Review
Copyright ©The Author(s) 2016.
World J Clin Infect Dis. May 25, 2016; 6(2): 6-21
Published online May 25, 2016. doi: 10.5495/wjcid.v6.i2.6
Table 1 Methods of gene expression profiling and systems biology and their applications in the field of human immunodeficiency virus latency and eradication
MethodApplications to discovery of latency biomarkers and mechanisms of regulation of HIV expressionApplications to studying the LRA mechanisms of action and evaluating combination therapies
Differential gene expressionIdentification of latency biomarkersIdentification of genes responsive to LRA treatment
GO term/pathway enrichment(1) Focusing study efforts upon gene groups of interest (e.g., membrane proteins as biomarkers)(1) Elucidation of mechanisms of action of LRAs
(2) Identification of the mechanisms behind gene expression alterations(2) Selection of gene targets for combination therapy based on gene function in enriched pathway
(3) Delineating the molecular mechanisms contributing to latency control
Network-based analysisIdentification of major regulators involved in HIV latency control, which may be only slightly dysregulated but significantly affect downstream molecules and pathways(1) Elucidation of mechanisms of action of LRAs;
(2) Prioritization of targets for combination therapies based upon type of connectivity (include if it regulates HIV-related processes; exclude if it regulates general intracellular processes)
Consolidating gene expression with other biological data (proteome, integration sites, chromatin features, etc.)(1) Identification of latency biomarkers with transient RNA, but stable protein expression;(1) Identification of post-transcriptional mechanisms of action of LRAs;
(2) Identification of mechanisms of latency control by correlating chromatin features to gene expression(2) Assessment of chromatin features of genes and HIV integration sites responsive to LRA treatment
HIV expression and transcript typePotential biomarker of latencyAssessment of the effectiveness of LRAs for HIV reactivation
Table 2 Features of gene expression studies comparing latently infected vs uninfected cells
Study characteristicsKrishnan and Zeichner[18]Iglesias-Ussel et al[19]Mohammadi et al[42]Evans et al[76]
Cells usedCell lines ACH-2, A3.01, J1.1Primary CD4+ T cellsPrimary CD4+ T cells co-cultured with feeder H80 human brain tumor cell linePrimary resting CD4+ T cells co-cultured with dendritic cells
Virus usedCXCR4 tropic HIV-1 LAV strainCXCR4 tropic GFP reporter virus (GFP inserted in place of Nef)CXCR4 tropic GFP reporter virus with mutations in Gag, Vif, Vpr, Vpu, Env and NefCCR5 tropic GFP reporter virus (GFP inserted into the Nef open reading frame)
Proportion of uninfected cells ≤ 1.1%0%8%-18%99.7%
Proportion of GFP+ or p24+ cells8.20%8.15%Approximately 16%0% (removed by sorting)
Proportion of latently infected cells98.9%100%Approximately 82%-92%Approximately 0.3%
Time of cultureN/A (chronically infected)20-22 d13 wk5 d
Experiment replicates84Not reported4
Gene expression profiling platformMicroarrays (Hs. UniGem2)Microarrays (Agilent-012391 Whole Human Genome Oligo Microarray G4112A)RNA-Seq (polyA RNA library; Illumina HiSeq2000)Microarrays (Illumina Human-Ref8)
Method to identify DEGsParametric one-sample random variance t-test (BRB-Array Tools, P < 0.001)Linear modeling and using an empirical Bayes method with FDR correction (limma)Generalized linear modeling (DESeq, FDR < 0.05)Linear modeling and using an empirical Bayes method (limma, FDR < 0.05)
Databases used for functional analysesNIH mAdbGO consortium;Reactome pathways Ver.40;IPA
MsigDb;MsigDb
KEGG pathways
Total number of DEGs32875227Not reported
