Machine learning analysis of type VII secretion system expression and its relationships with virulence traits and antibiotic resistance in Staphylococcus aureus

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At the heart of its virulence lies the type VII secretion system (T7SS)—a molecular weapon whose alliances with other pathogenic traits remain largely uncharted. Here, we combined wet-lab microbiology with machine learning to dissect the networks linking T7SS to enzymatic virulence factors, antibiotic resistance, and environmental triggers. We quantified associations with DNase, hemolysin, protease, lipase, staphyloxanthin and assessed modulation by physical (ultraviolet light), chemical (disinfectant), and biological (coculture with Escherichia coli ) factors. Among 150 clinical S. aureus isolates, 74% were methicillin-resistant, 0.6% vancomycin-resistant, and multidrug resistance observed in 88%. T7SS expression showed positive correlations with virulence factors but no association with MRSA status or antibiotic resistance. Machine learning analyses identified protease as the strongest predictor of T7SS expression and revealed clustering of high-expression phenotypes, highlighting complex interdependencies often overlooked by conventional approaches. Environmental factors significantly influenced T7SS expression: UV light downregulated it 1.5-fold, while sodium hypochlorite and E. coli coculture upregulated it by 2.5-fold and 2.2-fold, respectively. The positive correlation between T7SS and other virulence determinants suggests regulation by a shared accessory gene regulator (agr) system, though T7SS appears to act independently of resistance traits. Future research should investigate whether agr inhibitors can suppress T7SS and mitigate infection severity. Antibiotic resistance Machine learning Staphylococcus aureus Type VII secretion system Virulence factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Despite decades of research, Staphylococcus aureus continues to surprise clinicians and scientists with its remarkable ability to exploit an arsenal of virulence factors to invade tissues (Cheung et al. 2021 ), resist antibiotics (Ezeh et al. 2023 ), and evade the immune system(Pelz et al. 2005 ; Yehia et al. 2021 ; Bear et al. 2023 ). This Gram-positive pathogen, prioritised by the WHO for its public health impact (Sharaf et al. 2021 ), is notorious for causing infections that range from harmless skin lesions to life-threatening pneumonia and endocarditis (Cheung et al. 2021 ). The challenge is magnified by methicillin-resistant strains (MRSA), which compound virulence with multidrug resistance (Hassoun et al. 2017 ). Among these, the type VII secretion system (T7SS) has emerged as a critical weapon (Bowman and Palmer 2021 ), yet its connections to other virulence traits remain poorly understood. The T7SS, also known as the ESAT-6-like secretion system (ESS), is encoded by the ess locus and comprises multiple transmembrane components and secreted effectors, including EsxA, which serves as a marker of T7SS activity (Burts et al. 2005 ; Dai et al. 2017 ). T7SS contributes to infection persistence by lysing neutrophils (Dai et al. 2017 ), protecting against antimicrobial fatty acids (Kengmo Tchoupa et al. 2020 ), facilitating antibacterial competition (Garrett and Palmer 2024 ) and modulating host immunity (Cruciani et al. 2017 ). While the T7SS is a recognised virulence determinant, its relationships with other virulence factors and regulatory systems such as agr, SaeRS, and SarA remain incompletely understood (Jenul and Horswill 2019 ; Bezar et al. 2019 ; Wittekind et al. 2023 ; Liu et al. 2024 ). To date, no prior study has investigated how T7SS expression correlates with other virulence factors in S. aureus or how environmental stressors might modulate T7SS. We hypothesised that T7SS expression is associated with the levels of key virulence factors (DNase, hemolysin, protease, lipase, staphyloxanthin) (Fig. 1 ) and influenced by physical (UV irradiation), chemical (sodium hypochlorite), and biological (coculture with Escherichia coli ) exposure. Traditional analytical methods often fail to capture the complex interdependencies among virulence traits and resistance profiles. Therefore, this study integrated wet lab microbiology with machine learning approaches to systematically characterise co-expression patterns, quantify feature importance in predicting T7SS activity, and model resistance dynamics. Using this combined methodology, we aimed to elucidate the regulatory landscape of T7SS and its association with virulence and antibiotic resistance in clinical S. aureus isolates. Material and methods Bacterial collection, identification, and antibiotic susceptibility testing This study was conducted in a tertiary care setting and included 150 clinical S. aureus isolates collected at AIIMS Rishikesh between September 2023 and January 2025 from diverse specimens (pus, blood, tissue, sputum, urine, and body fluids). Control strains comprised S. aureus ATCC 25923, E. coli ATCC 25922, and the T7SS-positive S. aureus RN6390 (New York University School of Medicine). Isolates were identified using blood agar culture, standard biochemical tests, the VITEK2 system (BioMérieux), and MALDI-TOF (Bruker Biotyper), then preserved in glycerol stocks at − 20°C and − 80°C and maintained on nutrient agar at 4°C. Methicillin resistance was determined by cefoxitin disc diffusion (30 µg) on Mueller-Hinton agar following CLSI (2019) criteria, with the USA 600 MRSA strain as a positive control. Antibiotic susceptibility was assessed using the VITEK® 2 Compact system with AST p628 cards. The Institutional Ethics Committee approved the study (Letter No: AIIMS/IEC/23/294). Detection and quantification of the T7SS in clinical isolates of S. aureus Genomic DNA and total RNA were extracted from 24-hour blood agar cultures of S. aureus using HiPurA purification kits (HIMEDIA, India). Purity was assessed by agarose gel electrophoresis and NanoQuant spectrophotometry (Tecan Life Sciences). PCR amplification of the esxA gene was performed in 25 µl reactions containing Emerald dAMP GT Master Mix (Gene to Protein Pvt. Ltd.) and specific primers (Table 1 ) using an Applied Biosystems Veriti thermal cycler. S. aureus 16S rRNA served as the positive control, and E. coli ATCC 25922 as the negative control. For qPCR, RNA was reverse-transcribed to cDNA with G-Biosciences reagents. Reactions (20 µl) containing SYBR™ Green qPCR Master Mix were run on a Bio-Rad CFX96 system under standard cycling conditions. Amplicon specificity for both DNA and RNA analyses was confirmed by gel electrophoresis. Full protocols and thermal profiles are available in Supplementary Methods. Table 1 Primer sequences utilised in this study Gene Primer Sequence (5′-3′) Product Length (bp) Purpose esxA GCAATGATTAAGATGAGTCCAGAGG TTATTGCAAACCGAAATTATTAGAA 291 PCR esxA TTACGGGCAAGGTTCAGACC ACAGCGTCAGCAGTGCTATT 198 qPCR 16 srRNA ACGGTCTTGCTGTCACTTATA TACACATATGTTCTTCCCTAATAA 257 Internal control Detection of S. aureus virulence factors Colonies from 24-hour blood agar cultures were suspended in 0.9% saline (0.5 McFarland standard). DNase activity was tested on DNase agar with methyl green (HIMEDIA®); hemolysin on sheep blood agar (BioMérieux); protease on milk agar (HIMEDIA®); and lipase on egg yolk agar (10% egg yolk in nutrient agar). Plates were incubated at 37°C for 24 hours. DNase and lipase were indicated by clearance zones, hemolysin by lysis, and protease by precipitation. Zone sizes categorised production as weak (≤ 6 mm), moderate (7–8 mm), or high (≥ 9 mm). Staphyloxanthin was assessed on nutrient agar based on pigment intensity. Capsule detection was performed using Maneval’s stain as previously described (B et al. 2024), with visualisation under oil immersion microscopy. Correlations between the T7SS and other virulence factors of S. aureus Sample size (n = 84) was calculated in G*Power to achieve 80% power with 95% confidence. The T7SS Ct values were correlated with virulence factor zone sizes by bivariate analysis in IBM SPSS v21.0 (p < 0.05); as lower Ct indicates higher expression, negative correlations reflect positive associations. Furthermore, T7SS expression was correlated with MRSA and drug resistance patterns. Machine learning, dimensionality reduction, and resistance network analyses All data were normalised using a standard scaler before analysis. Machine learning analyses were conducted in Python v3.9. For dimensionality reduction and visualisation, Principal Component Analysis (PCA) was applied to capture variance in virulence factor expression, supported by scree plots and feature distributions to identify clustering and co-regulation patterns. Clustering analysis employed BIRCH pairplot visualisation and K-means clustering to explore whether elevated EsxA expression correlated with other virulence factors. Random Forest and SHapley Additive exPlanations (SHAP) analysis were conducted to quantify feature importance and rank contributions to EsxA expression, while Ridge regression examined the distribution of virulence factor levels. Interaction mapping involved constructing chord diagrams and line graphs to assess co-regulation among factors. A Markov Chain Model was developed to analyse transition dynamics based on feature co-occurrence. For the antibiotic susceptibility data of 150 S. aureus isolates, preprocessing and normalisation were performed to ensure consistency across variables. Heatmaps were used to visualise patterns of resistance, intermediate susceptibility, and sensitivity. Scatterplot clustering was applied to identify distinct resistance profiles. Violin plots were created to examine the distribution of resistance levels across antibiotics and assess the presence of mixed or bimodal resistance patterns. Chord diagrams were employed to map interactions and co-occurrence relationships among antibiotic resistance traits. A co-resistance network was generated in Gephi, with nodes representing antibiotics and edges denoting significant associations (p < 0.05). Dark grey edges indicated strong co-resistance. Finally, a Markov transition