Biology subjects

Zhang, Q.-R.

Publications and source records attributed to Zhang, Q.-R..

2 recordsLinked to original sources

bioRxiv2022

Microglia are involved in regulating histamine dependent and non-dependent itch transmissions with distinguished signal pathways

Although itch and pain have many similarities, they are completely different in perceptual experience and behavioral response. In recent years, we have a deep understanding of the neural pathways of itch sensation transmission. However, there are few reports on the role of non-neuronal cells in itch. Microglia are known to play a key role in chronic neuropathic pain and acute inflammatory pain. It is still unknown whether microglia are also involved in regulating the transmission of itch sensation. In the present study, we used several kinds of transgenic mice to specifically deplete CX3CR1+ central microglia and peripheral macrophages together (whole depletion), or selectively deplete central microglia alone (central depletion). We observed that the acute itch responses to histamine, compound 48/80 and chloroquine were all significantly reduced in mice with either whole or central depletion. Spinal c-fos mRNA assay and further studies revealed that histamine and compound 48/80, but not chloroquine elicited primary itch signal transmission from DRG to spinal Npr1- and somatostatin-positive neurons relied on microglial CX3CL1-CX3CR1 pathway. Our results suggested that central microglia were involved in multiple types of acute chemical itch transmission, while the underlying mechanisms for histamine dependent and non-dependent itch transmission were different that the former required the CX3CL1-CX3CR1 signal pathway.

neuroscience↗

bioRxiv2021

Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy

Persistent infection with high-risk types Human Papillomavirus could cause diseases including cervical cancers and oropharyngeal cancers. Nonetheless, so far there is no effective pharmacotherapy for treating the infection from high-risk HPV types, and hence it remains to be a severe threat to the health of female. Based on drug repositioning strategy, we trained and benchmarked multiple machine learning models so as to predict potential effective antiviral drugs for HPV infection in this work. Through optimizing models, measuring models predictive performance using 182 pairs of antiviral-target interaction dataset which were all approved by the United States Food and Drug Administration, and benchmarking different models predictive performance, we identified the optimized Support Vector Machine and K-Nearest Neighbor classifier with high precision score were the best two predictors (0.80 and 0.85 respectively) amongst classifiers of Support Vector Machine, Random forest, Adaboost, Naive Bayes, K-Nearest Neighbors, and Logistic regression classifier. We applied these two predictors together and successfully predicted 57 pairs of antiviral-HPV protein interactions from 864 pairs of antiviral-HPV protein associations. Our work provided good drug candidates for anti-HPV drug discovery. So far as we know, we are the first one to conduct such HPV-oriented computational drug repositioning study.

bioinformatics↗