymes, Protein synthesis

Introduction

Biofilms formed by various bacterial species present a significant threat to human health, with methicillin-resistant Staphylococcus aureus (MRSA) being a particularly remarkable example due to its resistance to many ordinary antibiotics (Tuon et al. 2023; Kaushik et al. 2024). The persistence of biofilms in medical settings can lead to chronic infections and increased resistance to antimicrobial treatments, making it crucial to find effective methods for biofilm disruption and eradication (Dakheel et al. 2019). Recent advancements have shown the practical success of bacteriophages in mitigating the dangers posed by these biofilms (Mendhe et al. 2023; Ali et al. 2023; Kovacs et al. 2024). Understanding the interactions between bacteriophages and their bacterial hosts is essential for developing effective anti-biofilm strategies (Meneses et al. 2023). Bacteriophages, or phages, are viruses that specifically infect bacteria, using the host's cellular machinery to replicate and produce progeny phages (Dennehy and Abedon 2021). This process often leads to the lysis of the bacterial cell and can significantly disrupt biofilm integrity (Roy et al. 2018). The use of phage-encoded enzymes, such as endolysins and depolymerases, has been described as a powerful means to degrade the extracellular polymeric substance (EPS) that forms the protective matrix of biofilms (Ferriol-González and Domingo-Calap 2020). The breakdown of EPS facilitates the release of bacterial cells from the biofilm matrix, increasing their vulnerability to antimicrobial agents and the host immune system (Pinto et al. 2020). The ability of phages to divert the host's transcriptional and translational machinery is a key aspect of their effectiveness against biofilms (Visnapuu et al. 2022). By taking control of the bacterial cellular processes, phages can alter the stability of the biofilm system, often leading to its collapse (Pires et al. 2021). This manipulation of host processes underscores the potential for modifying phage-host interaction proteins to improve the effectiveness of antibacterial treatments (Pal et al. 2024). Modifying these interactions could improve the efficiency of phage therapy by targeting specific bacterial processes critical for biofilm maintenance and survival (Hansen et al. 2019).

Research has identified several host factors that play a role in phage infection, including cellular chaperonins, proteases, ATP-binding cassette (ABC) transporters, and other heat shock proteins (Qin et al. 2022; Abril et al. 2022; Tribelli et al. 2020). These host factors are usually part of a larger regulatory network that responds to phage infection. The phage-host interaction is complex, with various genes being upregulated or downregulated in response to the stress induced by phage infection (Finstrlová et al. 2022). These changes can be part of a direct stress response or due to the expression of virulent phage factors introduced into the host cell (Fernández et al. 2018). Knowledge of these interactions at the molecular level is essential to designing phage therapies that are better able to circumvent bacterial resistance mechanisms. (Hasan and Ahn 2022). Emerging proteomic techniques have been instrumental in unravelling the effects of phage infections on the host cellular proteome (Fossati et al. 2023). These techniques allow for the comprehensive analysis of protein expression changes in response to phage infection, providing insights into the molecular mechanisms underlying phage-host interactions (Jia et al. 2023). Proteomics can reveal how phage infection alters host protein expression, potentially identifying new targets for therapeutic intervention (Coombs 2020). Previous studies have laid the groundwork for understanding the role of phages in biofilm disruption (Dakheel et al. 2022). For instance, Namonyo and co-workers showed that phage treatment could significantly reduce bacterial counts in biofilms formed on medical devices (Namonyo et al. 2023). Similarly, Gutiérrez and colleagues demonstrated the effectiveness of bacteriophages in reducing biofilm formation in industrial water systems (Gutiérrez et al. 2017). These studies highlight the potential of phage therapy as a viable alternative to traditional antimicrobial treatments, particularly in combating antibiotic-resistant bacteria like MRSA (Atshan et al. 2023). The possibility of phage-encoded enzymes in degrading biofilm EPS, a critical step in biofilm disruption, is discussed in earlier research (Li et al. 2022). That work highlights the importance of understanding the specific enzymes involved and how they can be harnessed to enhance phage therapy. The dispersal of EPS leads to increased susceptibility of biofilm bacteria to antimicrobial agents, a finding that underscores the synergy between phage therapy and traditional antibiotics (Shrestha et al. 2022). Insights into the molecular mechanisms by which phages manipulate host bacterial cells reveal how phages can alter host transcription and translation processes, ultimately destabilising the biofilm structure (Grabowski et al. 2021). This manipulation is key to the success of phage therapy and highlights the importance of studying phage-host interactions at a molecular level (León-Félix and Villicaña 2021). Specific host factors involved in phage infection, such as chaperonins and ABC transporters, have been identified (Carrera et al. 2017). That suggests targeting these host factors could enhance the efficacy of phage therapy by disrupting critical bacterial processes. Additionally, phage infection induces a stress response in the host cell, leading to changes in gene expression that can affect biofilm stability (Fernández et al. 2017). The utility of proteomic techniques in studying phage-host interactions was demonstrated, providing a comprehensive view of how phage infection affects host protein expression and identifying potential targets for therapeutic intervention (Parmar et al. 2017). These proteomic approaches are crucial for understanding the complex dynamics of phage-host interactions and developing effective phage therapies. In our previous research, two bacteriophages—UPMK_1 (GenBank accession: MG543995) and UPMK_2 (MG564297)—were propagated on MRSA strains t127/4 and t223/20, respectively. Extensive characterisation confirmed their lytic activity against planktonic bacteria. One-step growth curve analysis revealed latent periods of 20 min for UPMK_1 and 15 min for UPMK_2. The burst sizes were 32 PFU and 67 PFU per infected cell for UPMK_1 and UPMK_2, respectively. While these values don't meet the threshold (> 100 PFU/cell) for classification as highly effective lytic phages (a key requirement for phage therapy applications), both phages demonstrated complete burst cycles by 35 min—a critical period associated with bacterial death and subsequent release of cytoplasmic proteins and macromolecular content.