model, using maximum likelihood estimation, evaluated the probabilities of resistance evolution between antibiotics Regulation of the T7SS by environmental factors For assessment of physical stress, S. aureus colonies were cultured on blood agar and incubated overnight at 37°C. Plates were then exposed to UV irradiation using a 15-watt 254 nm UV lamp for 0, 10, 30, 60, or 120 minutes. For chemical treatment, bacterial suspensions adjusted to the 0.5 McFarland standard in 0.9% normal saline were treated with 0.1% sodium hypochlorite and incubated at 37°C for 60 minutes. For biological stress, S. aureus suspensions (0.5 McFarland) were cocultured with an equal volume of E. coli suspension (0.5 McFarland) for 60 minutes at 37°C. Following each treatment, RNA was extracted, converted to cDNA, and analysed by quantitative PCR (qPCR) to quantify T7SS expression, using the 16S rRNA gene as an internal control. Untreated or monoculture suspensions served as respective controls. Changes in T7SS expression were calculated using the delta-delta Ct (2^-ΔΔCt) method. All experiments were performed in triplicate for reproducibility. Results and discussion Detection of capsule production Capsules were identified in all 150 clinical isolates of S. aureus . The background was dark blue, featuring unstained capsules alongside magenta-red bacteria (Fig. 2 c). Our findings are consistent with a previous study by Aviles et al., which used PCR to show that all clinical strains of S. aureus have capsular polysaccharides (Echániz-Aviles et al. 2022 ). Molecular detection and quantification of the T7SS in S. aureus clinical isolates Agarose gel electrophoresis revealed the presence of the PCR-amplified esxA gene product (291 bp). Among the 150 clinical isolates, all tested positive for the esxA gene, indicating that all the S. aureus strains contained the T7SS protein complex, while the negative control, the ATCC Escherichia coli 25922 strain, displayed no band (Fig. 2 a). Subsequent RT-qPCR analysis quantified esxA expression across the clinical isolates, confirming its presence in all the samples. The amplified qPCR products were verified using agarose gel electrophoresis, which yielded a band of 198 bp (Fig. 2 b). However, the expression levels of these genes varied among the clinical isolates, with Ct values ranging from 18 to 30 (Fig. 2 d and e). The isolates were grouped by sample type, and the overall expression patterns were analysed using a heatmap (Fig. 2 f), which revealed a uniform T7SS expression across the samples, with minimal significant differences between categories. This suggests that T7SS expression is relatively consistent among the isolates. Secretion of virulence factors Among the clinical S. aureus isolates, DNase activity was the most prominently expressed, with a majority of strains producing large clearance zones. In contrast, staphyloxanthin pigment production was comparatively low across isolates. Lipase and protease exhibited similar levels of expression, typically falling into moderate production categories (Fig. 3 a-f). The predominance of DNase and protease aligns with its role in facilitating tissue invasion, evasion of neutrophil extracellular traps (Sharma et al. 2019 ), and evading the host immune response (Pietrocola et al. 2017 ), while the relatively lower pigment production may reflect strain-specific adaptations to oxidative stress (Múnera-Jaramillo et al. 2024 ). Hemolysin activity, evident by distinct hemolytic zones, supports its contribution to host membrane damage (Divyakolu et al. 2019). Overall, these findings emphasise the heterogeneity in virulence factor expression among clinical isolates and highlight the potential for DNase, protease, and lipase to contribute more significantly to pathogenicity. Correlation of the T7SS and virulence factors T7SS expression showed significant positive correlations with DNase (r = -0.314, p < 0.01), hemolysin (r = -0.423, p < 0.01), protease (r = -0.291, p < 0.05), lipase (r = -0.339, p < 0.05), and staphyloxanthin production (r = -0.188, p < 0.05). A negative sign indicates that as the cycle threshold for T7SS expression decreases, the levels of virulence factors increase. This finding suggested that a common regulatory mechanism governs the T7SS and other virulence factors. However, no correlation was found between MRSA and the T7SS (r = 0.039, p = 0.71) or between the drug resistance pattern of S. aureus and the T7SS (r = 0.012, p = 0.91), indicating that these factors operate independently. This finding contrasts with a study by Rasmi et al., which revealed that the antibiotic resistance characteristics of S. aureus were correlated with certain virulence genes, such as sea and icaA (Rasmi et al. 2022 ). Machine learning analysis of virulence factor expression The expression profiles of virulence factors were first visualised in a heatmap (Fig. 4 a, b), showing that DNase was most abundantly expressed, while lipase and protease clustered closely due to similar expression patterns. Principal Component Analysis (PCA) captured key variance (PC1: 25%, PC2: 22%), indicating that EsxA expression was distributed among other factors rather than forming discrete clusters, suggesting potential co-regulation (Fig. 4 c, d), aligning with the positive Pearson correlations. To further explore these patterns, BIRCH clustering and pairwise scatterplots were used (Fig. 5 a), revealing consistent associations between EsxA and other virulence determinants. K-means clustering of EsxA with individual factors confirmed that higher EsxA expression frequently coincided with elevated levels of protease, hemolysin, DNase, lipase, and staphyloxanthin (Fig. 5 b–f). This co-expression supports the hypothesis that the T7SS component EsxA contributes to the broader virulence landscape of S. aureus . Feature selection using Random Forest and SHAP analysis identified protease as the most influential predictor of overall expression patterns (Fig. 6 b, d). Ridge and violin plots showed unimodal distributions for lipase and protease, while DNase and hemolysin exhibited bimodal patterns, potentially reflecting differences in regulatory control or environmental adaptation (Fig. 6 a, c). To assess regulatory interactions, chord diagrams were constructed (Fig. 7 a), highlighting strong associations between EsxA and staphyloxanthin. This was further supported by line graph analysis (Fig. 7 b), indicating co-variation in their expression. These findings suggest that T7SS and staphyloxanthin biosynthesis may be co-regulated to enhance immune evasion and oxidative stress resistance. Interestingly, an inverse relationship was noted between staphyloxanthin and DNase, suggesting a potential trade-off between pigment production and nuclease activity. Finally, a Markov Chain Model illustrated dynamic transitions among virulence factors (Fig. 7 c). Lipase and DNase emerged as key hubs, with EsxA showing strong connectivity to DNase, lipase, and staphyloxanthin, highlighting its potential role in coordinating secretion-associated virulence. These network interactions support the concept that virulence determinants are not independently regulated but act within an integrated regulatory framework involving global regulators such as sigB, sarA, saeRS, and agr (Wittekind et al. 2023 ; Liu et al. 2024 ; Wen et al. 2024 ). Antibiotic susceptibility and resistance network analysis Among the 150 clinical isolates, 74% (n = 111) were identified as MRSA. In comparison, Dhungel et al. reported an 87.2% prevalence of MRSA (34 of 39 isolates) in their study (Dhungel et al. 2021 ). Furthermore, 97.4% of the isolates were classified as vancomycin-sensitive Staphylococcus aureus (VSSA), 2% were classified as vancomycin-intermediate Staphylococcus aureus (VISA), and 0.6% were classified as vancomycin-resistant Staphylococcus aureus (VRSA). This finding contrasts with that of Khanal et al., who reported 0% VRSA (0 out of 160 isolates) (Khanal et al. 2023 ), and Gwad et al., who reported a VRSA prevalence of 5.5% (12 out of 215 isolates) (Nagy et al. 2022 ). Multidrug resistance (MDR) was detected in 88% of our isolates, which is consistent with the findings of Alfeky et al., who reported that 79% (109 out of 138) of S. aureus isolates were MDR (Alfeky et al. 2022 ). Notably, we detected 0% extreme drug-resistant (XDR) S. aureus , whereas a study from Iran reported 22% (11 out of 50 isolates) as XDR (Moosavian et al. 2020 ). The overall resistance and sensitivity patterns are shown in Fig. 8 a-c. Notably, no isolates were resistant to daptomycin, nitrofurantoin, or tigecycline, while nearly all were resistant to ciprofloxacin, levofloxacin, and benzylpenicillin. Compared with prior data from 2015–2017 (31), we observed emerging resistance to teicoplanin and linezolid (Kot et al. 2020 ). Violin plots revealed bimodal distributions for trimethoprim-sulfamethoxazole, oxacillin, erythromycin, and clindamycin, while linezolid, daptomycin, tigecycline, and teicoplanin displayed narrow distributions indicative of high susceptibility (Fig. 8 d). To explore inter-drug relationships, chord analysis identified strong associations among tigecycline, trimethoprim-sulfamethoxazole, and rifampicin, and moderate interactions for vancomycin, teicoplanin, daptomycin, and linezolid. In contrast, beta-lactams and fluoroquinolones exhibited weaker pairwise correlations, reflecting more diverse resistance mechanisms (Fig. 9 a). Co-resistance network visualisation highlighted a central cluster including gentamicin, fluoroquinolones, erythromycin, clindamycin, and rifampicin, suggesting shared mechanisms such as efflux pumps, plasmid-mediated resistance, or target site modifications (Fig. 9 b). Peripheral nodes such as tigecycline and nitrofurantoin appeared isolated, supporting distinct resistance pathways. The co-occurrence heatmap (Fig. 10 a) showed frequent simultaneous resistance between ciprofloxacin and levofloxacin, with high co-resistance also involving benzylpenicillin and oxacillin. Gentamicin, clindamycin, and trimethoprim-sulfamethoxazole exhibited moderate co-occurrence, while linezolid, daptomycin, and tigecycline showed minimal