Systems biology applications to study mechanisms of human immunodeficiency virus latency and reactivation

Table 1 Methods of gene expression profiling and systems biology and their applications in the field of human immunodeficiency virus latency and eradication

Method	Applications to discovery of latency biomarkers and mechanisms of regulation of HIV expression	Applications to studying the LRA mechanisms of action and evaluating combination therapies
Differential gene expression	Identification of latency biomarkers	Identification 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 analysis	Identification 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 type	Potential biomarker of latency	Assessment of the effectiveness of LRAs for HIV reactivation

LRA: Latency reversing agent; GO: Gene ontology; HIV: Human immunodeficiency virus.

Table 2 Features of gene expression studies comparing latently infected vs uninfected cells

Study characteristics	Krishnan and Zeichner[18]	Iglesias-Ussel et al[19]	Mohammadi et al[42]	Evans et al[76]
Cells used	Cell lines ACH-2, A3.01, J1.1	Primary CD4+ T cells	Primary CD4+ T cells co-cultured with feeder H80 human brain tumor cell line	Primary resting CD4+ T cells co-cultured with dendritic cells
Virus used	CXCR4 tropic HIV-1 LAV strain	CXCR4 tropic GFP reporter virus (GFP inserted in place of Nef)	CXCR4 tropic GFP reporter virus with mutations in Gag, Vif, Vpr, Vpu, Env and Nef	CCR5 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+ cells	8.20%	8.15%	Approximately 16%	0% (removed by sorting)
Proportion of latently infected cells	98.9%	100%	Approximately 82%-92%	Approximately 0.3%
Time of culture	N/A (chronically infected)	20-22 d	13 wk	5 d
Experiment replicates	8	4	Not reported	4
Gene expression profiling platform	Microarrays (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 DEGs	Parametric 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 analyses	NIH mAdb	GO consortium;	Reactome pathways Ver.40;	IPA
		MsigDb;	MsigDb
		KEGG pathways
Total number of DEGs	32	875	227	Not reported

CXCR4: Chemokine (C-X-C motif) receptor 4; LAV: Lymphadenopathy-associated virus; CCR5: Chemokine (C-C motif) receptor 5 (gene/pseudogene); GFP: Green fluorescent protein; polyA: Polyadenylated; DEGs: Differentially expressed genes; BRB: Biometric Research Branch; FDR: False discovery rate; NIH: National Institutes of Health; mAdb: Mad Bee; GO: Gene ontology; MsigDb: Molecular Signature database; KEGG: Kyoto Encyclopedia of Genes and Genomes; IPA: Ingenuity Pathway Analysis; N/A: Not applicable.

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

Limitations	Solutions
Small percentage of latently infected cells	Isolate latently infected cells using reporter system OR perform gene expression profiling on a single-cell level
Effect from the exposure to the virus without infection	Use 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 cells	Identify a panel of biomarkers that best differentiates between latently infected and uninfected cells
Different models represent different aspects of latency establishment	Include 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 biomarkers	Perform 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 characteristics	Beliakova-Bethell et al[96]	Reardon et al[100]	White et al[99]	Mohammadi et al[42]	Elliott et al[25]
Cells used	Primary CD4+ T cells	Primary CD4+ T cells	Primary CD4+ T cells	In vitro primary CD4+ T cell latency model	Total blood from HIV-infected individuals on cART
Concentration or dose of SAHA	0.34 μmol/L	0.34, 1, 3, 10 μmol/L	1 μmol/L	0.5 μmol/L	400 mg orally once daily
Time of treatment	24 h	24 h	24 h	8 h and 24 h	14 d (samples analyzed at 2, 8 h; 1, 14 and 84 d)
Experiment replicates	9	6	6	Not reported	9
Gene expression profiling platform	Microarrays (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 DEGs	Multivariate 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 analyses	GO consortium, KEGG and Biocarta pathways (BRB-Array Tools), MetaCore networks	GO consortium, KEGG and Biocarta pathways (BRB-Array Tools), MetaCore networks	GO consortium, KEGG pathways (FAIME), MetaCore networks	Reactome pathways Ver.40; MsigDb	IPA, MsigDb
Total number of DEGs	1847	3477	2982	1289	Not reported

cART: Combination antiretroviral therapy; polyA: Polyadenylated; DEGs: Differentially expressed genes; BRB: Biometric Research Branch; FDR: False discovery rate; GO: Gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; MsigDb: Molecular Signature database; FAIME: Functional Analysis of Individual Microarray Expression; IPA: Ingenuity Pathway Analysis; HIV: Human immunodeficiency virus.

Table 5 Features of gene expression studies comparing cells treated with latency reversing agents of different functional classes and untreated cells

Study characteristics	Jiang et al[95]	Mohammadi et al[42]	Sung and Rice[97]	Banerjee et al[98]
Cells used	Primary cells from HIV-infected individuals on cART	In vitro primary CD4+ T cell latency model	Primary resting CD4+ T cells	J-Lat 10.6 T cell line
LRA (functional class)	Valproic acid (HDACi)	Disulfiram (alcohol dehydrogenase inhibitor)	Prostratin (PKC agonist)	JQ1 (bromodomain inhibitor)
Concentration	1 mmol/L (+20 U/mL IL-2)	0.5 μmol/L	250 ng/mL	0.1 μmol/L, 1 μmol/L
Time of treatment	6 h	8 and 24 h	48 h	24 h
Experiment replicates	4	Not reported	3	Not reported
Gene expression profiling platform	Microarrays (Agilent)	RNA-Seq (polyA RNA library; Illumina HiSeq2000)	Microarrays (Affymetrix Human Genome U133 Plus 2.0)	Microarrays (Affymetrix ST 1.0)
Methods to identify DEGs	Rosetta Resolver system (P < 0.01)	Generalized linear modeling (DESeq, FDR < 0.05)	t-test with FDR correction	ANOVA (P < 1E-5)
Databases used for functional analyses	Not used	Reactome pathways Ver.40; MsigDb	GO consortium, KEGG pathways	GO consortium
Total number of DEGs	199 (fold change > 3)	189	2514 (fold change > 1.5)	Not reported

cART: Combination antiretroviral therapy; LRA: Latency reversing agent; HDACi: Histone deacetylase inhibitor; PCK: Protein kinase C; polyA: Polyadenylated; DEGs: Differentially expressed genes; FDR: False discovery rate; ANOVA: Analysis of variance; MsigDb: Molecular Signature database; GO: Gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; LRAs: Latency reversing agents.