Further evaluation of their antibiofilm activity against 48-h-old biofilms in 96-well microtiter plates showed significant biomass reduction. UPMK_1 achieved maximum biofilm biomass reduction (52%) against MRSA t127/4 after 6 h, with 97% reduction of viable bacteria within the biofilm. UPMK_2 showed slightly greater efficacy, achieving 58% biomass reduction against MRSA t223/20 after 8 h with 99% bacterial reduction. This is particularly notable as bacterial viability serves as the fundamental scaffold for biofilm maintenance. Confocal laser scanning microscopy (CLSM) analysis further confirmed these phages'biofilm disruption capabilities, revealing substantially degraded biofilm architectures compared to untreated controls (Dakheel et al. 2019). This study aimed to assess the comparative proteomic analysis of the differentially displayed proteins of mature MRSA biofilms before and after bacteriophage treatment. The molecular interactions that take place during phage infection and biofilm disruption are complex, and our research aims to provide further insights into the consequences of phage-host interactions within the biofilm. Such information is critical for formulating efficient bacterial biofilm management approaches for use in both clinical and commercial sectors. The findings could have a big impact on the treatment of antibiotic-resistant infections and the outcomes of surgical and medical procedures, as well as bioprocesses for industry applications.

Materials and methods

Phage propagation

Two bacteriophages, UPMK_1 (accession number: MG543995) and UPMK_2 (accession number: MG564297), were propagated on MRSA strains t127/4 and t223/20, respectively. The procedures for phage propagation, concentration, and purification followed the methods described by Dakheel and her group (Dakheel et al. 2019).

Bacterial protein preparation

Biofilm formation by MRSA strains t127/4 and t223/20 was achieved as described by Dakheel and her team. MRSA t127/4 cultures were incubated in 96-well flat-bottomed polystyrene microtiter plates for 48 h. After incubation, the supernatant was discarded, and the plates were washed twice with normal saline (0.85 g NaCl per 100 mL of dH2O). Biofilms were treated with UPMK_1 at a concentration of 2 × 10^8 PFU/mL in SM buffer supplemented with 7 mM CaCl2 for 6 h at 37 °C without shaking. Untreated biofilms served as controls (Dakheel et al. 2016); the biofilms were sonicated for 35 min (1-min sonication, power output 50, pulses 50, with 1-min rest), and microscope used to confirm biofilm disrupted and SDS -PAGE to confirm cell lysis by releasing bacterial cytoplasmic proteins and macromolecules in supernatant after sonicated treatment. Both treated and untreated biofilms were collected and centrifuged at 8000 × g for 10 min at 4 °C. Supernatants were treated with 100% chilled tri-chloroacetic acid (TCA) added at one-tenth of the sample volume to concentrate the proteins. Suspensions were mixed and divided into two parts: one part incubated on ice for 15 min and the other overnight at 4 °C to enhance protein yields, though this may damage sialic acid on glycoproteins. Both 15-min and overnight-incubated precipitates were mixed and centrifuged at 19,000 × g for 30 min at 4 °C (Dakheel et al. 2022). The supernatant was removed, and the protein pellet was resuspended in 1 mL sterilised Milli-Q water. Chilled 100% acetone (10 mL) was added, vortexed for 30 s, and incubated at −20 °C, with vortexing every 20 min for 30 s over 1 h. Biofilm proteins were pelleted by centrifugation at 19,000 × g for 30 min at 4 °C, air-dried, and resuspended in lysis buffer (30 mM Tris–HCl, pH 8.8, 7 M urea, 2 M thiourea, 4% CHAPS). The same method was used for MRSA t223/20, with biofilms incubated with UPMK_2 for 8 h. Protein concentration was estimated using the Quick Start Bradford protein assay kit (Bio-Rad, USA) with bovine serum albumin (BSA) as the standard.

Protein separation

Protein separation was performed using both one-dimensional (1D) and two-dimensional (2D) denaturing sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE). For 1D SDS-PAGE, the method described earlier was used to assess protein sample quality and abundance (Hussain et al. 2020). For 2D SDS-PAGE, protein solutions were mixed with rehydration buffer (Bio-Rad), supplemented with Dithiothreitol (DTT), and pipetted into a channel in a reswelling tray (Bio-Rad). Immobilised pH gradient (IPG) polyacrylamide gel strips (Immobiline™ Dry Strips, pH 4–7) (GE Healthcare Bio-Sciences AB, Sweden) were laid onto the gel samples, covered with mineral oil, and left overnight in a cold room for rehydration (Mahmood et al. 2021). Isoelectric focusing (IEF) was performed in four steps at 20 °C under standard conditions (50 mA/strip) using the Ettan IPGphor II Electrophoresis unit (Amersham Biosciences, UK). IPG strips were incubated in equilibration buffer-1 (50 mM Tris–HCl, pH 8.8, 6 M urea, 30% glycerol, 2% SDS, a trace amount of bromophenol blue, and 10 mg/mL DTT) for 15 min with gentle shaking, followed by equilibration buffer-2 (50 mM Tris–HCl, pH 8.8, 6 M urea, 30% glycerol, 2% SDS, a trace amount of bromophenol blue, and 25 mg/mL iodoacetamide) for another 15 min. The IPG strips were rinsed in SDS gel running buffer and placed on 12% SDS gels, electrophoresed at 15 °C for 3 h, washed with Milli-Q water, and stained with Coomassie Brilliant Blue G-250. Gels were scanned with the Densitometer GS-800 Mode Imager (Bio-Rad, USA) and analysed using Progenesis SameSpots software (Nonlinear Dynamics, Durham, NC, USA), identifying differentially expressed proteins based on statistical confidence (Anova P-value ≤ 0.05) and fold change (≥ 2).

Proteomic analyses

Preparation of peptides

Protein spots in gels were excised in a clean laminar airflow hood and collected in 1.5 mL microcentrifuge tubes containing 200 µL resolving solution (10% methanol and 7% acetic acid) and stored at 4 °C. The spots were processed following the earlier used protocol (Al-Obaidi et al. 2017).

Proteomic identification using MALDI-TOF-TOF MS spectrometry and data analysis

One microliter of dried eluent peptides was spotted on an AnchorChip MALDI 384-target plate (Bruker Daltonics, Germany) and co-crystallised with 1 µL of CHCA matrix (10 mg/mL in 30% ACN and 0.3% TFA). MALDI-TOF/TOF MS analysis was conducted as described by Al-Obaidi et al. (2017) using reflectron positive ion mode for MS/MS analysis. The ion source accelerated voltage was set at 8 kV, with peaks detected at a signal-to-noise ratio of 30 for data analyses using the internal algorithm of the 4000 series software. Peptide fragments were examined with the MASCOT program (www.matrixscience.com) using parameters set for the NCBInr and Swiss-Prot sequence databases, taxonomy: bacteria, enzyme: trypsin, fixed modifications [carbamidomethyl (C)], variable modifications [oxidation (M)], peptide tolerance (100 ppm and 200 ppm). Protein significance was tested using a random match probability (< 0.05) of pI, molecular mass, peptide masses, and percentage sequence coverage (Al-Obaidi et al. 2016).