overlap. The Markov transition matrix (Fig. 10 b) indicated that vancomycin, fluoroquinolones, and benzylpenicillin had the highest probabilities of transitioning to broader resistance, whereas linezolid and tigecycline maintained low transition rates. The resistance state model (Fig. 10 c) demonstrated that fully resistant strains persisted in that state (77%), while intermediate resistance often reverted to sensitivity (56%), and 13% of initially sensitive strains progressed to resistance. Network analysis (Fig. 10 d) highlighted beta-lactams and fluoroquinolones as central hubs with stable resistance, and macrolides showed frequent transitions likely mediated by shared mechanisms such as erm gene–associated target modification. Finally, the steady-state probabilities (Fig. 10 e) confirmed that resistance to beta-lactams and fluoroquinolones is persistent and widespread, whereas susceptibility to linezolid, daptomycin, and tigecycline remains largely preserved, emphasising the need for ongoing surveillance and careful antibiotic stewardship. T7SS regulation by environmental factors To assess environmental regulation of T7SS, S. aureus was exposed to UV irradiation, 0.1% sodium hypochlorite, and coculture with E. coli . UV exposure significantly decreased T7SS expression by 1.5-fold (p < 0.05), whereas sodium hypochlorite and E. coli coculture increased expression by 2.5-fold and 2.2-fold, respectively (p < 0.05) (Fig. 11 ; Supplementary Fig. 1). Our findings align with Cincarova et al., who reported increased esxA expression after chloramine T treatment (Cincarova et al. 2016 ). Similarly, Cao et al. showed that T7SS contributes to interbacterial competition by secreting nuclease toxins like EsaD (Cao et al. 2016 ), while Fila et al. found that blue light photoirradiation reduces bacterial virulence factors (Fila et al. 2017 ). As most S. aureus virulence factors are regulated by the agr system (Mahdally et al. 2021 ), we propose that agr may also control T7SS expression, supported by Schulthess et al., who demonstrated agr -mediated upregulation of esxA (Schulthess et al. 2012 ). A limitation of this study is the omission of biosynthetic gene controls when assessing environmental effects on T7SS, which could confound specific expression changes. Future research should examine the temporal dynamics of T7SS expression during growth phases to clarify its regulatory mechanism. Additionally, since the agr system has been targeted to suppress other virulence factors (Sully et al. 2014 ; Salam and Quave 2018 ; Bezar et al. 2019 ), future studies should assess whether agr inhibition also reduces T7SS expression. Conclusion This work demonstrates that pairing wet-lab microbiological assays with machine learning provides a powerful lens for decoding the hidden architecture of S. aureus virulence. Beyond confirming that T7SS expression correlates with enzymatic factors, our integrative approach revealed nuanced patterns of co-expression and highlighted protease as a key predictor of T7SS activity. Unlike traditional analyses, machine learning uncovered clusters of high-expression phenotypes and modeled resistance transitions, offering insights into how virulence traits are organised within clinical populations. Environmental modulation experiments further showed that T7SS expression is not static but dynamically shaped by environmental pressures. Together, these findings highlight the need for future strategies that target shared regulatory nodes, such as the agr system, to disrupt T7SS-associated pathogenicity. More broadly, this study illustrates how combining experimental microbiology with computational tools can accelerate discovery in bacterial pathogenesis research. Declarations Consent for publication Not applicable Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Competing interests The authors declare that they have no competing interests. Funding The authors state that no funding was received for this study. Authors contribution The manuscript was designed by Nirmala B, Balram Ji Omar, and Manju O Pai. Material preparation, data collection, methodology, and analysis were performed by Nirmala B. Supervision and validation were provided by Balram Ji Omar and Manju O Pai. Gaurav Badoni, Ganesh Kumar Verma, and Ankur Kumar contributed to the methodology. The first draft of the manuscript was written by Nirmala B, and all authors reviewed and commented on earlier versions. All authors read and approved the final manuscript. Additional text PCR cycle parameters cDNA synthesis and qPCR cycle parameters Supplementary Fig. 1 Fold change in S. aureus T7SS gene expression in response to environmental factors. Excel-Experimental Data T7SS References Alfeky AAE, Tawfick MM, Ashour MS, El-Moghazy ANA (2022) High Prevalence of Multi-drug Resistant Methicillin-Resistant Staphylococcus aureus in Tertiary Egyptian Hospitals. 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International Journal of Medical Microbiology 309 Sully EK, Malachowa N, Elmore BO, et al (2014) Selective Chemical Inhibition of agr Quorum Sensing in Staphylococcus aureus Promotes Host Defense with Minimal Impact on Resistance. PLoS Pathog 10:. https://doi.org/10.1371/journal.ppat.1004174 Wen Z, Chen C, Shang Y, et al (2024) Baohuoside I inhibits virulence of multidrug-resistant Staphylococcus aureus by targeting the transcription Staphylococcus accessory regulator factor SarZ. Phytomedicine 130:. https://doi.org/10.1016/j.phymed.2024.155590 Wittekind MA, Briaud P, Smith JL, et al (2023) The Small Protein ScrA Influences Staphylococcus aureus Virulence-Related Processes via the SaeRS System. Microbiol Spectr 11:. https://doi.org/10.1128/spectrum.05255-22 Yehia FAZA, Yousef N, Askoura M (2021) Exploring Staphylococcus aureus Virulence Factors; Special Emphasis on Staphyloxanthin. Microbiology and Biotechnology Letters 49:467–477 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFileAVL.pdf ExcelExperimentalDataT7SS.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7352746","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503555023,"identity":"c9f59bd8-29a9-4aa7-8629-02eb200b57d5","order_by":0,"name":"B Nirmala","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACxgY2ECUBYjY++ACk2NiJ18LcbDgDpIWZoD1sMAZ7mzQPiCakhbm9LfFx5Q6LaN0ZiW3SNr+2yfMxMzB++JiDx2E9xw4bnj0jkbvtRmKzdW7fbcM2ZgZmyZnb8GiZkd4m2dgG1tJ4O7fnNiNQCxszL34t7T+hWhqkLXtu2xOhJe0YI1RLkzTDj9uJhLX0HEuGOOzMw2bD3obbyW3MjM14/WLY3mb4sbGtLnfb8fSHD378uW07v7354IeP+LQ0wFgCCUA728A2N+BQDAHycBb/ASDxB6/iUTAKRsEoGKEAALgWVyN0MgkyAAAAAElFTkSuQmCC","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"B","middleName":"","lastName":"Nirmala","suffix":""},{"id":503555024,"identity":"73522de4-fff2-4b5c-bc3c-1c747ab47779","order_by":1,"name":"Manju O Pai","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Manju","middleName":"O","lastName":"Pai","suffix":""},{"id":503555026,"identity":"4ba62be4-42ba-4db3-aee5-e8604c66c0c8","order_by":2,"name":"Gaurav Badoni","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Gaurav","middleName":"","lastName":"Badoni","suffix":""},{"id":503555027,"identity":"e46c8207-fe10-41b8-b490-b783188654f9","order_by":3,"name":"Ganesh Kumar Verma","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ganesh","middleName":"Kumar","lastName":"Verma","suffix":""},{"id":503555028,"identity":"afc7a387-82a2-4c8f-beb6-2d4be9dd14a3","order_by":4,"name":"Ankur Kumar","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ankur","middleName":"","lastName":"Kumar","suffix":""},{"id":503555031,"identity":"ceae59c7-ab70-4284-be57-c569b16a78e6","order_by":5,"name":"Balram Ji Omar","email":"data:image/png;base64,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","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Balram","middleName":"Ji","lastName":"Omar","suffix":""}],"badges":[],"createdAt":"2025-08-12 07:38:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7352746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7352746/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89841487,"identity":"61d00c6d-d2ac-4815-bf3e-ebe9dde9a8b5","added_by":"auto","created_at":"2025-08-25 15:25:15","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":221037,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the hypothesis: Is the T7SS correlated with other virulence factors secreted by Staphylococcus aureus?\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/46bb937706fc61ffcbfc0b04.jpeg"},{"id":89840519,"identity":"f57a4ced-9c5c-4b81-b2e2-9a755dd2e9f1","added_by":"auto","created_at":"2025-08-25 15:17:15","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e PCR results: L – 100 bp ladder; lanes 1–7 – samples showing a positive 291 bp amplified PCR product; NC – negative control (ATCC Escherichia coli 25922); PC – positive control (Staphylococcus aureus 16S rRNA gene, 257 bp).\u003cbr\u003e\n \u003cstrong\u003e(b)\u003c/strong\u003eqPCR results: L – 100 bp ladder; lanes 1–11 – samples displaying a positive 196 bp amplified qPCR product; PC – positive control (S. aureus16S rRNA gene, 257 bp); NC – no-template control.\u003cbr\u003e\n \u003cstrong\u003e(c)\u003c/strong\u003eCapsule visualisation in S. aureus using Maneval’s stain. Unstained capsules appear as clear halos surrounding magenta-red bacterial cells against a blue background.\u003cbr\u003e\n \u003cstrong\u003e(d)\u003c/strong\u003eScatterplot showing esxA gene expression across samples, with Ct values ranging from 18 to 30.\u003cbr\u003e\n \u003cstrong\u003e(e)\u003c/strong\u003eViolin plot illustrating the distribution of esxA Ct values.\u003cbr\u003e\n \u003cstrong\u003e(f)\u003c/strong\u003eGrouping of isolates by sample type demonstrating uniform T7SS expression across categories.