Biology subjects

Chung, C.-R.

Publications and source records attributed to Chung, C.-R..

4 recordsLinked to original sources

bioRxiv2025

Impact of Peptide Initial Configuration and Membrane Composition on Melittin's Pore-Forming Ability under Unbiased All-Atom Molecular Dynamics Simulations

The rising challenge of antimicrobial resistance has accelerated the search for alternative therapeutics. Antimicrobial peptides (AMPs), a class of naturally occurring defense molecules found across diverse species, are promising candidates. Despite their potent membrane-disrupting activity, the atomic details of the pore formation process remain insufficiently understood. In this study, we employed all-atom molecular dynamics (MD) simulations to investigate the pore formation process of melittin under different initial configurations. Simulations were conducted using three different membrane systems: a pure POPC bilayer, a mammalian membrane model (DOPC:Cholesterol, 9:1), and a bacterial membrane model (DOPE:DOPG, 3:1). For each system, we examined four different starting configurations, in which six melittin peptides were arranged in a star-like pattern. Our results demonstrated that the pore formation process strongly depends on the initial peptide configuration. In one specific initial arrangement (termed as Conf.I), pore formation consistently occurred within 100 nanoseconds, regardless of membrane composition. Furthermore, the simulations revealed that pore formation was more challenging in the mammalian membrane model and even more so in the bacterial membrane model, in comparison with the pure POPC bilayer. These findings are in line with previously reported minimum inhibitory concentration (MIC) and the 50% hemolysis concentration (HC50) of melittin in the literature. Additionally, we identified lysine-7 (K7) as the key residue in determining whether a stable pore can form. In configurations where the K7 side chain formed electrostatic interactions with the phosphate group of a lipid, melittin were anchored to the membrane surface, thereby preventing pore formation. In contrast, simulations of melittin mutants K7A and K7Q showed no such anchoring effect, and thus pore formation was possible in multiple initial configurations. Notably, the K7Q mutation showed a preference for pore formation in bacterial membranes over mammalian membranes, suggesting that reducing toxicity while maintaining antimicrobial efficacy is possible.

biochemistry↗

bioRxiv2022

Rapid and accurate discrimination of Mycobacterium abscessus subspecies based on matrix-assisted laser desorption ionization-time of flight spectrum and machine learning

BackgroundMycobacterium abscessus complex (MABC) has been reported to cause considerable complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance property amid the subspecies. However, long incubation days is needed for the traditional antibiotic susceptibility testing (AST) method. Effective antibiotics administration often delayed considerably and caused unfavorable outcomes. Thus, we proposed a novel and accurate method to identify subspecies and its potential antibiotics resistance, to guide clinical treatment within hours. MethodsSubspecies of the MABC isolates were determined by secA1, rpoB, and hsp65. AST was tested by using microdilution method, as well as sequencing of erm(41) and rrl genes. MALDI-TOF mass spectrometry (MS) spectra were analyzed. The informative peaks on MS spectra were detected by random forest (RF) importance. Machine learning (ML) algorithms were used to build models for classifying MABC subspecies based on MALDI-TOF spectrum. The models were developed and validated by nested five-fold cross-validation to avoid over-fitting. ResultsIn total, 102 MABC isolates (52 subspecies abscessus and 50 subspecies massiliense) were analyzed. Top informative peaks including m/z 6715, 4739, 2805, etc. were identified. RF model attained AUROC of 0.9166 (95% CI: 0.9072-0.9196) and outperformed other algorithms in discriminating subspecies abscessus from massiliense. ConclusionWe developed a MALDI-TOF based ML model for rapid and accurate MABC subspecies identification. The novel diagnostic tool would guide a more accurate and timely MABC subspecies-specific treatment.

microbiology↗

bioRxiv2021

Clinically applicable rapid susceptibility testing of multi-drug resistant Staphylococcus aureus by mass spectrometry and extreme gradient boosting machine

Multi-drug resistant Staphylococcus aureus is one of the major causes of severe infections. Due to the delays of conventional antibiotic susceptibility test (AST), most cases were prescribed by experience with a lower recovery rate. Linking a 7-year study of over 20,000 Staphylococcus aureus infected patients, we incorporated mass spectrometry and machine learning technology to predict the susceptibilities of patients for 4 different antibiotics that can enable early antibiotic decisions. The predictive models were externally validated in an independent patient cohort, resulting in an area under the receiver operating characteristic curve of 0.94, 0.90, 0.86, 0.91 and an area under the precision-recall curve of 0.93, 0.87, 0.87, 0.81 for oxacillin (OXA), clindamycin (CLI), erythromycin (ERY) and trimethoprim-sulfamethoxazole (SXT), respectively. Moreover, our pipeline provides AST 24-36 h faster than standard workflows, reduction of inappropriate antibiotic usage with preclinical prediction, and demonstrates the potential of combining mass spectrometry with machine learning (ML) to assist early and accurate prescription. Therapies to individual patients could be tailored in the process of precision medicine.

bioinformatics↗

bioRxiv2020

Rapidly predicting vancomycin resistance of Enterococcus faecium through MALDI-TOF MS spectra obtained in real-world clinical microbiology laboratory

Enterococcus faecium is one of the leading pathogens in the world. In this study, we proposed a strategy to rapidly and accurately distinguish vancomycin-resistant Enterococcus faecium (VREfm) and vancomycin-susceptible E. faecium (VSEfm) to help doctors correctly determine the use of vancomycin by a machine learning (ML)-based algorithm. A predictive model was developed and validated to distinguish VREfm and VSEfm by analyzing MALDI-TOF MS spectra of unique E. faecium isolates from different specimen types. Firstly, 5717 mass spectra, including 2795 VREfm and 2922 VSEfm, were used to develop the algorithm. And 2280 mass spectra of isolates, namely 1222 VREfm and 1058 VSEfm, were used to externally validate the algorithm. The random forest-based algorithm demonstrated good classification performances for overall specimens, whose mean AUROC in 5-fold cross validation, time-wise validation, and external validation was all greater than 0.84. For the detection of VREfm in blood, sterile body fluid, urinary tract, and wound, the AUROC in external validation was also greater than 0.84. The predictions with algorithms were significantly more accurate than empirical antibiotic use. The accuracy of antibiotics administration could be improved by 30%. And the algorithm could provide rapid antibiotic susceptibility results at least 24 hours ahead of routine laboratory tests. The turn-around-time of antibiotic susceptibility could be reduced by 50%. In conclusion, a ML algorithm using MALDI-TOF MS spectra obtained in routine workflow accurately differentiated VREfm from VSEfm, especially in blood and sterile body fluid, which can be applied to facilitate the clinical testing process due to its accuracy, generalizability, and rapidness.

microbiology↗