RESUMEN
Comprehensive knowledge of host-pathogen interactions is central to understand the life cycle of a pathogen and devise specific therapeutic strategies. Protein-protein interactions (PPIs) are key mediators of host-pathogen interactions. Hepatitis E virus (HEV) is a major cause of viral hepatitis in humans. Recent reports also demonstrate its extrahepatic manifestations in the brain. Toward understanding the molecular details of HEV life cycle, we screened human liver and fetal brain cDNA libraries to identify the host interaction partners of proteins encoded by genotype 1 HEV and constructed the virus-host PPI network. Analysis of the network indicated a role of HEV proteins in modulating multiple host biological processes such as stress and immune responses, the ubiquitin-proteasome system, energy and iron metabolism, and protein translation. Further investigations revealed the presence of multiple host translation regulatory factors in the viral translation/replication complex. Depletion of host translation factors such as eIF4A2, eIF3A, and RACK1 significantly reduced the viral replication, whereas eIF2AK4 depletion had no effect. These findings highlight the ingenuity of the pathogen in manipulating the host machinery to its own benefit, a clear understanding of which is essential for the identification of strategic targets and development of specific antivirals against HEV. IMPORTANCE Hepatitis E virus (HEV) is a pathogen that is transmitted by the fecal-oral route. Owing to the lack of an efficient laboratory model, the life cycle of the virus is poorly understood. During the course of infection, interactions between the viral and host proteins play essential roles, a clear understanding of which is essential to decode the life cycle of the virus. In this study, we identified the direct host interaction partners of all HEV proteins and generated a PPI network. Our functional analysis of the HEV-human PPI network reveals a role of HEV proteins in modulating multiple host biological processes such as stress and immune responses, the ubiquitin-proteasome system, energy and iron metabolism, and protein translation. Further investigations revealed an essential role of several host factors in HEV replication. Collectively, the results from our study provide a vast resource of PPI data from HEV and its human host and identify the molecular components of the viral translation/replication machinery.
RESUMEN
Pluripotency is a unique property of stem cells that allows them to differentiate into all types of adult cells or maintain the self-renewal property. PluriPred predicts whether a protein is involved in pluripotency from primary protein sequence using manually curated pluripotent proteins as training datasets. Machine learning techniques (MLTs) such as Support Vector Machine (SVM), Naïve Base (NB), Random Forest (RF), and sequence alignment technique BLAST were used in our study. The combination of SVM and PSI-BLAST was our proposed best model, which obtained a sensitivity of 77.40 percent, specificity of 79.72 percent, accuracy of 79.2 percent, and area under the ROC curve was 0.82 using 5-fold cross-validation. Furthermore, PluriPred gives the confidence of the prediction from training dataset's SVM score distribution and p-value from BLAST. We validated our proposed model with the other existing highthroughput studies using blind/independent datasets. Using PluriPred, 233 novel core and 323 novel extended core pluripotent proteins from mouse proteome, and 167 novel core and 385 extended core pluripotent proteins from human proteome, were predicted with high confidence. The Web application of PluriPred is available from bicresources. jcbose.ac.in/ssaha4/pluripred/. Many pluripotent genes/proteins take part in protein-protein networks associated with stem cell, cancer, and developmental biology, and we believe that PluriPred will help in these research.