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/3bc4f19924cc1e114d4d6b51.jpeg"},{"id":89841488,"identity":"cc0ef138-50d3-4f7b-b9eb-65ba5b8e7eb7","added_by":"auto","created_at":"2025-08-25 15:25:15","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163548,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images illustrating virulence factor production in clinical Staphylococcus aureus isolates are shown. Panel (a) displays DNase activity on DNase agar supplemented with methyl green. Panel (b) shows hemolysin production evidenced by clear zones of hemolysis on sheep blood agar. Panel (c) demonstrates protease activity on milk agar through casein hydrolysis. Panel (d) depicts lipase production on egg-yolk agar. Panel (e) shows staphyloxanthin pigment production on nutrient agar. Panel (f) presents a heatmap summarising the overall distribution of virulence factor expression levels among the S. aureus clinical isolates, categorised as weak, moderate, and high producers.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/11c7495659958fcd64ab0840.jpeg"},{"id":89840529,"identity":"d6f3ac20-b179-4d66-9817-128684a274c9","added_by":"auto","created_at":"2025-08-25 15:17:15","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":183827,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive analysis of virulence factor expression in Staphylococcus aureus isolates.\u003cbr\u003e\n \u003cstrong\u003e(a) \u003c/strong\u003eHeatmap depicting the expression levels of DNase, EsxA, hemolysin, lipase, and protease across clinical isolates, with hierarchical clustering of both samples and factors.\u003cbr\u003e\n \u003cstrong\u003e(b) \u003c/strong\u003eDistribution of staphyloxanthin production scores among the isolates.\u003cbr\u003e\n \u003cstrong\u003e(c) \u003c/strong\u003eScree plot showing the proportion of variance explained by each principal component derived from virulence factor data.\u003cbr\u003e\n\u003cstrong\u003e(d) \u003c/strong\u003ePrincipal component analysis (PCA) scatterplot illustrating clustering patterns of isolates based on virulence factor expression.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/4dc0e106c2d3962e05080666.jpeg"},{"id":89842058,"identity":"67127326-0e0b-4234-8562-d55ef884c84f","added_by":"auto","created_at":"2025-08-25 15:33:15","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":214880,"visible":true,"origin":"","legend":"\u003cp\u003ePairwise and clustering analyses of virulence factor expression in Staphylococcus aureus isolates are shown. Panel (a) presents a scatterplot matrix displaying the distributions and pairwise relationships among lipase, protease, hemolysin, DNase, staphyloxanthin, and EsxA expression levels. Panels (b) through (f) depict K-means clustering analyses of EsxA expression with individual virulence factors: protease (b), hemolysin (c), DNase (d), lipase (e), and staphyloxanthin (f). Data points are colored by cluster membership, highlighting patterns of co-expression and clustering among the different virulence determinants.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/285046fa7e6a97b74694eeb5.jpeg"},{"id":89841493,"identity":"ed9d4781-fb15-4254-ab48-a44a2e372a29","added_by":"auto","created_at":"2025-08-25 15:25:16","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":156025,"visible":true,"origin":"","legend":"\u003cp\u003eMachine learning-based assessment of virulence factor importance and expression distributions in Staphylococcus aureus isolates.\u003cbr\u003e\n \u003cstrong\u003e(a)\u003c/strong\u003eViolin plots showing the distribution of expression values for each virulence factor across identified clusters.\u003cbr\u003e\n \u003cstrong\u003e(b)\u003c/strong\u003eSHAP value summary plot indicating the impact of individual virulence factors on model output, with color reflecting feature value magnitude.\u003cbr\u003e\n \u003cstrong\u003e(c)\u003c/strong\u003eRidge plots illustrating the distribution patterns of lipase, protease, hemolysin, DNase, staphyloxanthin, and EsxA expression.\u003cbr\u003e\n \u003cstrong\u003e(d)\u003c/strong\u003eViolin plots of feature importance scores derived from random forest analysis, highlighting protease as the most influential predictor.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/359c3aa6991860ca1e3296d4.jpeg"},{"id":89840530,"identity":"edae1037-0bad-4dc1-96cc-ae3ea882104f","added_by":"auto","created_at":"2025-08-25 15:17:15","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":251151,"visible":true,"origin":"","legend":"\u003cp\u003eVisualisation of interactions among virulence factors in Staphylococcus aureus isolates.\u003cbr\u003e\n \u003cstrong\u003e(a)\u003c/strong\u003eChord diagram illustrating the strength and direction of pairwise interactions between virulence factors. Colors indicate interaction strength (from low in purple to high in red), while the thickness of each chord reflects the magnitude of the association.\u003cbr\u003e\n \u003cstrong\u003e(b)\u003c/strong\u003eLine graph showing standardised expression values across all virulence factors, highlighting patterns of co-variation among isolates.\u003cbr\u003e\n \u003cstrong\u003e(c)\u003c/strong\u003eMarkov chain model depicting probabilistic transitions and interconnections among virulence factors, suggesting potential regulatory relationships.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/bf7378c9d3a1381df65e02b1.jpeg"},{"id":89840535,"identity":"7995d1b6-8836-4a8c-bed0-8ef7e69e6d4c","added_by":"auto","created_at":"2025-08-25 15:17:16","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":298201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisualisation of antimicrobial susceptibility patterns across \u003c/strong\u003eStaphylococcus aureus\u003cstrong\u003e isolates.\u003c/strong\u003e\u003cbr\u003e\n(a) Heatmap showing resistance, intermediate susceptibility, and sensitivity profiles for each antibiotic, with red shades indicating higher sensitivity and lighter colors representing resistance.\u003cbr\u003e\n(b) Scatterplot illustrating clustering of resistance patterns by drug, categorised into resistant, intermediate, and sensitive groups.\u003cbr\u003e\n(c) Bar plot displaying the percentage of isolates sensitive to each antibiotic.\u003cbr\u003e\n(d) Violin plots depicting the distribution of sensitivity levels for each antibiotic, highlighting variability and multimodal patterns in susceptibility.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/c42c942aa23fdd598f153f76.jpeg"},{"id":89840533,"identity":"9801f7ae-5afd-4eb1-bc17-a7d08110f324","added_by":"auto","created_at":"2025-08-25 15:17:16","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":201229,"visible":true,"origin":"","legend":"\u003cp\u003eVisualisation of antibiotic co-resistance patterns among Staphylococcus aureus isolates.\u003cbr\u003e\n \u003cstrong\u003e(a)\u003c/strong\u003eChord diagram showing pairwise interaction strengths between antibiotics, with color gradients indicating the magnitude of association (from low in purple to high in red) and thicker chords representing stronger correlations in susceptibility or resistance profiles.\u003cbr\u003e\n \u003cstrong\u003e(b)\u003c/strong\u003eCo-resistive network graph displaying the multivariate relationships among antibiotics. Nodes are colored according to the drug legend, and their proximity indicates similarity in resistance patterns.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/1c50fd6b9b421da25afd3036.jpeg"},{"id":89840539,"identity":"dd82e2f0-a1f6-4ad4-8e75-82870f6f1fa0","added_by":"auto","created_at":"2025-08-25 15:17:16","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":320313,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive analysis of antibiotic resistance co-occurrence, transition probabilities, and persistence in Staphylococcus aureus isolates.\u003cbr\u003e\n \u003cstrong\u003e(a)\u003c/strong\u003eCo-occurrence heatmap showing the frequency of simultaneous resistance between antibiotic pairs, with higher counts indicated by darker red shading.\u003cbr\u003e\n \u003cstrong\u003e(b)\u003c/strong\u003eMarkov transition matrix heatmap illustrating the probability of transitioning from resistance to one antibiotic (rows) to resistance to another (columns).\u003cbr\u003e\n \u003cstrong\u003e(c)\u003c/strong\u003eHeatmap of resistance state transitions, indicating probabilities of moving between sensitive, intermediate, and resistant states.\u003cbr\u003e\n \u003cstrong\u003e(d)\u003c/strong\u003eNetwork graph depicting the relationships and transition pathways among antibiotic resistances; node size and self-loops represent the persistence and interconnectedness of resistance traits.\u003cbr\u003e\n \u003cstrong\u003e(e)\u003c/strong\u003eBar plot of steady-state probabilities, reflecting the long-term likelihood of resistance persisting to each antibiotic.\u003c/p\u003e","description":"","filename":"image10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/a62b51b23d35d11d288c1d39.jpeg"},{"id":89840547,"identity":"6fc5d8fd-4df9-4780-908c-9458ae54dccb","added_by":"auto","created_at":"2025-08-25 15:17:16","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":187733,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of environmental treatments on T7SS expression in Staphylococcus aureus. (1) UV irradiation, which led to downregulation of esxA expression and reduced T7SS activity; (2) exposure to 0.1% sodium hypochlorite, resulting in upregulation of T7SS expression; and (3) coculture with Escherichia coli, which also induced upregulation of T7SS expression.\u003c/p\u003e","description":"","filename":"image11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/ed082ec5ffe008e627d8d8f2.jpeg"},{"id":91764530,"identity":"777f89d1-fc84-43d3-aece-94fb4d01537d","added_by":"auto","created_at":"2025-09-20 11:01:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2986667,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/0dc87911-fd04-46d6-99f9-d93bd8fa1f55.pdf"},{"id":89840523,"identity":"4cb29620-1bfb-4bd2-b836-7f1f0780167d","added_by":"auto","created_at":"2025-08-25 15:17:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":230069,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileAVL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/f27c5b1136b06ff95b286ae9.pdf"},{"id":89840525,"identity":"3a9d62ec-022e-4994-9e07-ed1393316056","added_by":"auto","created_at":"2025-08-25 15:17:15","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":142862,"visible":true,"origin":"","legend":"","description":"","filename":"ExcelExperimentalDataT7SS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7352746/v1/b414eed000b68446c6019f02.