Analysis of KEGG pathways of UPMK_1 and UPMK_2

To investigate the amino acid metabolism pathways, we utilised the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, a comprehensive resource for exploring high-level functions of biological systems from molecular-level information. Initially, the raw data, which included protein IDs, was carefully curated and formatted according to the KEGG database's requirements. The sequences were then annotated with enzyme commission (EC) numbers, gene names, or orthologous gene groups using the KEGG Automatic Annotation Server (KAAS). For this study, we focused specifically on the pathways involved in amino acid metabolism.

Once the IDs were appropriately annotated, they were uploaded to the KEGG Mapper tool for pathway reconstruction. This tool allowed us to map the annotated protein from MRSA t127/4 biofilm after treatment with UPMK_1 for six hours, and from MRSA t223/20 biofilm after treatment with UPMK_2 for eight hours. Onto the amino acid pathway (map00250) and amino acid metabolism, which encompasses essential metabolic reactions and intermediates involved in processes such as biosynthesis and degradation of amino acids. The output from the KEGG Mapper was visualised to identify the location and presence of the input genes or proteins within the amino acid metabolism pathway (Mahmood et al. 2021).

Pathway and biological function analysis with ingenuity pathway analysis (IPA)

The Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Redwood City, CA, USA; www.ingenuity.com) was employed to predict the canonical pathways and biological functions influenced by differentially expressed proteins identified through 2D-SDS-PAGE and MALDI-TOF/TOF analysis in MRSA t127/4 biofilm after treatment with UPMK_1 and UPMK_2. Key details, including protein identifiers, quantitative expression values, and p-values, were imported into IPA for a comprehensive analysis.

The analysis began with the ‘Core Analysis’ function in IPA, which contextualises data within relevant biological pathways and functions. Proteins displaying differential expression were included as input parameters. Each protein identifier was mapped and integrated into a global molecular network derived from the Ingenuity Knowledge Base. Based on this, IPA algorithmically generated a protein network according to established connectivity. A right-tailed Fisher’s exact test was then applied to determine the likelihood that each biological function's association with the network was not due to random chance.

Subsequently, an IPA comparison analysis was conducted to identify similarities and distinctions between the pathways and biological functions affected by UPMK_1 and UPMK_2 treatments in MRSA t127/4 biofilm. This comparison analysis followed the protocol outlined in previous research (Ahmed et al. 2021). The results were visualised in a heat map displaying pathway activity changes, with increased activity indicated by orange bars and decreased activity by blue bars, reflecting enrichment scores.

The top pathways linked to the highly expressed proteins identified in the MRSA t127/4 biofilm after treatment with UPMK_1 and UPMK_2 were illustrated by IPA molecular interactions analyses. The nodes in red show the identified proteins, alkyl hydroperoxide reductase subunit C, thiol peroxidase, superoxide dismutase and superoxide dismutase [Mn/Fe], which have greater impacts in affecting the top pathways, namely ‘Detoxification of ROS, NRF2-mediated Oxidative Stress Response, Superoxide Radicals Degradation and TP53 Regulates Metabolic Genes. Notably, the treatment with UPMK_2 showed a higher enrichment score in affecting the top pathway, ‘Detoxification of ROS’, compared to the UPMK_1 treatment. The supplementary table provides detailed information on pathways illustrated in the heat map and their enrichment scores (Table 2s).

STRING’s active interaction sources include text mining, experimental data, curated databases, co-expression patterns, gene neighbourhood proximity, gene fusion events, and co-occurrence across species, offering a comprehensive interaction landscape (Szklarczyk et al. 2023). A minimum required interaction score of medium confidence (0.400) ensures reliable associations. The network limits first-shell interactors to no more than five, with no second-shell interactors displayed.

Results

Protein sample preparation and SDS-PAGE analyses

The total protein concentration was measured using protein standard curves. For MRSA t127/4 biofilm, protein concentrations measured were 5.585 mg/mL in intact biofilm (before sonication) and 6.761 mg/mL after sonication (control). Similarly, MRSA t223/20 biofilm showed concentrations of 5.918 mg/mL before sonication and 6.585 mg/mL after sonication (control), reflecting bacterial lysis and release of cytoplasmic proteins/macromolecules through sonication (see supplementary Fig. 1s). Notably, phage-treated samples showed higher protein concentrations: 7.625 mg/mL for MRSA t127/4 treated with UPMK_1 and 7.712 mg/mL for MRSA t223/20 treated with UPMK_2. All samples (sonicated controls and phage-treated) were then separated by molecular weight using one-dimensional SDS-PAGE analysis (Fig. 1).

Fig. 1.

Fig. 1

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Testing for reproducibility and solubility of protein samples using SDS-PAGE after precipitation with TCA/acetone, followed by re-suspension in a lysis buffer. Lane M: Molecular mass markers in KDa; Lane A1: control biofilm of MRSA t127/4 post-sonicated; Lane A2: biofilm of MRSA t127/4 under treatment with phage UPMK_1; Lane B1: control biofilm of MRSA t223/20 after sonicated; Lane B2: biofilm of MRSA t223/20 after treatment with phage UPMK_2

2D-SDS-PAGE analysis

To further analyse the proteomic changes in MRSA biofilms, 2D SDS-PAGE was employed. The analysis compared the biofilm proteome of MRSA t127/4 after a 6-h exposure to phage UPMK_1 and MRSA t223/20 after an 8-h exposure to phage UPMK_2. Total extracellular proteins were extracted from the biofilms before and after phage treatment and subjected to 2D SDS-PAGE. This separation process involved two stages, as described earlier (Al-Obaidi et al. 2017). Initially, proteins were separated by isoelectric focusing at their isoelectric points, followed by further separation based on molecular weight on a gel strip in the second dimension.

Representative images of the two-dimensional separation for MRSA t127/4 before and after treatment with UPMK_1, as well as MRSA t223/20 before and after treatment with UPMK_2, are presented in Figs. 2 and 3. Image analysis performed with Progenesis SameSpot software (www.nonlinear.com, Newcastle, UK) identified differentially expressed spots between treated and control (untreated) biofilms, as seen in Figs. 2 and 3. The intensity of each protein spot was assessed using a p-value from a one-way analysis of variance, with a significance threshold of 0.05 and a max fold change of ≥ 2. The software normalised the intensity of each protein spot and calculated the average normalised volumes for all protein spots.

