Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors

Abstract

Human immunodeficiency virus type 1 (HIV-1) depends on a class of host proteins called host dependency factors (HDFs) to facilitate its infection. So far experimental efforts have detected a certain number of HDFs, but the gene inventory of HIV-1 HDFs remains incomplete. Here, we implemented an existing network-based gene discovery strategy to predict HIV-1 HDFs. First, an encoding scheme based on a publicly available human tissue-specific gene functional network (GIANT; http://giant.princeton.edu/) was designed to convert each human gene into a 25,825-dimensional feature vector. Then, a random forest-based predictive model was trained on a data set containing 868 known HDFs and 1,736 non-HDFs. Through 5-fold cross-validation, an independent test, and comparison with one existing method, the proposed prediction method consistently revealed accurate and competitive performance. The highlight of our method should be ascribed to the introduction of the GIANT encoding scheme, which contains rich information regarding gene interactions. By merging known HDFs and genome-wide HDF prediction results, network analysis was conducted to catch the common patterns of HDFs in the context of the GIANT network. Interestingly, HDFs reveal significantly lower betweenness than HIV-1-interacting human proteins (i.e., HIV targets). In the meantime, the functional roles of HDFs were also examined by mapping all the HDF candidates into human protein complexes. Especially, we observed the frequent co-occurrence of HDFs and HIV targets at the protein complex level. Collectively, we hope the proposed prediction method not only can accelerate the HDF identification and antiviral drug target discovery, but also can provide some mechanistic insights into human-virus relationships. IMPORTANCE Identification of HIV-1 HDFs remains a crucial step to understand the complicated relationships between human and HIV-1. To complement the experimental identification of HDFs, we have implemented an existing network-based gene discovery strategy to predict HDFs from the human genome. The core idea of the proposed method is that the rich information deposited in host gene functional networks can be effectively utilized to infer the potential HDFs. We hope the proposed prediction method could further guide hypothesis-driven experimental efforts to interrogate human–HIV-1 relationships and provide new hints for the development of antiviral drugs to combat HIV-1 infection.

Systematic comparison of differential expression networks in MTB mono-, HIV mono- and MTB/HIV co-infections for drug repurposing

“…The use of ribosomal pathway was possibly because the host needs to synthesize a large number of proteins in response to pathogen infections [ 16 ]. The spliceosome mediates pathogen infections by participating in mRNA splicing, which enables pathogens to prevent the downstream immune responses [ 53 ]. Besides, MMI and MHCI shared several additional pathways, such as ‘proteasome’ and ‘prion disease’.…”

A Virus-Packageable CRISPR System Identifies Host Dependency Factors Co-Opted by Multiple HIV-1 Strains

“…A subset of these interacting proteins were functionally validated in primary CD4 + T cells as HIV dependency or restriction factors ( 8 ). In addition, interaction-based and gene network approaches have been used to computationally predict dependency factor genes ( 9 ) and to develop a viral-host dependency epistasis map (Ve-MAP) ( 10 ). However, none of these approaches have used an assay with a functional readout to examine multiple viral strains across the entire HIV life cycle in T cells.…”

A Virus-Packageable CRISPR System Identifies Host Dependency Factors Across Multiple HIV-1 Strains

“…A subset of these interacting proteins were functionally validated in primary CD4+ T cells as HIV dependency or restriction factors (8). In addition, interaction based and gene network approaches have been used to computationally predict dependency factor genes (9) and to develop a viral-host dependency epistasis map (Ve-MAP) (10). However, none of these approaches have used an assay with a functional readout to examine multiple viral strains across the entire HIV lifecycle in T cells.…”