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine learning analysis of type VII secretion system expression and its relationships with virulence traits and antibiotic resistance in Staphylococcus aureus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite decades of research, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e continues to surprise clinicians and scientists with its remarkable ability to exploit an arsenal of virulence factors to invade tissues (Cheung et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), resist antibiotics (Ezeh et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and evade the immune system(Pelz et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Yehia et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bear et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This Gram-positive pathogen, prioritised by the WHO for its public health impact (Sharaf et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), is notorious for causing infections that range from harmless skin lesions to life-threatening pneumonia and endocarditis (Cheung et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The challenge is magnified by methicillin-resistant strains (MRSA), which compound virulence with multidrug resistance (Hassoun et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Among these, the type VII secretion system (T7SS) has emerged as a critical weapon (Bowman and Palmer \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), yet its connections to other virulence traits remain poorly understood.\u003c/p\u003e\u003cp\u003eThe T7SS, also known as the ESAT-6-like secretion system (ESS), is encoded by the \u003cem\u003eess\u003c/em\u003e locus and comprises multiple transmembrane components and secreted effectors, including EsxA, which serves as a marker of T7SS activity (Burts et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Dai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). T7SS contributes to infection persistence by lysing neutrophils (Dai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), protecting against antimicrobial fatty acids (Kengmo Tchoupa et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), facilitating antibacterial competition (Garrett and Palmer \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and modulating host immunity (Cruciani et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While the T7SS is a recognised virulence determinant, its relationships with other virulence factors and regulatory systems such as agr, SaeRS, and SarA remain incompletely understood (Jenul and Horswill \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bezar et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wittekind et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo date, no prior study has investigated how T7SS expression correlates with other virulence factors in \u003cem\u003eS. aureus\u003c/em\u003e or how environmental stressors might modulate T7SS. We hypothesised that T7SS expression is associated with the levels of key virulence factors (DNase, hemolysin, protease, lipase, staphyloxanthin) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and influenced by physical (UV irradiation), chemical (sodium hypochlorite), and biological (coculture with \u003cem\u003eEscherichia coli\u003c/em\u003e) exposure. Traditional analytical methods often fail to capture the complex interdependencies among virulence traits and resistance profiles. Therefore, this study integrated wet lab microbiology with machine learning approaches to systematically characterise co-expression patterns, quantify feature importance in predicting T7SS activity, and model resistance dynamics. Using this combined methodology, we aimed to elucidate the regulatory landscape of T7SS and its association with virulence and antibiotic resistance in clinical \u003cem\u003eS. aureus\u003c/em\u003e isolates.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eBacterial collection, identification, and antibiotic susceptibility testing\u003c/p\u003e\u003cp\u003eThis study was conducted in a tertiary care setting and included 150 clinical \u003cem\u003eS. aureus\u003c/em\u003e isolates collected at AIIMS Rishikesh between September 2023 and January 2025 from diverse specimens (pus, blood, tissue, sputum, urine, and body fluids). Control strains comprised \u003cem\u003eS. aureus\u003c/em\u003e ATCC 25923, \u003cem\u003eE. coli\u003c/em\u003e ATCC 25922, and the T7SS-positive \u003cem\u003eS. aureus\u003c/em\u003e RN6390 (New York University School of Medicine). Isolates were identified using blood agar culture, standard biochemical tests, the VITEK2 system (BioM\u0026eacute;rieux), and MALDI-TOF (Bruker Biotyper), then preserved in glycerol stocks at \u0026minus;\u0026thinsp;20\u0026deg;C and \u0026minus;\u0026thinsp;80\u0026deg;C and maintained on nutrient agar at 4\u0026deg;C.\u003c/p\u003e\u003cp\u003eMethicillin resistance was determined by cefoxitin disc diffusion (30 \u0026micro;g) on Mueller-Hinton agar following CLSI (2019) criteria, with the USA 600 MRSA strain as a positive control. Antibiotic susceptibility was assessed using the VITEK\u0026reg; 2 Compact system with AST p628 cards. The Institutional Ethics Committee approved the study (Letter No: AIIMS/IEC/23/294).\u003c/p\u003e\u003cp\u003eDetection and quantification of the T7SS in clinical isolates of \u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e\u003cp\u003eGenomic DNA and total RNA were extracted from 24-hour blood agar cultures of \u003cem\u003eS. aureus\u003c/em\u003e using HiPurA purification kits (HIMEDIA, India). Purity was assessed by agarose gel electrophoresis and NanoQuant spectrophotometry (Tecan Life Sciences). PCR amplification of the \u003cem\u003eesxA\u003c/em\u003e gene was performed in 25 \u0026micro;l reactions containing Emerald dAMP GT Master Mix (Gene to Protein Pvt. Ltd.) and specific primers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) using an Applied Biosystems Veriti thermal cycler. \u003cem\u003eS. aureus\u003c/em\u003e 16S rRNA served as the positive control, and \u003cem\u003eE. coli\u003c/em\u003e ATCC 25922 as the negative control.\u003c/p\u003e\u003cp\u003eFor qPCR, RNA was reverse-transcribed to cDNA with G-Biosciences reagents. Reactions (20 \u0026micro;l) containing SYBR\u0026trade; Green qPCR Master Mix were run on a Bio-Rad CFX96 system under standard cycling conditions. Amplicon specificity for both DNA and RNA analyses was confirmed by gel electrophoresis. Full protocols and thermal profiles are available in Supplementary Methods.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimer sequences utilised in this study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimer Sequence (5\u0026prime;-3\u0026prime;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProduct Length (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePurpose\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eesxA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCAATGATTAAGATGAGTCCAGAGG\u003c/p\u003e\u003cp\u003eTTATTGCAAACCGAAATTATTAGAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePCR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eesxA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTTACGGGCAAGGTTCAGACC\u003c/p\u003e\u003cp\u003eACAGCGTCAGCAGTGCTATT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eqPCR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16 srRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACGGTCTTGCTGTCACTTATA\u003c/p\u003e\u003cp\u003eTACACATATGTTCTTCCCTAATAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInternal control\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDetection of \u003cem\u003eS. aureus\u003c/em\u003e virulence factors\u003c/p\u003e\u003cp\u003eColonies from 24-hour blood agar cultures were suspended in 0.9% saline (0.5 McFarland standard). DNase activity was tested on DNase agar with methyl green (HIMEDIA\u0026reg;); hemolysin on sheep blood agar (BioM\u0026eacute;rieux); protease on milk agar (HIMEDIA\u0026reg;); and lipase on egg yolk agar (10% egg yolk in nutrient agar). Plates were incubated at 37\u0026deg;C for 24 hours. DNase and lipase were indicated by clearance zones, hemolysin by lysis, and protease by precipitation. Zone sizes categorised production as weak (\u0026le;\u0026thinsp;6 mm), moderate (7\u0026ndash;8 mm), or high (\u0026ge;\u0026thinsp;9 mm). Staphyloxanthin was assessed on nutrient agar based on pigment intensity.\u003c/p\u003e\u003cp\u003eCapsule detection was performed using Maneval\u0026rsquo;s stain as previously described (B et al. 2024), with visualisation under oil immersion microscopy.\u003c/p\u003e\u003cp\u003eCorrelations between the T7SS and other virulence factors of \u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSample size (n\u0026thinsp;=\u0026thinsp;84) was calculated in G*Power to achieve 80% power with 95% confidence. The T7SS Ct values were correlated with virulence factor zone sizes by bivariate analysis in IBM SPSS v21.0 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); as lower Ct indicates higher expression, negative correlations reflect positive associations. Furthermore, T7SS expression was correlated with MRSA and drug resistance patterns.\u003c/p\u003e\u003cp\u003eMachine learning, dimensionality reduction, and resistance network analyses\u003c/p\u003e\u003cp\u003eAll data were normalised using a standard scaler before analysis. Machine learning analyses were conducted in Python v3.9. For dimensionality reduction and visualisation, Principal Component Analysis (PCA) was applied to capture variance in virulence factor expression, supported by scree plots and feature distributions to identify clustering and co-regulation patterns. Clustering analysis employed BIRCH pairplot visualisation and K-means clustering to explore whether elevated EsxA expression correlated with other virulence factors.