Fig. 2.

Fig. 2

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Profiling of 2D Gel Protein Patterns in Untreated MRSA t127/4 Biofilm vs. Phage UPMK_1-Treated Biofilm. In total, 300 µg of the biofilm protein extract was separated using 2D gels with IPG strips (PI 4–7) in the first dimension. Coomassie blue-stained protein spots were scanned using a Densitometer GS-800 Mode Imager

Fig. 3.

Fig. 3

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Profiling of 2D Gel Protein Patterns in Untreated MRSA t223/20 biofilm vs. UPMK_2 Treated Biofilm. In total, 300 µg of the biofilm protein extract was separated using 2D gels with IPG strips (PI 4–7) in the first dimension. Coomassie blue-stained protein spots were scanned using a Densitometer GS-800 Mode Imager

These analyses were conducted to identify trends in protein expression, determining whether specific spots were up-regulated or down-regulated. Progenesis SameSpot software analysis of gels (MRSA t127/4 treated and untreated with UPMK_1) identified approximately 51 protein spots, with 6 down-regulated and 45 up-regulated. In comparison, analysis of gels in Fig. 3 (MRSA t223/20 treated and untreated with UPMK_2) revealed around 45 protein spots, with 6 down-regulated and 39 up-regulated. All protein spots were further analysed by MALDI-TOF/TOF MS (Figs. 4, 5).

Fig. 4.

Fig. 4

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Sample 2D Gel Electrophoresis Image of MRSA t127/4 biofilm after being treated with phage UPMK_1. The location of the identified differential protein spots detected by MS/MS

Fig. 5.

Fig. 5

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Sample 2D Gel Electrophoresis Image of MRSA t223/20 biofilm after being treated with phage UPMK_2. The location of the identified differential protein spots detected by MS/MS

Database searches and MS/MS analysis using the Swiss-Prot and NCBInr databases identified 22 proteins (represented by 22 spots) from the 51 proteins (MRSA t127/4 biofilm after UPMK_1 treatment). Among these, 18 proteins had known functions, while 5 proteins had unknown functions, as illustrated in Fig. 6A and detailed in Table 1. The identified proteins had molecular masses ranging from 13 to 86 kDa. Analysis of 45 protein spots (from MRSA t223/20 biofilm after UPMK_2 treatment) identified 13 proteins (represented by 22 spots) through MS/MS; 12 of these proteins had known functions, while one protein (SAV1854) had an unknown function, as shown in Fig. 6B and Table 2. The proteins identified from MRSA t223/20 biofilm had molecular masses ranging from 11 to 65 kDa. The SAV1854 protein was also detected in MRSA t127/4 biofilm after UPMK_1 treatment. Multiple spots on the gels indicated the presence of several isoforms or post-translational modifications of certain proteins, such as protein A (spots 38, 39, 44), argininosuccinate synthase (spots 83 and 88), and alkyl hydroperoxide reductase subunit C (spots 236 and 350) in MRSA t127/4 biofilm after UPMK_1 treatment. Similarly, enolase (spots 222 and 287), phosphate acetyltransferase (spots 251, 334, and 344), alkyl hydroperoxide reductase subunit C (spots 453, 460, 498, 500, and 508), and superoxide dismutase (spots 436, 448, and 450) were detected in MRSA t223/20 biofilm after UPMK_2 treatment, suggesting the presence of multiple isoforms or modifications of these proteins.

Fig. 6.

Fig. 6

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Functional categorisation of MRSA Biofilm Proteins: Post-Phage UPMK_1 Treatment in t127/4 (A) and Post-Phage UPMK_2 Treatment in t223/20 (B)

Table 1.

Proteins Identified in MRSA t127/4 biofilm via Differential 2D-SDS-PAGE and MALDI-TOF/TOF Analysis Post Six-Hour UPMK_1 Treatment

