MAMMAL-SPECIFIC GENOMIC FEATURES CAN PREDICT HIGH ADAPTABILITY TO THE WEST NILE VIRUS
10.3969/j.issn.1005-0507.2025.02.004
- VernacularTitle:哺乳动物特异性基因组特征可预测高适应西尼罗河病毒
- Author:
Yu-Rong CAI
1
;
Dan-Dan ZENG
;
Sen ZHANG
;
Jing LI
;
Quan FU
Author Information
1. 内蒙古医科大学附属医院(检验科),呼和浩特 010030
- Keywords:
West Nile virus(WNV);
Adaptation;
Dinucleotide Composition Representation(DCR);
Deep learning
- From:
Acta Parasitologica et Medica Entomologica Sinica
2025;32(2):84-92
- CountryChina
- Language:Chinese
-
Abstract:
Objective West Nile virus(WNV)is one of the most common mosquito-borne zoonotic viruses worldwide,with unique transmission dynamics and varied hosts.Lots of ecological and host factors have been reported to influence the host adaptation and transmission of WNVs,however,general genomic features of WNVs are less focused,except for some exact host-specific genotypes at molecular level.Artificial intelligence that analyzes genome composition characteristics currently shows significant advantages in identifying and predicting viral host adaptability.This research aimed to establish a convolutional neural network(CNN)model to predict the host adaptability of WNVs based on general genomic features.Methods Presently available WNV gene sequences were embedded for their genomic features with an embedding approach of dinucleotide composition representation(DCR).And DCR-based distribution difference of WNV samples among various hosts was performed with unsupervised learning methods.Then a classification model was built with a convolutional neural network(CNN)framework based on genomic DCR to evaluate the adaptation of the WNVs from birds,mammals and mosquitos.Additionally,host-specific amino acids in WNV proteins were inferred via Bayes method.Results DCR features could effectively distinguish host-specific WNVs.The trained CNN model predicted accurately mammalian susceptible WNVs from avian susceptible WNVs,however,much less accurately for mosquito/mammalian WNVs.Such predicted host adaptation was interpreted as host specified significance of biased amino acid distribution on the bayes-inferred sites in WNV proteins,implying a possible high significance of these sites for WNV adaptive phenotypes.Conclusions Genomic compositional features of WNVs are host-specific,and such genomic bias facilitates predicting the adaptation of WNVs to avian or mammalian hosts via deep learning methods.DCR-based decomposition is helpful to recognize the high risk of infecting mammals of WNVs.The present study provides a general knowledge of genomic features contributing to host adaptation to WNVs.