\u003c/p\u003e\u003cp\u003eRandom Forest and SHapley Additive exPlanations (SHAP) analysis were conducted to quantify feature importance and rank contributions to EsxA expression, while Ridge regression examined the distribution of virulence factor levels. Interaction mapping involved constructing chord diagrams and line graphs to assess co-regulation among factors. A Markov Chain Model was developed to analyse transition dynamics based on feature co-occurrence.\u003c/p\u003e\u003cp\u003eFor the antibiotic susceptibility data of 150 \u003cem\u003eS. aureus\u003c/em\u003e isolates, preprocessing and normalisation were performed to ensure consistency across variables. Heatmaps were used to visualise patterns of resistance, intermediate susceptibility, and sensitivity. Scatterplot clustering was applied to identify distinct resistance profiles. Violin plots were created to examine the distribution of resistance levels across antibiotics and assess the presence of mixed or bimodal resistance patterns. Chord diagrams were employed to map interactions and co-occurrence relationships among antibiotic resistance traits. A co-resistance network was generated in Gephi, with nodes representing antibiotics and edges denoting significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Dark grey edges indicated strong co-resistance. Finally, a Markov transition model, using maximum likelihood estimation, evaluated the probabilities of resistance evolution between antibiotics\u003c/p\u003e\u003cp\u003eRegulation of the T7SS by environmental factors\u003c/p\u003e\u003cp\u003eFor assessment of physical stress, \u003cem\u003eS. aureus\u003c/em\u003e colonies were cultured on blood agar and incubated overnight at 37\u0026deg;C. Plates were then exposed to UV irradiation using a 15-watt 254 nm UV lamp for 0, 10, 30, 60, or 120 minutes. For chemical treatment, bacterial suspensions adjusted to the 0.5 McFarland standard in 0.9% normal saline were treated with 0.1% sodium hypochlorite and incubated at 37\u0026deg;C for 60 minutes. For biological stress, \u003cem\u003eS. aureus\u003c/em\u003e suspensions (0.5 McFarland) were cocultured with an equal volume of \u003cem\u003eE. coli\u003c/em\u003e suspension (0.5 McFarland) for 60 minutes at 37\u0026deg;C.\u003c/p\u003e\u003cp\u003eFollowing each treatment, RNA was extracted, converted to cDNA, and analysed by quantitative PCR (qPCR) to quantify T7SS expression, using the 16S rRNA gene as an internal control. Untreated or monoculture suspensions served as respective controls. Changes in T7SS expression were calculated using the delta-delta Ct (2^-ΔΔCt) method. All experiments were performed in triplicate for reproducibility.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eDetection of capsule production\u003c/p\u003e\n\u003cp\u003eCapsules were identified in all 150 clinical isolates of \u003cem\u003eS. aureus\u003c/em\u003e. The background was dark blue, featuring unstained capsules alongside magenta-red bacteria (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec). Our findings are consistent with a previous study by Aviles et al., which used PCR to show that all clinical strains of \u003cem\u003eS. aureus\u003c/em\u003e have capsular polysaccharides (Ech\u0026aacute;niz-Aviles et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMolecular detection and quantification of the T7SS in \u003cem\u003eS. aureus\u003c/em\u003e clinical isolates\u003c/p\u003e\n\u003cp\u003eAgarose gel electrophoresis revealed the presence of the PCR-amplified esxA gene product (291 bp). Among the 150 clinical isolates, all tested positive for the esxA gene, indicating that all the \u003cem\u003eS. aureus\u003c/em\u003e strains contained the T7SS protein complex, while the negative control, the ATCC \u003cem\u003eEscherichia coli\u003c/em\u003e 25922 strain, displayed no band (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). Subsequent RT-qPCR analysis quantified esxA expression across the clinical isolates, confirming its presence in all the samples. The amplified qPCR products were verified using agarose gel electrophoresis, which yielded a band of 198 bp (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). However, the expression levels of these genes varied among the clinical isolates, with Ct values ranging from 18 to 30 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed and e). The isolates were grouped by sample type, and the overall expression patterns were analysed using a heatmap (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef), which revealed a uniform T7SS expression across the samples, with minimal significant differences between categories. This suggests that T7SS expression is relatively consistent among the isolates.\u003c/p\u003e\n\u003cp\u003eSecretion of virulence factors\u003c/p\u003e\n\u003cp\u003eAmong the clinical \u003cem\u003eS. aureus\u003c/em\u003e isolates, DNase activity was the most prominently expressed, with a majority of strains producing large clearance zones. In contrast, staphyloxanthin pigment production was comparatively low across isolates. Lipase and protease exhibited similar levels of expression, typically falling into moderate production categories (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea-f). The predominance of DNase and protease aligns with its role in facilitating tissue invasion, evasion of neutrophil extracellular traps (Sharma et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), and evading the host immune response (Pietrocola et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), while the relatively lower pigment production may reflect strain-specific adaptations to oxidative stress (M\u0026uacute;nera-Jaramillo et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hemolysin activity, evident by distinct hemolytic zones, supports its contribution to host membrane damage (Divyakolu et al. 2019). Overall, these findings emphasise the heterogeneity in virulence factor expression among clinical isolates and highlight the potential for DNase, protease, and lipase to contribute more significantly to pathogenicity.\u003c/p\u003e\n\u003cp\u003eCorrelation of the T7SS and virulence factors\u003c/p\u003e\n\u003cp\u003eT7SS expression showed significant positive correlations with DNase (r = -0.314, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), hemolysin (r = -0.423, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), protease (r = -0.291, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), lipase (r = -0.339, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and staphyloxanthin production (r = -0.188, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A negative sign indicates that as the cycle threshold for T7SS expression decreases, the levels of virulence factors increase. This finding suggested that a common regulatory mechanism governs the T7SS and other virulence factors. However, no correlation was found between MRSA and the T7SS (r\u0026thinsp;=\u0026thinsp;0.039, p\u0026thinsp;=\u0026thinsp;0.71) or between the drug resistance pattern of \u003cem\u003eS. aureus\u003c/em\u003e and the T7SS (r\u0026thinsp;=\u0026thinsp;0.012, p\u0026thinsp;=\u0026thinsp;0.91), indicating that these factors operate independently. This finding contrasts with a study by Rasmi et al., which revealed that the antibiotic resistance characteristics of \u003cem\u003eS. aureus\u003c/em\u003e were correlated with certain virulence genes, such as sea and icaA (Rasmi et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMachine learning analysis of virulence factor expression\u003c/p\u003e\n\u003cp\u003eThe expression profiles of virulence factors were first visualised in a heatmap (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, b), showing that DNase was most abundantly expressed, while lipase and protease clustered closely due to similar expression patterns. Principal Component Analysis (PCA) captured key variance (PC1: 25%, PC2: 22%), indicating that EsxA expression was distributed among other factors rather than forming discrete clusters, suggesting potential co-regulation (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec, d), aligning with the positive Pearson correlations.\u003c/p\u003e\n\u003cp\u003eTo further explore these patterns, BIRCH clustering and pairwise scatterplots were used (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea), revealing consistent associations between EsxA and other virulence determinants. K-means clustering of EsxA with individual factors confirmed that higher EsxA expression frequently coincided with elevated levels of protease, hemolysin, DNase, lipase, and staphyloxanthin (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb\u0026ndash;f). This co-expression supports the hypothesis that the T7SS component EsxA contributes to the broader virulence landscape of \u003cem\u003eS. aureus\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eFeature selection using Random Forest and SHAP analysis identified protease as the most influential predictor of overall expression patterns (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb, d). Ridge and violin plots showed unimodal distributions for lipase and protease, while DNase and hemolysin exhibited bimodal patterns, potentially reflecting differences in regulatory control or environmental adaptation (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, c).\u003c/p\u003e\n\u003cp\u003eTo assess regulatory interactions, chord diagrams were constructed (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea), highlighting strong associations between EsxA and staphyloxanthin. This was further supported by line graph analysis (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb), indicating co-variation in their expression. These findings suggest that T7SS and staphyloxanthin biosynthesis may be co-regulated to enhance immune evasion and oxidative stress resistance. Interestingly, an inverse relationship was noted between staphyloxanthin and DNase, suggesting a potential trade-off between pigment production and nuclease activity.