Spot number Protein name Reference organism Accession No Theoretical MW(KDa)/pI* Experimental Mw(KDa)/PI Score Biological function Fold change P-value Expression value
Bacteria Bacteria with phage
10 Elongation factor G Alkaliphilus metalliredigens EFG_ALKMQ 76.1/4.93 86/4.56 92 Translation factor 6.5 0.027 5.19E + 03 3.36E + 04
38 Protein A (ttg start codon) S. aureus gi|153,104 0.188/5.52 74/5.09 99 Unknown 5.7 1.90E-02 1.06E + 03 5.99E + 03
39 Protein A (ttg start codon) S. aureus gi|153,104 0.188/5.52 74/5.01 55 Unknown 7.3 2.00E-03 4.27E + 02 3.12E + 03
40 ATP-dependent Clp protease ATP-binding (ClpP) S. aureus CLPL_STAAC 77.8/4.88 47/5.17 58 Chaperones and folding catalysts 9.7 2.00E-03 3.35E + 02 3.25E + 03
44 Immunoglobulin G-binding protein A S. aureus SPA_STAAU 48.6/5.18 48/5.15 164 IgG -binding protein 6 2.00E-03 1.02E + 04 6.13E + 04
63 Serine-tRNA ligase S. aureus SYS_STAAB 0.188/5.52 46/5.13 101 Aminoacyl-tRNA biosynthesis 5.8 0.003 4.76E + 04 2.77E + 05
75 phosphoglycerate kinase S. aureus PGK_STAA1 0.188/5.52 44/5.31 131 Glycolysis/Gluconeogenesis 4.3 0.057 413.268 1795.224
83 Argininosuccinate synthase S. aureus gi|417,895,184 0.188/5.52 44/4.89 144 Amino acid metabolism 2.1 0.006 2.12E + 04 4.48E + 04
88 Argininosuccinate synthase S. aureus ASSY_STAAC 44.4/4.86 44/4.94 144 Amino acid metabolism 2.4 0.016 1.44E + 04 3.39E + 04
98 Pyrimidine-nucleoside phosphorylase S. aureus PDP_STAAC 46.3/4.94 43/4.95 84 Nucleotide metabolism 3.4 4.10E-02 1.51E + 04 5.10E + 04
108 Putative 2-hydroxyacid dehydrogenase SA2098 S. aureus Y2098_STAAN 34.7/5.14 34/5.29 167 Amino acid metabolism 2.2 1.70E-02 4.77E + 04 2.21E + 04
125 Phosphate acetyltransferase S. aureus PTAS_STAAC 34.9/4.72 33/4.71 92 Carbohydrate metabolism 2.8 2.40E-02 4.65E + 03 1.30E + 04
145 Purine nucleoside phosphorylase S. aureus gi|15,925,128 0.188/5.52 21/4.92 104 Purine metabolism 3.6 4.70E-02 2.72E + 04 9.74E + 04
197 Ribosome-recycling factor S. aureus RRF_STAAB 20.4/5.04 20/5.08 74 Translation factor 6.2 0.015 1.65E + 03 1.02E + 04
218 Phage minor structural GP20 S. aureus gi|296,276,233 0.188/5.52 21/4.52 60 Viral capsid assembly 5.5 2.40E-02 2.35E + 03 1.29E + 04
236 Alkyl hydroperoxide reductase subunit C S. aureus AHPC_STAAB 20.9/4.88 18/4.92 196 Oxidoreductases 3.9 4.10E-02 7.20E + 03 2.79E + 04
240 Uncharacterized protein SA1692 S. aureus Y1692_STAAN 18.6/4.59 19/4.59 38 Unknown 14.4 4.47E-04 1.34E + 03 1.93E + 04
243 Bacterial non-heme ferritin S. aureus FTN_STAAB 19.5/4.64 18/4.72 76 Oxidoreduct-ases 8.3 0.041 4.85E + 02 4.01E + 03
244 Superoxide dismutase S. aureus SODM1_STAA3 22.7/5.08 18/5.15 114 Oxidoreductases 4.7 9.50E-04 1.94E + 04 9.16E + 04
286 Hypothetical protein SAV1854 S. aureus gi|15,924,844 0.188/5.52 17/5.49 68 Unknown 3.8 0.019 1.41E + 04 3.68E + 03
291 Thiol peroxidase (Scavengase P20) S. capitis SK14 gi|223,043,592 0.188/5.52 16/4.52 86 Oxidoreductases 7.4 3.50E-02 5.47E + 03 4.08E + 04
316 Cold shock protein CspA S. aureus CSPA_STAAB 7.3/4.51 13/4.6 75 Transcription factors 3.6 2.00E-03 1.81E + 04 5.07E + 03
342 Hypothetical protein SACIG290_2573 S. aureus gi|418,994,651 0.188/5.52 17/5.29 96 Unknown 3.2 8.00E-03 1.62E + 04 5.06E + 03
350 Alkyl hydroperoxide reductase subunit C S. aureus AHPC_STAAB 20.9/4.88 18/4.83 79 Oxidoreductases 4.9 0.002 9.59E + 04 1.97E + 04
364 DNA-binding protein HU Bacillus caldotenax DBH_BACCA 9.7/9.52 13/4.39 56 DNA condensation 2.7 0.002 5.80E + 02 1.58E + 03
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*Theoretical molecular weight and pI values were determined via the Compute pI/Mw tool on ExPASy (http://web.expasy.org/compute_pi/)

Table 2.

Proteins Identified in MRSA t223/20 biofilm via Differential 2D-SDS-PAGE and MALDI-TOF/TOF Analysis Post Eight-Hours UPMK_2 Treatment

Spot number Protein name Reference organism Accession No Theoretical Mw (KDa)/pI* Experimental Mw (KDa)/pI Score Biological function Fold change P- Value Expression value
Bacteria Bacteria with phage
154 Putative acetolactate synthase S. aureus gi|386,831,787 0.19/5.52 65/5.25 145 Amino acid metabolism 2.6 1.90E-02 8.74E + 03 2.27E + 04
222 Enolase S. aureus ENO_STAAB 47.1/4.55 55/4.4 136 Glycolysis/Gluconeogenesis 2.4 1.20E-02 1.07E + 05 4.45E + 04
251 Phosphate acetyltransferase S. aureus PTAS_STAAC 34.9/4.72 40/4 95 Pyruvate metabolism 3.4 7.00E-03 4.58E + 05 1.35E + 05
287 Enolase S. aureus ENO_ENTFA 46.3/4.56 42/5.4 214 Glycolysis/Gluconeogenesis 2.1 3.40E-02 3.98E + 04 8.23E + 04
334 Phosphate acetyltransferase S. aureus PTAS_STAAC 34.9/4.72 33/4.8 71 Pyruvate metabolism 5.2 1.62E-04 6.01E + 04 3.12E + 05
344 Phosphate acetyltransferase S. aureus PTAS_STAAC 34.9/4.72 33/5.2 95 Pyruvate metabolism 2.8 2.00E-03 3.96E + 04 1.09E + 05
394 Ketose-bisphosphate aldolase Enterococcus faecium gi|69,245,010 0.188/5.52 27/5.3 62 Glycolysis/Gluconeogenesis 2.4 4.10E-02 4.43E + 04 1.05E + 05
415 Purine nucleoside phosphorylase S. aureus gi|15,925,128 0.19/5.52 24/4.65 133 Nucleotide metabolism 2 2.40E-02 1.31E + 05 2.67E + 05
417 Adenylate kinase S. aureus KAD_STAAC 23.9/4.80 24/5.0 196 Nucleotide metabolism 2.9 7.36E-04 1.60E + 05 4.67E + 05
436 Superoxide dismutase S. aureus SODM1_STAA3 22.7/5.08 21/5.49 88 Oxidoreductases 2.3 4.50E-02 6.46E + 03 1.48E + 04
437 UPF0173 metal-dependent hydrolase SA1529 S. aureus Y1529_STAAN 25.2/5.11 23/5.5 180 Methane metabolism 3 4.50E-02 4.69E + 03 1.43E + 04
445 3-hexulose-6-phosphate synthase S. aureus HPS_STAAB 22.4/4.58 22/4.75 63 Pentose phosphate pathway 2.5 4.00E-03 8.08E + 04 2.04E + 05
448 Superoxide dismutase [Mn/Fe] S. aureus SODM1_STAA3 22.7/5.08 21/4.45 70 Oxidoreductases 2.9 1.10E-02 6.64E + 03 1.93E + 04
450 Superoxide dismutase S. aureus SODM1_STAA3 22.7/5.08 22.5/5.2 73 Oxidoreductases 2.8 1.20E-02 2.65E + 04 7.43E + 04
453 Alkyl hydroperoxide reductase subunit C S. aureus AHPC_STAAB 20.9/4.88 21/5.19 323 Oxidoreductases 3.4 1.23E-04 1.10E + 05 3.75E + 05
456 Hypothetical protein SAV1854 S. aureus gi|15,924,844 0.19/5.52 21/5.9 134 Unknown 3.7 2.74E-04 2.05E + 04 7.56E + 04
460 Alkyl hydroperoxide reductase subunit C S. aureus AHPC_STAAB 20.9/4.88 20.5/4.75 123 Oxidoreductases 2.6 3.00E-03 8.31E + 04 2.14E + 05
498 Alkyl hydroperoxide reductase subunit C S. aureus AHPC_STAAB 20.9/4.88 18.5/5.15 168 Oxidoreductases 11.5 1.17E-06 4.46E + 04 5.11E + 05
500 Alkyl hydroperoxide reductase subunit C S. aureus AHPC_STAAB 20.9/4.88 18.5/4.35 117 Oxidoreductases 5 3.00E-03 1.27E + 04 6.43E + 04
508 Alkyl hydroperoxide reductase subunit C S. aureus AHPC_STAAB 20.9/4.88 18/4.9 357 Oxidoreductases 4.5 1.95E-05 6.79E + 04 3.64E + 05
521 Probable thiol peroxidase S. aureus TPX_STAAC 18/4.56 17.5/4.75 109 Oxidoreductases 3 2.00E-03 6.14E + 04 1.85E + 05
578 50S ribosomal protein L11 S. aureus RL11_STAA1 14.8/9.04 13.5/4.1 82 Translation 2.7 2.00E-02 1.24E + 04 3.37E + 04
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*Theoretical molecular weight and pI values were determined via the Compute pI/Mw tool on ExPASy (http://web.expasy.org/compute_pi/)

