Probability of consolidation constrains novel serotype emergence in dengue fever virus

Deep learning guided prediction modeling of dengue virus evolving serotype

Evolution remains an incessant process in viruses, allowing them to elude the host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Emerging and re-emerging diseases are among the significant public health concerns globally. The revival of dengue is mainly due to the potential for naturally arising mutations to induce genotypic alterations in serotypes. These transformations could lead to future outbreaks, underscoring the significance of studying DENV evolution in endemic regions. Predicting the emerging Dengue Virus (DENV) genome is crucial as the virus disrupts host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging DENV serotypes. While Recurrent Neural Networks (RNN) were initially designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing the contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generated sequences were analyzed along with available DENV serotype sequences to find conserved motifs in the genome through MEME Suite (version 5.5.5). The generative model showed an accuracy of 93 %, and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score, all to be around 99.00 %, demonstrating the classification model's reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99 % similarity to DENV-4 and around 63-65 % similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum of 90 % similarity underscored their uniqueness.

Let's Get Physical: Flavivirus-Host Protein-Protein Interactions in Replication and Pathogenesis

Flaviviruses comprise a genus of viruses that pose a significant burden on human health worldwide. Transmission by both mosquito and tick vectors, and broad host tropism contribute to the presence of flaviviruses globally. Like all viruses, they require utilization of host molecular machinery to facilitate their replication through physical interactions. Their RNA genomes are translated using host ribosomes, synthesizing viral proteins that cooperate with each other and host proteins to reshape the host cell into a factory for virus replication. Thus, dissecting the physical interactions between viral proteins and their host protein targets is essential in our comprehension of how flaviviruses replicate and how they alter host cell behavior. Beyond replication, even single interactions can contribute to immune evasion and pathogenesis, providing potential avenues for therapeutic intervention. Here, we review protein interactions between flavivirus and host proteins that contribute to virus replication, immune evasion, and disease.