Staphylococcus aureus (S. aureus) causes persistent biofilm-related infections. Biofilm formation by S. aureus is affected by the culture conditions and is associated with certain genotypic characteristics. Here, we show that glucose and sodium chloride (NaCl) supplementation of culture media, a common practice in studies of biofilms in vitro, influences both biofilm formation by 40 S. aureus clinical isolates (methicillin-resistant and methicillin-sensitive S. aureus) and causes variations in biofilm quantification. Methicillin-resistant strains formed more robust biofilms than methicillin-sensitive strains in tryptic soy broth (TSB). However, glucose supplementation in TSB greatly promoted and stabilized biofilm formation of all strains, while additional NaCl was less efficient in this respect and resulted in significant variation in biofilm measurements. In addition, we observed that the ST239-SCCmec (Staphylococcal Cassette Chromosome mec) type III lineage formed strong biofilms in TSB supplemented with glucose and NaCl. Links between biofilm formation and accessory gene regulator (agr) status, as assessed by δ-toxin production, and with mannitol fermentation were not found. Our results show that TSB supplemented with 1.0% glucose supports robust biofilm production and reproducible quantification of S. aureus biofilm formation in vitro, whereas additional NaCl results in major variations in measurements of biofilm formation.

Methicillin-resistant Staphylococcus aureus (MRSA) is a serious pathogen in clinical settings and early detection is critical. Here, we investigated the MRSA discrimination potential of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) using 320 clinical S. aureus isolates obtained in 2005–2014 and 181 isolates obtained in 2018. We conducted polymerase chain reactions (PCR) for staphylococcal cassette chromosome mec (SCCmec) typing and MALDI-TOF MS to find specific markers for methicillin resistance. We identified 21 peaks with significant differences between MRSA and methicillin-susceptible S. aureus (MSSA), as determined by mecA and SCCmec types. Each specific peak was sufficient to discriminate MRSA. We developed two methods for simple discrimination according to these peaks. First, a decision tree for MRSA based on six MRSA-specific peaks, three MSSA-specific peaks, and two SCCmec type IV peaks showed a sensitivity of 96.5%. Second, simple discrimination based on four MRSA-specific peaks and one MSSA peak had a maximum sensitivity of 88.3%. The decision tree applied to 181 S. aureus isolates from 2018 had a sensitivity of 87.6%. In conclusion, we used specific peaks to develop sensitive MRSA identification methods. This rapid and easy MALDI-TOF MS approach can improve patient management.

The prompt presumptive identification of methicillin-resistant Staphylococcus aureus (MRSA) using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) can aid in early clinical management and infection control during routine bacterial identification procedures. This study applied a machine learning approach to MALDI-TOF peaks for the presumptive identification of MRSA and compared the accuracy according to staphylococcal cassette chromosome mec (SCCmec) types. We analyzed 194 S. aureus clinical isolates to evaluate the machine learning-based identification system (AMRQuest software, v.2.1, ASTA: Suwon, Korea), which was constructed with 359 S. aureus clinical isolates for the learning dataset. This system showed a sensitivity of 91.8%, specificity of 83.3%, and accuracy of 87.6% in distinguishing MRSA. For SCCmec II and IVA types, common MRSA types in a hospital context, the accuracy was 95.4% and 96.1%, respectively, while for the SCCmec IV type, it was 21.4%. The accuracy was 90.9% for methicillin-susceptible S. aureus. This presumptive MRSA identification system may be helpful for the management of patients before the performance of routine antimicrobial resistance testing. Further optimization of the machine learning model with more datasets could help achieve rapid identification of MRSA with less effort in routine clinical procedures using MALDI-TOF MS as an identification method.

Staphylococcus aureus is a major human bacterial pathogen that carries a large number of virulence factors. Many virulence factors of S. aureus are regulated by the accessory gene regulator (agr) quorum‐sensing system. Phenol‐soluble modulins (PSMs) are one of the agr‐mediated virulence determinants known to play a significant role in S. aureus pathogenesis. In the present study, the efficacy of thymol to inhibit PSM production including δ‐toxin in S. aureus was explored. We employed liquid chromatography–mass spectrometry (LC–MS) to quantify the PSMsα1–PSMα4, PSMβ1 and PSMβ2, and δ‐toxin production from culture supernatants. We found that thymol at 0.5 MIC (128 μg/mL) significantly reduced the PSMα and δ‐toxin production in S. aureus WKZ‐1, WKZ‐2, LAC USA300, and ATCC29213. Downregulation in transcription by quantitative real‐time (qRT) PCR analysis of response regulator agrA and receptor histidine kinase agrC upon 0.5 MIC thymol treatment affirmed the results of LC–MS quantification of PSMs. In silico molecular docking analysis demonstrated the binding affinity of thymol with receptors AgrA and AgrC. Transmission electron microscopy images revealed no ultrastructural alterations (cell wall and membrane) in thymol‐treated WKZ‐1 and WKZ‐2 S. aureus strains. Here, we demonstrated that thymol reduces various PSM production in S. aureus clinical isolates and reference strains with mass spectrometry.

Phenol-soluble modulins (PSMs) are important S. aureus virulence factors that cause cytolysis, mast cell degranulation, and stimulate inflammatory responses. In this study, PSM production by S. aureus clinical isolates was measured by liquid chromatography/mass spectrometry (LC-MS) and correlated with staphylococcal protein A (spa) type and staphylococcal cassette chromosome mec (SCCmec) type. Of 106 S. aureus clinical isolates, 50 (47.2%) corresponded to methicillin-susceptible S. aureus (MSSA) and 56 (52.8%) to methicillin-resistant S. aureus (MRSA). LC-MS analysis revealed no significant difference in average PSMα3, PSMα4, PSMβ2, and δ-toxin production between MSSA and MRSA isolates, but PSMα1, PSMα2, and PSMβ1 production were higher in MSSA than MRSA. This study demonstrated that average PSMα1–α4, PSMβ1–β2, and δ-toxin production by SCCmec type II strains was significantly lower than the IV, IVA, and V strains. Most of the SCCmec type II strains (n = 17/25; 68.0%) did not produce δ-toxin, suggesting a dysfunctional Agr system. The spa type t111 (except one strain) and t2460 (except one strain producing PSM α1–α4) did not produce PSMα1–α4 and δ-toxin, while average PSM production was higher among the t126 and t1784 strains. This study showed that the genotype of S. aureus, specifically the spa and SCCmec types, is important in characterizing the production of PSMs.

Colorectal cancer (CRC) is a major cause of cancer-related mortality, highlighting the need for accurate and non-invasive diagnostics. This study assessed the utility of tumor-associated circulating transcripts (TACTs) as biomarkers for CRC detection and integrated these markers into machine learning models to enhance diagnostic performance. We evaluated five models—Generalized Linear Model, Random Forest, Gradient Boosting Machine, Deep Neural Network (DNN), and AutoML—and identified the DNN model as optimal owing to its high sensitivity (85.7%) and specificity (90.9%) for CRC detection, particularly in early-stage cases. Our findings suggest that combining TACT markers with AI-based analysis provides a scalable and precise approach for CRC screening, offering significant advancements in non-invasive cancer diagnostics to improve early detection and patient outcomes.