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 implemente...

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Autores principales:	Chen Fu, Shiping Yang, Xiaodi Yang, Xianyi Lian, Yan Huang, Xiaobao Dong, Ziding Zhang
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	HIV-1 host dependency factors prediction machine learning gene functional network Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/cef25fbe9c37412f9427899e3d607fdf
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spelling	oai:doaj.org-article:cef25fbe9c37412f9427899e3d607fdf2021-12-02T19:47:36ZHuman Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors10.1128/mSystems.00960-202379-5077https://doaj.org/article/cef25fbe9c37412f9427899e3d607fdf2020-12-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00960-20https://doaj.org/toc/2379-5077ABSTRACT 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.Chen FuShiping YangXiaodi YangXianyi LianYan HuangXiaobao DongZiding ZhangAmerican Society for MicrobiologyarticleHIV-1host dependency factorspredictionmachine learninggene functional networkMicrobiologyQR1-502ENmSystems, Vol 5, Iss 6 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	HIV-1 host dependency factors prediction machine learning gene functional network Microbiology QR1-502
spellingShingle	HIV-1 host dependency factors prediction machine learning gene functional network Microbiology QR1-502 Chen Fu Shiping Yang Xiaodi Yang Xianyi Lian Yan Huang Xiaobao Dong Ziding Zhang Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors
description	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.
format	article
author	Chen Fu Shiping Yang Xiaodi Yang Xianyi Lian Yan Huang Xiaobao Dong Ziding Zhang
author_facet	Chen Fu Shiping Yang Xiaodi Yang Xianyi Lian Yan Huang Xiaobao Dong Ziding Zhang
author_sort	Chen Fu
title	Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors
title_short	Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors
title_full	Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors
title_fullStr	Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors
title_full_unstemmed	Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors
title_sort	human gene functional network-informed prediction of hiv-1 host dependency factors
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/cef25fbe9c37412f9427899e3d607fdf
work_keys_str_mv	AT chenfu humangenefunctionalnetworkinformedpredictionofhiv1hostdependencyfactors AT shipingyang humangenefunctionalnetworkinformedpredictionofhiv1hostdependencyfactors AT xiaodiyang humangenefunctionalnetworkinformedpredictionofhiv1hostdependencyfactors AT xianyilian humangenefunctionalnetworkinformedpredictionofhiv1hostdependencyfactors AT yanhuang humangenefunctionalnetworkinformedpredictionofhiv1hostdependencyfactors AT xiaobaodong humangenefunctionalnetworkinformedpredictionofhiv1hostdependencyfactors AT zidingzhang humangenefunctionalnetworkinformedpredictionofhiv1hostdependencyfactors
_version_	1718375990591225856

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

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