Table 3 Limitations of the present studies that identify differentially expressed genes between latently infected and uninfected cells and possible solutions that may enable identification of solid candidate biomarkers of latency
LimitationsSolutions
Small percentage of latently infected cellsIsolate latently infected cells using reporter system OR perform gene expression profiling on a single-cell level
Effect from the exposure to the virus without infectionUse aldrithiol-2 inactivated virus[123] instead of mock-infection to compare to latently infected cell model
Identified differentially expressed genes are ubiquitously expressed on all CD4+ T cellsIdentify a panel of biomarkers that best differentiates between latently infected and uninfected cells
Different models represent different aspects of latency establishmentInclude additional models into analysis; use same statistical approaches to ensure differences in biomarkers are biological, not technical differences
Gene expression profiling can only identify candidate biomarkersPerform experimental validation that latently infected cells can be detected using these biomarkers
Table 4 Features of gene expression studies comparing suberoylanilide hydroxamic acid -treated and untreated primary cells
Study characteristicsBeliakova-Bethell et al[96]Reardon et al[100]White et al[99]Mohammadi et al[42]Elliott et al[25]
Cells usedPrimary CD4+ T cellsPrimary CD4+ T cellsPrimary CD4+ T cellsIn vitro primary CD4+ T cell latency modelTotal blood from HIV-infected individuals on cART
Concentration or dose of SAHA0.34 μmol/L0.34, 1, 3, 10 μmol/L1 μmol/L0.5 μmol/L400 mg orally once daily
Time of treatment24 h24 h24 h8 h and 24 h14 d (samples analyzed at 2, 8 h; 1, 14 and 84 d)
Experiment replicates966Not reported9
Gene expression profiling platformMicroarrays (Illumina HT12 Beadchips version 3)Microarrays (Illumina HT12 Beadchips version 3)Microarrays (Illumina HT12 Beadchips version 3)RNA-Seq (polyA RNA library; Illumina HiSeq2000)Microarrays (Illumina Human HT12 version 4)
Methods to identify DEGsMultivariate permutation test (BRB-Array tools)Dose-response analysis using likelihood ratio test (Isogene) with Bonferroni correction (P < 0.05)Linear modeling (limma, FDR P < 0.05)Generalized linear modeling (DESeq, FDR < 0.05)Linear modeling (limma, P < 0.05)
Databases used for functional analysesGO consortium, KEGG and Biocarta pathways (BRB-Array Tools), MetaCore networksGO consortium, KEGG and Biocarta pathways (BRB-Array Tools), MetaCore networksGO consortium, KEGG pathways (FAIME), MetaCore networksReactome pathways Ver.40; MsigDbIPA, MsigDb
Total number of DEGs1847347729821289Not reported
Table 5 Features of gene expression studies comparing cells treated with latency reversing agents of different functional classes and untreated cells
Study characteristicsJiang et al[95]Mohammadi et al[42]Sung and Rice[97]Banerjee et al[98]
Cells usedPrimary cells from HIV-infected individuals on cARTIn vitro primary CD4+ T cell latency modelPrimary resting CD4+ T cellsJ-Lat 10.6 T cell line
LRA (functional class)Valproic acid (HDACi)Disulfiram (alcohol dehydrogenase inhibitor)Prostratin (PKC agonist)JQ1 (bromodomain inhibitor)
Concentration1 mmol/L (+20 U/mL IL-2)0.5 μmol/L250 ng/mL0.1 μmol/L, 1 μmol/L
Time of treatment6 h8 and 24 h48 h24 h
Experiment replicates4Not reported3Not reported
Gene expression profiling platformMicroarrays (Agilent)RNA-Seq (polyA RNA library; Illumina HiSeq2000)Microarrays (Affymetrix Human Genome U133 Plus 2.0)Microarrays (Affymetrix ST 1.0)
Methods to identify DEGsRosetta Resolver system (P < 0.01)Generalized linear modeling (DESeq, FDR < 0.05)t-test with FDR correctionANOVA (P < 1E-5)
Databases used for functional analysesNot usedReactome pathways Ver.40; MsigDbGO consortium, KEGG pathwaysGO consortium
Total number of DEGs199 (fold change > 3)1892514 (fold change > 1.5)Not reported