The identified proteins from MRSA t127/4 biofilm after UPMK_1 treatment and MRSA t223/20 biofilm after UPMK_2 treatment were assigned biological functions as shown in Figs. 6 A and B (Table 1s, 2s). Additionally, functional pathways for the identified proteins (protein–protein interactions) were predicted using the STRING database, version 12 (www.string-db.org), as depicted in Figs. 7 and 8 (Tables 3s,4s).

Fig. 7.

Fig. 7

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STRING network analysis of proteins identified from the biofilm of MRSA t127/4 following treatment with UPMK_1. The network illustrates the functional and physical associations among identified proteins. Colored nodes represent query proteins and their first-shell interactors, while white nodes indicate second-shell interactors. Edges indicate various types of interactions, including curated databases, experimental data, gene neighbourhood, gene fusion, co-occurrence, text mining, and co-expression, as indicated in the legend. Line thickness corresponds to confidence in the data support for each interaction

Fig. 8.

Fig. 8

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STRING network analysis of proteins identified from the biofilm of MRSA t223/20 following treatment with UPMK_2. The network displays the functional and physical associations among the identified proteins. Coloured nodes represent query proteins and their first-shell interactors, while white nodes represent second-shell interactors. Edges depict various interaction types, such as those derived from curated databases, experimental data, gene neighbourhood, gene fusion, co-occurrence, text mining, and co-expression, as indicated in the legend. Line thickness corresponds to the confidence level of data support for each interaction

KEGG pathway mapping of host metabolism affected by UPMK_1 and UPMK_2

The pathway map was further analysed using a colour-coded scheme to distinguish active and inactive regions of the pathway; green arrows indicated active pathways based on the input data. This visualisation, as depicted in Fig. 2s provided insights into the metabolic fluxes and potential bottlenecks within the carbon metabolism under various experimental conditions.

Subsequently, we interpreted the mapping of the pathways to conclude the overall activity of the carbon metabolism pathway. Comparisons were made against control or reference pathways to identify significant deviations or disruptions in the metabolic flow. Enzymes, metabolites, and reactions that demonstrated notable changes were highlighted for further investigation. Finally, the identified pathways and their activities were validated through additional experimental data and cross-referenced with existing literature, ensuring the robustness of our findings. Integration with data from transcriptomic or proteomic analyses further corroborated the conclusions drawn from this KEGG pathway mapping exercise.

Top canonical pathways affected by the significantly identified proteins in the MRSA t127/4 biofilm after treatment with UPMK_1 and UPMK_2

From the analyses, we identified the top canonical pathways associated with a dataset of significantly identified proteins from the MRSA t127/4 biofilm after treatment with UPMK_1 and UPMK_2, which uses Fisher's exact test to ascertain pathway enrichment. IPA analyses identified 14 canonical pathways that are affected by the protein expression changes.

The top pathways are associated with oxidative stress response, namely, ‘Detoxification of ROS, NRF2-mediated Oxidative Stress Response, Superoxide Radicals Degradation, and TP53 Regulates Metabolic Genes. UPMK_2 treatment strongly affects the detoxification of the ROS pathway with the highest enrichment score of 2.8, Fig. 9 and Table 5s.

Fig. 9.

Fig. 9

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Heat maps generated from IPA comparison analysis show the top 14 pathways that are differently affected by the significantly identified proteins in the MRSA t127/4 biofilm after treatment with UPMK_1 and UPMK_2. Orange bars indicate a positive enrichment score and white bars indicate no activation

The molecular interactions graphically show that 4 protein expression changes after the UMPK_1 and UMPK_2 treatments, namely, alkyl hydroperoxide reductase subunit C, thiol peroxidase, superoxide dismutase and superoxide dismutase [Mn/Fe] prominently affected the activation of the most top pathways that are associated with oxidative stress, Fig. 10.

Fig. 10.

Fig. 10

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IPA graphical representation shows the molecular interactions linking 4 proteins that significantly affected the “Detoxification of ROS” and later affected the other pathways, NRF2-mediated Oxidative Stress Response, Superoxide Radicals Degradation, and TP53 Regulates Metabolic Genes. The molecular interactions between the canonical pathway and the associated metabolites are displayed graphically as red nodes (proteins) with different intensities to represent the fold changes and lines (the biological activation)

Discussion

Comparative proteomic analyses of MRSA biofilm producers before and after treatment with bacteriophage (6 h for MRSA t127/4 biofilm treated with UPMK_1 and 8 h for MRSA t223/20 biofilm after treatment with UPMK_2 were evaluated. This data enabled the unravelling of some information on the process behind the degradation of biofilms by phages. Bacteria in biofilm face starvation in amino acid synthesis with very low energy due to the shutting down of several microbial metabolic cellular pathways to maintain a metabolically active state between an extremely metabolic quiescent state to a dormant state (Joshi et al. 2021; Létoffé et al. 2017). The results in this study revealed that the biological processes in the biofilms changed due to the reactivation of the metabolic and cellular pathways in MRSA t127/4 and MRSA t223/20 biofilms after treatment with UPMK_1 and UPMK_2, respectively. Full knowledge of the scope of virus-host interactions is lacking, especially that which allows phages to undergo revitalisation and redirect cellular machinery and energy resources to support the viral progeny production. Consequently, each response, however, yielded new information; despite that, 20% and 4% of the detectable proteins were hypothetical and uncharacterised proteins, respectively (Piechota et al. 2018).

