Dysregulation of Long Non-coding RNA (lncRNA) Genes and Predicted lncRNA-Protein Interactions during Zika Virus Infection

Arunachalam Ramaiah; Deisy Contreras; Vineela Gangalapudi; Masumi Sameer Padhye; Jie Tang; Vaithilingaraja Arumugaswami

Identifying an interaction network of lncRNAs and protein coding genes, which are differentially regulated in ZIKV infected cells relative to the uninfected control, is essential to uncover the key biological functions of lncRNAs in cell injury processes. Hence, we focused on identifying the factors that can interact with lncRNAs NEAT1 and MALAT1 using RAIN and STRING. Initial seed interaction networks for NEAT1 and MALAT1 predicted a maximum of 20 interacting factors ( Supplementary Figures 1 and 2 ). There were 20 and 19 high confidence nodes that involved in 31 and 32 interactions for NEAT1 and MALAT1 networks, respectively. Almost, all the interactions in these two independent networks were supported by text-mining. Afterwards, the factors interacted with each of the two lncRNAs and were combined to find the common interacting factors (Figure 4 ). We identified two lncRNAs namely MIAT and TUG1 as common Comprehensive lncRNA-Protein interaction network. Proteins collectively contribute to a shared and specific function. For instance, if a single edge appears with different lines/colors between two nodes, this clearly indicates that the interaction between two given genes/nodes is supported by more than one verification [25] . It is ambiguous if current co-regulated gene data . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. can be correlated to the predicted human lncRNA-protein networks even though it is widely known that interacting factors are frequently co-expressed [26] . With the evidence from the interacting network, we identified known co-expressing genes that are differentially regulated during ZIKV infection from our genome-wide gene expression data sets. We extended our analysis using the 11 interacting factors defined in our previous analysis for further comprehensive interacting network analysis in order to identify more high confidence co- Table 3 ). It is possible that 5 lncRNAs having interactions with 24 proteins may form post-transcriptional regulatory networks [27] [28] [29] , and control fundamental cellular and developmental processes. We also observed that apart from the text-mining, the majority of the lncRNA-protein interactions were supported by experimental evidence. progression. We found that the cell cycle pathways and various cell cycle genes including CDK1, Cyclin B, E2F, and p21 were deregulated in ZIKV infected cells (Supplementary Fig. 3 ).