\u003c/p\u003e\n\u003cp\u003eFinally, a Markov Chain Model illustrated dynamic transitions among virulence factors (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ec). Lipase and DNase emerged as key hubs, with EsxA showing strong connectivity to DNase, lipase, and staphyloxanthin, highlighting its potential role in coordinating secretion-associated virulence. These network interactions support the concept that virulence determinants are not independently regulated but act within an integrated regulatory framework involving global regulators such as sigB, sarA, saeRS, and agr (Wittekind et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wen et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAntibiotic susceptibility and resistance network analysis\u003c/p\u003e\n\u003cp\u003eAmong the 150 clinical isolates, 74% (n\u0026thinsp;=\u0026thinsp;111) were identified as MRSA. In comparison, Dhungel et al. reported an 87.2% prevalence of MRSA (34 of 39 isolates) in their study (Dhungel et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, 97.4% of the isolates were classified as vancomycin-sensitive \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (VSSA), 2% were classified as vancomycin-intermediate \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (VISA), and 0.6% were classified as vancomycin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (VRSA). This finding contrasts with that of Khanal et al., who reported 0% VRSA (0 out of 160 isolates) (Khanal et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), and Gwad et al., who reported a VRSA prevalence of 5.5% (12 out of 215 isolates) (Nagy et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Multidrug resistance (MDR) was detected in 88% of our isolates, which is consistent with the findings of Alfeky et al., who reported that 79% (109 out of 138) of \u003cem\u003eS. aureus\u003c/em\u003e isolates were MDR (Alfeky et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, we detected 0% extreme drug-resistant (XDR) \u003cem\u003eS. aureus\u003c/em\u003e, whereas a study from Iran reported 22% (11 out of 50 isolates) as XDR (Moosavian et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe overall resistance and sensitivity patterns are shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea-c. Notably, no isolates were resistant to daptomycin, nitrofurantoin, or tigecycline, while nearly all were resistant to ciprofloxacin, levofloxacin, and benzylpenicillin. Compared with prior data from 2015\u0026ndash;2017 (31), we observed emerging resistance to teicoplanin and linezolid (Kot et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eViolin plots revealed bimodal distributions for trimethoprim-sulfamethoxazole, oxacillin, erythromycin, and clindamycin, while linezolid, daptomycin, tigecycline, and teicoplanin displayed narrow distributions indicative of high susceptibility (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ed).\u003c/p\u003e\n\u003cp\u003eTo explore inter-drug relationships, chord analysis identified strong associations among tigecycline, trimethoprim-sulfamethoxazole, and rifampicin, and moderate interactions for vancomycin, teicoplanin, daptomycin, and linezolid. In contrast, beta-lactams and fluoroquinolones exhibited weaker pairwise correlations, reflecting more diverse resistance mechanisms (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea). Co-resistance network visualisation highlighted a central cluster including gentamicin, fluoroquinolones, erythromycin, clindamycin, and rifampicin, suggesting shared mechanisms such as efflux pumps, plasmid-mediated resistance, or target site modifications (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eb). Peripheral nodes such as tigecycline and nitrofurantoin appeared isolated, supporting distinct resistance pathways.\u003c/p\u003e\n\u003cp\u003eThe co-occurrence heatmap (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ea) showed frequent simultaneous resistance between ciprofloxacin and levofloxacin, with high co-resistance also involving benzylpenicillin and oxacillin. Gentamicin, clindamycin, and trimethoprim-sulfamethoxazole exhibited moderate co-occurrence, while linezolid, daptomycin, and tigecycline showed minimal overlap. The Markov transition matrix (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eb) indicated that vancomycin, fluoroquinolones, and benzylpenicillin had the highest probabilities of transitioning to broader resistance, whereas linezolid and tigecycline maintained low transition rates. The resistance state model (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ec) demonstrated that fully resistant strains persisted in that state (77%), while intermediate resistance often reverted to sensitivity (56%), and 13% of initially sensitive strains progressed to resistance. Network analysis (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ed) highlighted beta-lactams and fluoroquinolones as central hubs with stable resistance, and macrolides showed frequent transitions likely mediated by shared mechanisms such as \u003cem\u003eerm\u003c/em\u003e gene\u0026ndash;associated target modification. Finally, the steady-state probabilities (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ee) confirmed that resistance to beta-lactams and fluoroquinolones is persistent and widespread, whereas susceptibility to linezolid, daptomycin, and tigecycline remains largely preserved, emphasising the need for ongoing surveillance and careful antibiotic stewardship.\u003c/p\u003e\n\u003cp\u003eT7SS regulation by environmental factors\u003c/p\u003e\n\u003cp\u003eTo assess environmental regulation of T7SS, \u003cem\u003eS. aureus\u003c/em\u003e was exposed to UV irradiation, 0.1% sodium hypochlorite, and coculture with \u003cem\u003eE. coli\u003c/em\u003e. UV exposure significantly decreased T7SS expression by 1.5-fold (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas sodium hypochlorite and \u003cem\u003eE. coli\u003c/em\u003e coculture increased expression by 2.5-fold and 2.2-fold, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e; Supplementary Fig. 1). Our findings align with Cincarova et al., who reported increased \u003cem\u003eesxA\u003c/em\u003e expression after chloramine T treatment (Cincarova et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, Cao et al. showed that T7SS contributes to interbacterial competition by secreting nuclease toxins like EsaD (Cao et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), while Fila et al. found that blue light photoirradiation reduces bacterial virulence factors (Fila et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). As most \u003cem\u003eS. aureus\u003c/em\u003e virulence factors are regulated by the \u003cem\u003eagr\u003c/em\u003e system (Mahdally et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), we propose that \u003cem\u003eagr\u003c/em\u003e may also control T7SS expression, supported by Schulthess et al., who demonstrated \u003cem\u003eagr\u003c/em\u003e-mediated upregulation of \u003cem\u003eesxA\u003c/em\u003e (Schulthess et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA limitation of this study is the omission of biosynthetic gene controls when assessing environmental effects on T7SS, which could confound specific expression changes. Future research should examine the temporal dynamics of T7SS expression during growth phases to clarify its regulatory mechanism. Additionally, since the agr system has been targeted to suppress other virulence factors (Sully et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Salam and Quave \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bezar et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), future studies should assess whether agr inhibition also reduces T7SS expression.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis work demonstrates that pairing wet-lab microbiological assays with machine learning provides a powerful lens for decoding the hidden architecture of \u003cem\u003eS. aureus\u003c/em\u003e virulence. Beyond confirming that T7SS expression correlates with enzymatic factors, our integrative approach revealed nuanced patterns of co-expression and highlighted protease as a key predictor of T7SS activity. Unlike traditional analyses, machine learning uncovered clusters of high-expression phenotypes and modeled resistance transitions, offering insights into how virulence traits are organised within clinical populations. Environmental modulation experiments further showed that T7SS expression is not static but dynamically shaped by environmental pressures. Together, these findings highlight the need for future strategies that target shared regulatory nodes, such as the agr system, to disrupt T7SS-associated pathogenicity. More broadly, this study illustrates how combining experimental microbiology with computational tools can accelerate discovery in bacterial pathogenesis research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors state that no funding was received for this study.\u003c/p\u003e\n\u003cp\u003eAuthors contribution\u003c/p\u003e\n\u003cp\u003eThe manuscript was designed by Nirmala B, Balram Ji Omar, and Manju O Pai. Material preparation, data collection, methodology, and analysis were performed by Nirmala B. Supervision and validation were provided by Balram Ji Omar and Manju O Pai. Gaurav Badoni, Ganesh Kumar Verma, and Ankur Kumar contributed to the methodology. The first draft of the manuscript was written by Nirmala B, and all authors reviewed and commented on earlier versions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAdditional text\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePCR cycle parameters\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ecDNA synthesis and qPCR cycle parameters\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSupplementary Fig. 1 Fold change in S. aureus T7SS gene expression in response to environmental factors.\u003c/li\u003e\n \u003cli\u003eExcel-Experimental Data T7SS\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlfeky AAE, Tawfick MM, Ashour MS, El-Moghazy ANA (2022) High Prevalence of Multi-drug Resistant Methicillin-Resistant Staphylococcus aureus in Tertiary Egyptian Hospitals. J Infect Dev Ctries 16:795\u0026ndash;806. https://doi.org/10.3855/jidc.15833\u003c/li\u003e\n\u003cli\u003eB N, Manhas PL, Jadli M, et al (2024) A novel dual-staining method for cost-effective visualization and differentiation of microbial biofilms. 