Phage-induced protein synthesis pathways were significantly upregulated in MRSA t127/4 after UPMK_1 treatment, which aligns with the need for increased amino acid resources to support massive phage replication (Fig. 1s).

The relationship that exists between the bacterial amino acid metabolism and interactions of specific phage proteins with its host has been proposed by Fitzpatrick Alexa et al. ( 2025) to be ultimately linked to alterations in the metabolic products of amino acids that could promote phage multiplication in the host.

. Intracellular protease proteins, including ATP-dependent Clp protease (ClpP), which was up-regulated by 9.7-fold, have a role in refolding or degradation of misfolded or defective proteins (Aljghami et al. 2022). It has been previously reported that the amount of unfolded polypeptides within the cell increases during the infection of phages such as PRD1 (Guliy and Evstigneeva 2025). This is in contrast to the downregulation of the ClpP involved in gene expression in S. aureus during phage infection (Fernandez et al. 2017). In some way, this report supported the observation made in this study that the increased biofilm formation relative to phage infection was mediated by the down-regulation of the ATP-dependent Clp protease (Liu et al. 2017). Analysis of the biofilm produced following treatment with phage UPMK_2 showed that 50S ribosomal protein L11 (prmA) represented by spot 578, was up-regulated. Furthermore, putative acetolactate synthase was up-regulated by 2.6-fold, which indicated the role it can play in valine, leucine, and isoleucine biosynthesis. It can also be inferred that the process of these protein syntheses could serve in parallel with phage biosynthesis (Fig. 11).

Fig.11.

Fig.11

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Illustrates the stages of bacteriophage treatment on MRSA biofilms. In the untreated state, the biofilm is dense, with dormant bacteria exhibiting low metabolic activity and limited nutrient uptake. During phage treatment, bacteriophages adsorb to bacterial cells, degrade the biofilm matrix, trigger ROS production, and upregulate proteases to disrupt biofilm defences. The treated biofilm shows a disrupted structure, scattered bacterial cells, lysed remnants, and active metabolic pathways, including ATP production and amino acid synthesis, facilitating biofilm degradation

The capacity for phage DNA synthesis can occur due to bacteria's amino acid synthesis activities by up-regulation of the enzymes participating in the nucleotide metabolism. The pyrimidine–nucleotide phosphorylase (pdp) was up-regulated 3.4-fold after treatment of the MRSA t127/4 biofilm with UPMK_1. In the reversible phosphorolysis of the pyrimidine nucleosides (uridine, thymidine, and 2′-deoxyuridine), the pyrimidine nucleoside phosphorylase was reported to catalyse the formation of the corresponding pyrimidine base and ribose-1-phosphate. While purine-nucleoside phosphorylase (deoD1) was up-regulated in both MRSA t127/4 biofilm and MRSA t223/20 after being treated with UPMK_1 and UPMK_2, respectively, which catalyse the purine nucleosides and deoxynucleosides to generate purine bases. Furthermore, the adenylate kinase (adk) was observed to be up-regulated after treatment of the MRSA t223/20 biofilm with UPMK_2. Previous research has reported the involvement of adenylate kinase in effectively converting all nucleoside diphosphates to corresponding triphosphates (Tran et al. 2014). Additionally, another pathway was up-regulated to provide further requirements for building blocks (pentose skeletons, nucleotides) for phage DNA synthesis. The phage UPMK_2 was up-regulated by the 3-hexulose-6-phosphate synthase (SACOL0617). This enzyme has been reported to contribute to the pentose shunt pathway, which increases the formation of deoxyribonucleotides from ribonucleotides (Polat et al. 2021).

DNA stabilisation in bacteria is dependent on the histone-like DNA-binding protein (HU protein) and prevents the denaturation of DNA under extreme environmental conditions. During UPMK_1 infection, the HU protein was observed to be up-regulated. HU protein isolated from E. coli was different from HU isolated after bacteriophage T4 infection (Patterson-West et al. 2021). The group also revealed that the T4-encoded polypeptide bound to HU and altered their function, thereby modifying the HU protein, which could be involved in destroying the host genome while protecting the phage genome (Stojkova et al. 2019).

Glycolysis is the generation of energy in the form of ATP mediated by the metabolic cellular respiration pathway. The phosphoglycerate kinase (PGK) is also ATP-generating by which the carbon-oxidation reaction is mediated through a catalysed transfer of the phosphate group from 1,3-BPG to ADP to yield ATP (Melkonian and Schury 2024). This enzyme in MRSA t127/4 biofilm after UPMK_1 treatment was up-regulated 4.3-fold. Similarly, ketose-bisphosphate aldolase and enolase (ENO) from MRSA t223/20 biofilm after treatment with UPMK_2 were up-regulated. It is also worth mentioning that all these enzymes are essential for the degradation of carbohydrates in the glycolysis pathway (Kot et al. 2020).

Another critical finding was the upregulation of Staphylococcal protein A (SpA) under UPMK_1 treatment, potentially as a response to evade further phage adsorption by altering receptor availability on the bacterial surface (Plumet et al. 2022). This adaptive response highlights a nuanced host-phage interplay where bacterial proteins may modulate host surface properties to mitigate further phage binding, representing a complex balance between susceptibility to infection and host survival strategies. The MRSA t127/4 after treatment with phage UPMK_1 produced Staphylococcal protein A represented by three distinct proteins, which were detected as protein A (TTG start codon) spots 38 and 39, as well as spot 44 representing the immunoglobulin G-binding protein A (spa). These proteins were up-regulated under UPMK_1 treatment. The great difference in charge and mass between spots (44 and both 38 and 39) is likely due to post-translational modifications (phosphorylation or acetylation), and possibly conversion to active and/or inactive forms. It may be induced by phage infection to prevent the other phage UPMK_1 from being adsorbed to MRSA bacteria by masking the actual receptor site (Boero et al. 2022).

