Histone methylation in Epstein-Barr virus-associated diseases.

Epstein-Barr virus (EBV) infection is linked to various human diseases, including both noncancerous conditions like infectious mononucleosis and cancerous diseases such as lymphoma and nasopharyngeal carcinoma. After the initial infection, EBV establishes a lifelong presence and remains latent in specific cells. This latent infection causes changes in the epigenetic marks known as histone methylation. Many studies have examined the role of histone methylation in different EBV-associated diseases, and understanding how EBV affects histone methylation can help us identify potential targets for epigenetic therapies. This review focuses on the research progress made in understanding histone methylation in well-studied EBV-associated diseases, intending to provide insights into potential strategies based on histone methylation to combat EBV-related ailments.

This review focuses on histone methylation in EBV-associated diseases, offering potential strategies to combat EBV-related ailments. #EBV #histonemethylation #epigenetics #medicalresearch.

Rapid Classification of Multilocus Sequence Subtype for Group B Streptococcus Based on MALDI-TOF Mass Spectrometry and Statistical Models.

Group B Streptococcus (GBS) is an important etiological agent of maternal and neonatal infections as well as postpartum women and individuals with impaired immunity. We developed and evaluated a rapid classification method for sequence types (STs) of GBS based on statistic models with Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF/MS). Whole-cell lysates MALDI-TOF/MS analysis was performed on 235 well-characterized GBS isolates from neonatal invasive infections in a multi-center study in China between 2015 and 2017. Mass spectra belonging to major STs (ST10, ST12, ST17, ST19, ST23) were selected for model generation and validation. Recognition and cross validation values were calculated by Genetic Algorithm-K Nearest Neighbor (GA-KNN), Supervised Neural Network (SNN), QuickClassifier (QC) to select models with the best performance for validation of diagnostic efficiency. Informative peaks were further screened through peak statistical analysis, ST subtyping MSP peak data and mass spectrum visualization. For major STs, the ML models generated by GA-KNN algorithms attained highest cross validation values in comparison to SNN and QC algorithms. GA-KNN models of ST10, ST17, and ST12/ST19 had good diagnostic efficiency, with high sensitivity (95-100%), specificity (91.46%-99.23%), accuracy (92.79-99.29%), positive prediction value (PPV, 80%-92.68%), negative prediction value (NPV, 94.32%-99.23%). Peak markers were firstly identified for ST10 (m/z 6250, 3125, 6891) and ST17 strains (m/z 2956, 5912, 7735, 5218). Statistical models for rapid GBS ST subtyping using MALDI-TOF/MS spectrometry contributes to easier epidemical molecular monitoring of GBS infection diseases.