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Sci Rep 9:. https://doi.org/10.1038/s41598-019-42853-z\u003c/li\u003e\n\u003cli\u003eBowman L, Palmer T (2021) The Type VII Secretion System of Staphylococcus. https://doi.org/10.1146/annurev-micro-012721\u003c/li\u003e\n\u003cli\u003eBurts ML, Williams WA, Debord K, Missiakas DM (2005) EsxA and EsxB are secreted by an ESAT-6-like system that is required for the pathogenesis of Staphylococcus aureus infections\u003c/li\u003e\n\u003cli\u003eCao Z, Casabona MG, Kneuper H, et al (2016) The type VII secretion system of Staphylococcus aureus secretes a nuclease toxin that targets competitor bacteria. Nat Microbiol 2:. https://doi.org/10.1038/nmicrobiol.2016.183\u003c/li\u003e\n\u003cli\u003eCheung GYC, Bae JS, Otto M (2021) Pathogenicity and virulence of Staphylococcus aureus. 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Adv Infect Dis 09:80\u0026ndash;104. https://doi.org/10.4236/aid.2019.92007\u003c/li\u003e\n\u003cli\u003eEch\u0026aacute;niz-Aviles G, Velazquez-Meza ME, Rodr\u0026iacute;guez-Arvizu B, et al (2022) Detection of capsular genotypes of methicillin-resistant Staphylococcus aureus and clonal distribution of the cap5 and cap8 genes in clinical isolates. Arch Microbiol 204:. https://doi.org/10.1007/s00203-022-02793-1\u003c/li\u003e\n\u003cli\u003eEzeh CK, Eze CN, Dibua MEU, Emencheta SC (2023) A meta-analysis on the prevalence of resistance of Staphylococcus aureus to different antibiotics in Nigeria. Antimicrob Resist Infect Control 12\u003c/li\u003e\n\u003cli\u003eFila G, Kawiak A, Grinholc MS (2017) Blue light treatment of pseudomonas aeruginosa: Strong bactericidal activity, synergism with antibiotics and inactivation of virulence factors. Virulence 8:938\u0026ndash;958. https://doi.org/10.1080/21505594.2016.1250995\u003c/li\u003e\n\u003cli\u003eGarrett SR, Palmer T (2024) The role of proteinaceous toxins secreted by Staphylococcus aureus in interbacterial competition. FEMS Microbes 5\u003c/li\u003e\n\u003cli\u003eHassoun A, Linden PK, Friedman B (2017) Incidence, prevalence, and management of MRSA bacteremia across patient populations-a review of recent developments in MRSA management and treatment. Crit Care 21:211\u003c/li\u003e\n\u003cli\u003eJenul C, Horswill AR (2019) Regulation of Staphylococcus aureus Virulence . Microbiol Spectr 7:. https://doi.org/10.1128/microbiolspec.gpp3-0031-2018\u003c/li\u003e\n\u003cli\u003eKengmo Tchoupa A, Watkins KE, Jones RA, et al (2020) The type VII secretion system protects Staphylococcus aureus against antimicrobial host fatty acids. Sci Rep 10:. https://doi.org/10.1038/s41598-020-71653-z\u003c/li\u003e\n\u003cli\u003eKhanal LK, Sah AK, Adhikari RP, et al (2023) Prevalence and molecular characterization of methicillin resistant Staphylococcus aureus (MRSA) and vancomycin resistant Staphylococcus aureus (VRSA) in a tertiary care hospital. Nepal Medical College Journal 25:32\u0026ndash;37. https://doi.org/10.3126/nmcj.v25i1.53373\u003c/li\u003e\n\u003cli\u003eKot B, Wierzchowska K, Piechota M, Gruzewska A (2020) Antimicrobial Resistance Patterns in Methicillin-Resistant Staphylococcus aureus from Patients Hospitalized during 2015-2017 in Hospitals in Poland. Medical Principles and Practice 29:61\u0026ndash;68. https://doi.org/10.1159/000501788\u003c/li\u003e\n\u003cli\u003eLiu X, Wang Y, Chang W, et al (2024) AgrA directly binds to the promoter of vraSR and downregulates its expression in Staphylococcus aureus. Antimicrob Agents Chemother 68:. https://doi.org/10.1128/aac.00893-23\u003c/li\u003e\n\u003cli\u003eMahdally NH, George RF, Kashef MT, et al (2021) Staquorsin: A Novel Staphylococcus aureus Agr-Mediated Quorum Sensing Inhibitor Impairing Virulence in vivo Without Notable Resistance Development. Front Microbiol 12:. https://doi.org/10.3389/fmicb.2021.700494\u003c/li\u003e\n\u003cli\u003eMoosavian M, Dehkordi PB, Hashemzadeh M (2020) Characterization of sccmec, spa types and multidrug resistant of methicillin-resistant staphylococcus aureus isolates in ahvaz, iran. Infect Drug Resist 13:1033\u0026ndash;1044. https://doi.org/10.2147/IDR.S244896\u003c/li\u003e\n\u003cli\u003eM\u0026uacute;nera-Jaramillo J, L\u0026oacute;pez GD, Suesca E, et al (2024) The role of staphyloxanthin in the regulation of membrane biophysical properties in Staphylococcus aureus. Biochim Biophys Acta Biomembr 1866:. https://doi.org/10.1016/j.bbamem.2024.184288\u003c/li\u003e\n\u003cli\u003eNagy A, Gwad IA, Al-Ghareeb KA, et al (2022) PREVALENCE OF VANCOMYCIN RESISTANT STAPHYLOCOCCUS AUREUS (VRSA) IN SOME EGYPTIAN HOSPITALS\u003c/li\u003e\n\u003cli\u003ePelz A, Wieland KP, Putzbach K, et al (2005) Structure and biosynthesis of staphyloxanthin from Staphylococcus aureus. Journal of Biological Chemistry 280:32493\u0026ndash;32498. https://doi.org/10.1074/JBC.M505070200\u003c/li\u003e\n\u003cli\u003ePietrocola G, Nobile G, Rindi S, Speziale P (2017) Staphylococcus aureus manipulates innate immunity through own and host-expressed proteases. Front Cell Infect Microbiol 7\u003c/li\u003e\n\u003cli\u003eRasmi AH, Ahmed EF, Darwish AMA, Gad GFM (2022) Virulence genes distributed among Staphylococcus aureus causing wound infections and their correlation to antibiotic resistance. BMC Infect Dis 22:. https://doi.org/10.1186/s12879-022-07624-8\u003c/li\u003e\n\u003cli\u003eSalam AM, Quave CL (2018) Targeting Virulence in Staphylococcus aureus by Chemical Inhibition of the Accessory Gene Regulator System In Vivo . mSphere 3:. https://doi.org/10.1128/msphere.00500-17\u003c/li\u003e\n\u003cli\u003eSchulthess B, Bloes DA, Berger-B\u0026auml;chi B (2012) Opposing roles of \u0026sigma; b and \u0026sigma; b-controlled SpoVG in the global regulation of esxA in Staphylococcus aureus. BMC Microbiol 12:. https://doi.org/10.1186/1471-2180-12-17\u003c/li\u003e\n\u003cli\u003eSharaf MH, El-Sherbiny GM, Moghannem SA, et al (2021) New combination approaches to combat methicillin-resistant Staphylococcus aureus (MRSA). Sci Rep 11:. https://doi.org/10.1038/s41598-021-82550-4\u003c/li\u003e\n\u003cli\u003eSharma P, Garg N, Sharma A, et al (2019) Nucleases of bacterial pathogens as virulence factors, therapeutic targets and diagnostic markers. International Journal of Medical Microbiology 309\u003c/li\u003e\n\u003cli\u003eSully EK, Malachowa N, Elmore BO, et al (2014) Selective Chemical Inhibition of agr Quorum Sensing in Staphylococcus aureus Promotes Host Defense with Minimal Impact on Resistance. PLoS Pathog 10:. https://doi.org/10.1371/journal.ppat.1004174\u003c/li\u003e\n\u003cli\u003eWen Z, Chen C, Shang Y, et al (2024) Baohuoside I inhibits virulence of multidrug-resistant Staphylococcus aureus by targeting the transcription Staphylococcus accessory regulator factor SarZ. Phytomedicine 130:. https://doi.org/10.1016/j.phymed.2024.155590\u003c/li\u003e\n\u003cli\u003eWittekind MA, Briaud P, Smith JL, et al (2023) The Small Protein ScrA Influences Staphylococcus aureus Virulence-Related Processes via the SaeRS System. Microbiol Spectr 11:. https://doi.org/10.1128/spectrum.05255-22\u003c/li\u003e\n\u003cli\u003eYehia FAZA, Yousef N, Askoura M (2021) Exploring Staphylococcus aureus Virulence Factors; Special Emphasis on Staphyloxanthin. Microbiology and Biotechnology Letters 49:467\u0026ndash;477\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Antibiotic resistance, Machine learning, Staphylococcus aureus, Type VII secretion system, Virulence factors","lastPublishedDoi":"10.21203/rs.3.rs-7352746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7352746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEven after decades of scrutiny, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e refuses to yield all its secrets. At the heart of its virulence lies the type VII secretion system (T7SS)\u0026mdash;a molecular weapon whose alliances with other pathogenic traits remain largely uncharted. Here, we combined wet-lab microbiology with machine learning to dissect the networks linking T7SS to enzymatic virulence factors, antibiotic resistance, and environmental triggers. We quantified associations with DNase, hemolysin, protease, lipase, staphyloxanthin and assessed modulation by physical (ultraviolet light), chemical (disinfectant), and biological (coculture with \u003cem\u003eEscherichia coli\u003c/em\u003e) factors. Among 150 clinical \u003cem\u003eS. aureus\u003c/em\u003e isolates, 74% were methicillin-resistant, 0.6% vancomycin-resistant, and multidrug resistance observed in 88%. T7SS expression showed positive correlations with virulence factors but no association with MRSA status or antibiotic resistance. Machine learning analyses identified protease as the strongest predictor of T7SS expression and revealed clustering of high-expression phenotypes, highlighting complex interdependencies often overlooked by conventional approaches. Environmental factors significantly influenced T7SS expression: UV light downregulated it 1.5-fold, while sodium hypochlorite and \u003cem\u003eE. coli\u003c/em\u003e coculture upregulated it by 2.5-fold and 2.2-fold, respectively. The positive correlation between T7SS and other virulence determinants suggests regulation by a shared accessory gene regulator (agr) system, though T7SS appears to act independently of resistance traits. Future research should investigate whether agr inhibitors can suppress T7SS and mitigate infection severity.\u003c/p\u003e","manuscriptTitle":"Machine learning analysis of type VII secretion system expression and its relationships with virulence traits and antibiotic resistance in Staphylococcus aureus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 15:17:10","doi":"10.21203/rs.3.rs-7352746/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9240885-1b69-4068-9c17-c982522b003f","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-20T10:53:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 15:17:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7352746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7352746","identity":"rs-7352746","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}