In bacteria, the normal course of aerobic metabolism produces reactive oxygen species (ROS) with the associated requirement for the constitutive expression of ROS-scavenging systems such as antioxidant enzymes (Jomova et al. 2023). Interestingly, phage treatment led to the up-regulation of several antioxidant proteins within the biofilm with different specificities toward different peroxides that were represented by alkyl hydroperoxide reductase subunit C (AhpC) spot 236 in MRSA t127/4 after treatment with UPMK_1 and spots 453, 460, 498, 500 and 508 in MRSA t223/20 after treatment with UPMK_2. Bacterial non-heme ferritin (ftnA) in MRSA t127/4 after treatment with UPMK_1 and thio peroxidase (tpx) were up-regulated in MRSA t127/4 and MRSA t223/20 after treatment with UPMK_1 and UPMK_2, respectively. Furthermore, superoxide dismutase (SOD) was up-regulated with 4.7-fold in MRSA t127/4 after treatment with UPMK_1and in MRSA t223/20 after treatment with UPMK_2 represented by three spots were also upregulated with 2.3, 2.9 and 2.8-fold. The antioxidant pathway up-regulated in this study was in multiple components with apparently overlapping functions. This may be part of a possible unique stress response induced by phage within the biofilm. However, the removal of different peroxides may not be the main function of these antioxidants (Joo et al. 2023). These proteins have been suggested to function as"moonlighting"proteins, performing more than one role inside the bacterial cell. Thioredoxin, an antioxidant expressed by E. coli itself, is exploited by bacteriophage T7 to enhance phage replication (Šimoliūnienė et al. 2021). Thioredoxin binds to DNA polymerase, which makes the replication more specific, thus increasing the number of phages. This interaction, other than promoting efficient phage replication, also affects bacterial pathogenicity at large by influencing the entire phage-bacterium dynamics (Naureen et al. 2020). Interestingly, Proline dehydrogenase (Delta-1-pyrroline-5-carboxylate dehydrogenase) is involved in 14 metabolic pathways. It is beneficial for phage propagation that one phage protein can control the expression of many genes in the host genome. Furthermore, this protein leads to the enrichment of many metabolic pathways when combined with other proteins, as shown in the STRING analysis. The 1-pyrroline-5-carboxylate dehydrogenase produced in response to stress plays a role in catabolizing proline for energy generation. Furthermore, the 1-pyrroline-5-carboxylate dehydrogenase is an oxidoreductase protein capable of preventing S. aureus colonisation and/or infection. Similarly, Weiland-Brauer et al. (2016) found that quorum quenching (QQ) protein is an oxidoreductase that efficiently inhibits AI-2 modulated biofilm formation in Klebsiella oxytoca M5a1 (Weiland-Bräuer et al. 2016). Therefore, further evaluation of delta-1-pyrroline-5-carboxylate dehydrogenase to examine its capability in biofilm formation and degradation could be a novel biotechnologically relevant anti-pathogenic/biofilm compound remedy. The KEGG annotation for the phage UPMK_2 genome showed that acrylamides with enzyme ID (EC:3.5.1.4) represented by gp44; with annotation, as mannosyl-glycoprotein endo-beta-N-acetylglucosaminidase (cell wall hydrolase) participated in five KEGG pathways. Notably, for mannosyl-glycoprotein, endo-beta-N-acetyl glucosaminidase could degrade the biofilm. This enzyme may stimulate some pathways to produce energy as required for phage proliferation (Ramakrishnan et al. 2022). Methane metabolism is an anaerobic process that produces methane as a final product of metabolism (Carr and Buan 2022). Both UPF0173 metal-dependent hydrolase and phosphate acetyltransferase (Pta) proteins were up-regulated and participated in the methane metabolism. While many of the mechanisms underlying bacterial death after phage infection are transient, others are not. In this case, the main mechanism is the lysis of a fraction of the infected cells, but secondary killing is due to factors released in the environment by cell lysis. Such a process, commonly referred to as death without lysis (DWL), is believed to treat stress responses and programmed cell death pathways (Ranveer et al. 2024). In the treated biofilms, pathways for reactive radical compounds (associated with superoxide, peroxidase, and hydroxyl) were enriched in UPMK_1 and UPMK_2 biosynthetic intermediaries. The extremely reactive species released during their secretion would kill cells after phage treatment by oxidative damage to the biofilm, leading to disturbance of structures and functions (Dakheel et al. 2022).

Conclusion

Analyses of the proteins that are induced by the phage in the infected cells through 2D followed by MALDI/TOF/TOF analysis, were performed to understand the mechanisms of interactions between the phages and bacteria within the biofilms. The findings suggest that bacteriophages could be a potential treatment strategy for antibiotic-resistant non-biofilm (MRSA) infection, especially the biofilm-associated ones, which aim to expand the current knowledge about human chronic infection. This means that MRSA can form biofilms, which can protect against antibiotics and the host immune system, often leading to chronic infections which are more difficult to treat. We show that phages UPMK_1 and UPMK_2 can degrade the biofilm matrix and increase susceptibility to biofilm treatment. Moreover, phages can trigger oxidative stress in MRSA cells, which destabilises the biofilm and wakes up dormant biofilm bacteria that remain recalcitrant to antibiotic treatments- a common tactic of biofilms. The phage therapy positioning with these mechanisms serves as research-backed current experimental regimen strategies to these complex biofilm-associated diseases and other antibiotic-resistant diseases.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1616 KB) (1.6MB, docx)

Author contributions

Conceptualisation and experimental design were carried out by JRAO, KY, KHD, RAR, and VKN. The experiments were performed by KHD. Data interpretation and manuscript writing were conducted by KHD and JRAO. Bioinformatic analysis was performed by NR and TGH. All authors approved the final version of the manuscript. JRAO, KHD, and KY are the corresponding authors.

Funding

Open access funding provided by The Ministry of Higher Education Malaysia and Universiti Pendidikan Sultan Idris. Fundamental Research Grant Scheme (FRGS/1/2015/SGOS/UPM/01/2) awarded by the Ministry of Higher Education, Malaysia.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethical approval

No ethical approval was required for this study, as no human participants or animal samples were involved.

Informed consent

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Khulood Hamid Dakheel, Email: khuloodhh76@yahoo.com.

Jameel R. Al-Obaidi, Email: jr_alobaidi@yahoo.com

Khatijah Yusoff, Email: kyusoff@upm.edu.my.

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Associated Data

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Supplementary Materials

Supplementary file1 (DOCX 1616 KB) (1.6MB, docx)

Data Availability Statement

No datasets were generated or analysed during the current study.

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