Zika virus leads to olfactory disorders in mice by targeting olfactory ensheathing cells.

BACKGROUND: Zika virus (ZIKV) is an emerging arbovirus of the genus flavivirus that is associated with congenital Zika syndrome (CZS) in newborns. A wide range of clinical symptoms including intellectual disability, speech delay, coordination or movement problems, and hearing and vision loss, have been well documented in children with CZS. However, whether ZIKV can invade the olfactory system and lead to post-viral olfactory dysfunction (PVOD) remains unknown. METHODS: We investigated the susceptibility and biological responses of the olfactory system to ZIKV infection using mouse models and human olfactory organoids derived from patient olfactory mucosa. FINDINGS: We demonstrate that neonatal mice infected with ZIKV suffer from transient olfactory dysfunction when they reach to puberty. Moreover, ZIKV mainly targets olfactory ensheathing cells (OECs) and exhibits broad cellular tropism colocalizing with small populations of mature/immature olfactory sensory neurons (mOSNs/iOSNs), sustentacular cells and horizontal basal cells in the olfactory mucosa (OM) of immunodeficient AG6 mice. ZIKV infection induces strong antiviral immune responses in both the olfactory mucosa and olfactory bulb tissues, resulting in the upregulation of proinflammatory cytokines/chemokines and genes related to the antiviral response. Histopathology and transcriptomic analysis showed typical tissue damage in the olfactory system. Finally, by using an air-liquid culture system, we showed that ZIKV mainly targets sustentacular cells and OECs and support robust ZIKV replication. INTERPRETATION: Our results demonstrate that olfactory system represents as significant target for ZIKV infection, and that PVOD may be neglected in CZS patients. FUNDING: Stated in the acknowledgment.

Aerosolized Zika virus infection in Guinea pigs.

Zika virus (ZIKV) is primarily transmitted through mosquito bites and sexual contact, and vertical transmission of ZIKV has also been observed in humans. In addition, ZIKV infection via unknown transmission routes has been frequently reported in clinical settings. However, whether ZIKV can be transmitted via aerosol routes remains unknown. In this study, we demonstrated that aerosolized ZIKV is fully infectious in vitro and in vivo. Remarkably, intratracheal (i.t.) inoculation with aerosolized ZIKV led to rapid viremia and viral secretion in saliva, as well as robust humoral and innate immune responses in guinea pigs. Transcriptome analysis further revealed that the expression of genes related to viral processes, biological regulation and the immune response was significantly changed. Together, our results confirm that aerosolized ZIKV can result in systemic infection and induce both innate and adaptive immune responses in guinea pigs, highlighting the possibility of ZIKV transmission via aerosols.

Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods.

Pupylation is an important posttranslational modification in proteins and plays a key role in the cell function of microorganisms; an accurate prediction of pupylation proteins and specified sites is of great significance for the study of basic biological processes and development of related drugs since it would greatly save experimental costs and improve work efficiency. In this work, we first constructed a model for identifying pupylation proteins. To improve the pupylation protein prediction model, the KNN scoring matrix model based on functional domain GO annotation and the Word Embedding model were used to extract the features and Random Under-sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied to balance the dataset. Finally, the balanced data sets were input into Extreme Gradient Boosting (XGBoost). The performance of 10-fold cross-validation shows that accuracy (ACC), Matthew's correlation coefficient (MCC), and area under the ROC curve (AUC) are 95.23%, 0.8100, and 0.9864, respectively. For the pupylation site prediction model, six feature extraction codes (i.e., TPC, AAI, One-hot, PseAAC, CKSAAP, and Word Embedding) served to extract protein sequence features, and the chi-square test was employed for feature selection. Rigorous 10-fold cross-validations indicated that the accuracies are very high and outperformed its existing counterparts. Finally, for the convenience of researchers, PUP-PS-Fuse has been established at https://bioinfo.jcu.edu.cn/PUP-PS-Fuse and http://121.36.221.79/PUP-PS-Fuse/as a backup.

Predicting S-nitrosylation proteins and sites by fusing multiple features.

Protein S-nitrosylation is one of the most important post-translational modifications, a well-grounded understanding of S-nitrosylation is very significant since it plays a key role in a variety of biological processes. For an uncharacterized protein sequence, it is a very meaningful problem for both basic research and drug development when we can firstly identify whether it is a S-nitrosylation protein or not, and then predict the specific S-nitrosylation site(s). This work has proposed two models for identifying S-nitrosylation protein and its PTM sites. Firstly, three kinds of features are extracted from protein sequence: KNN scoring of functional domain annotation, PseAAC and bag-of-words based on the physical and chemical properties of amino acids. Secondly, the synthetic minority oversampling technique is used to balance the data sets, and some state-of-the-art classifiers and feature fusion strategies are performed on the balanced data sets. In the five-fold cross-validation for predicting S-nitrosylation proteins, the results of Accuracy (ACC), Matthew's correlation coefficient (MCC) and area under ROC curve (AUC) are 81.84%, 0.5178, 0.8635, respectively. Finally, a model for predicting S-nitrosylation sites has been constructed on the basis of tripeptide composition (TPC) and the composition of k-spaced amino acid pairs (CKSAAP). To eliminate redundant information and improve work efficiency, elastic nets are employed for feature selection. The five-fold cross-validation tests have indicated the promising success rates of the proposed model. For the convenience of related researchers, the web-server named "RF-SNOPS" has been established at http://www.jci-bioinfo.cn/RF-SNOPS.