Journal of Microbiology

Journal Article

Alterations of oral microbiota in Chinese children with viral encephalitis and/or viral meningitis: Yijie Li , Jing Liu , Yimin Zhu , Chunying Peng , Yao Dong , Lili Liu , Yining He , Guoping Lu , Yingjie Zheng; J. Microbiol. 2022;60(4):429-437. Published online February 14, 2022; DOI: https://doi.org/10.1007/s12275-022-1560-y

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The role of oral microbiota in viral encephalitis and/or viral meningitis (VEVM) remains unclear. In this hospital-based, frequency-matched study, children with clinically diagnosed VEVM (n = 68) and those with other diseases (controls, n = 68) were recruited. Their oral swab samples were collected and the oral microbiota was profiled using 16S rRNA gene sequencing. The oral microbiota of children with VEVM exhibited different beta diversity metrics (unweighted UniFrac distance: P < 0.001, R2 = 0.025, Bray-curtis dissimilarity: P = 0.045, R2 = 0.011, and Jaccard dissimilarity: P < 0.001, R2 = 0.017) and higher relative abundances of taxa identified by Linear discriminant analysis (LDA) with effect size (Enterococcus, Pedobacter, Massilia, Prevotella_9, Psychrobacter, Butyricimonas, Bradyrhizobium, etc., LDA scores > 2.0) when compared with the control group. The higher pathway abundance of steroid hormone biosynthesis predicted by oral microbiota was suggested to be linked to VEVM (q = 0.020). Further, a model based on oral microbial traits showed good predictive performance for VEVM with an area under the receiver operating characteristic curve of 0.920 (95% confidence interval: 0.834–1.000). Similar results were also obtained between children with etiologically diagnosed VEVM (n = 43) and controls (n = 68). Our preliminary study identified VEVM-specific oral microbial traits among children, which can be effective in the diagnosis of VEVM.

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Metagenomic next-generation sequencing and proteomics analysis in pediatric viral encephalitis and meningitis
Yi-Long Wang, Xiao-Tong Guo, Meng-Ying Zhu, Yu-Chen Mao, Xue-Bin Xu, Yi Hua, Lu Xu, Li-Hua Jiang, Cong-Ying Zhao, Xin Zhang, Guo-Xia Sheng, Pei-Fang Jiang, Zhe-Feng Yuan, Feng Gao
Frontiers in Cellular and Infection Microbiology.2023;[Epub] CrossRef
Bacterial Biomarkers of the Oropharyngeal and Oral Cavity during SARS-CoV-2 Infection
William Bourumeau, Karine Tremblay, Guillaume Jourdan, Catherine Girard, Catherine Laprise
Microorganisms.2023; 11(11): 2703. CrossRef

Review

[MINIREVIEW]The rapid adaptation of SARS-CoV-2–rise of the variants: transmission and resistance: Sandrine M. Soh , Yeongjun Kim , Chanwoo Kim , Ui Soon Jang , Hye-Ra Lee; J. Microbiol. 2021;59(9):807-818. Published online August 27, 2021; DOI: https://doi.org/10.1007/s12275-021-1348-5

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Abstract

The causative factor of COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously mutating. Interestingly, identified mutations mainly occur in the spike (S) protein which interacts with the ACE2 receptor and is cleaved via serine protease TMPRSS2. Some mutated strains are becoming dominant in various parts of the globe because of increased transmissibility as well as cell entry efficacy. Remarkably, the neutralizing activity of monoclonal antibodies, convalescent sera, and vaccines against the variants has been reported to be significantly reduced. Therefore, the efficacy of various monoclonal antibodies therapy and vaccines against these variants is becoming a great global concern. We herein summarize the current status of SARS-CoV- 2 with gears shifted towards the recent and most common genetic variants in relation to transmission, neutralizing activity, and vaccine efficacy.

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Research Support, Non-U.S. Gov't

Improved Prediction of Coreceptor Usage and Phenotype of HIV-1 Based on Combined Features of V3 Loop Sequence Using Random Forest: Shungao Xu , Xinxiang Huang , Huaxi Xu , Chiyu Zhang; J. Microbiol. 2007;45(5):441-446.; DOI: https://doi.org/2592 [pii]

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Abstract: HIV-1 coreceptor usage and phenotype mainly determined by V3 loop are associated with the disease progression of AIDS. Predicting HIV-1 coreceptor usage and phenotype facilitates the monitoring of R5-to-X4 switch and treatment decision-making. In this study, we employed random forest to predict HIV-1 biological phenotype, based on 37 random features of V3 loop. In comparison with PSSM method, our RF predictor obtained higher prediction accuracy (95.1% for coreceptor usage and 92.1% for phenotype), especially for non-B non-C HIV-1 subtypes (96.6% for coreceptor usage and 95.3% for phenotype). The net charge, polarity of V3 loop and five V3 sites are seven most important features for predicting HIV-1 coreceptor usage or phenotype. Among these features, V3 polarity and four V3 sites (22, 12, 18 and 13) are first reported to have high contribution to HIV-1 biological phenotype prediction.

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