Gene Expression: the Key to Understanding HIV-1 Infection?

Gene expression profiling of the host response to HIV infection has promised to fill the gaps in our knowledge and provide new insights toward vaccine and cure. However, despite 20 years of research, the biggest questions remained unanswered. A literature review identified 62 studies examining gene expression dysregulation in samples from individuals living with HIV. Changes in gene expression were dependent on cell/tissue type, stage of infection, viremia, and treatment status. Some cell types, notably CD4⁺ T cells, exhibit upregulation of cell cycle, interferon-related, and apoptosis genes consistent with depletion.

SUMMARY

Gene expression profiling of the host response to HIV infection has promised to fill the gaps in our knowledge and provide new insights toward vaccine and cure. However, despite 20 years of research, the biggest questions remained unanswered. A literature review identified 62 studies examining gene expression dysregulation in samples from individuals living with HIV. Changes in gene expression were dependent on cell/tissue type, stage of infection, viremia, and treatment status. Some cell types, notably CD4⁺ T cells, exhibit upregulation of cell cycle, interferon-related, and apoptosis genes consistent with depletion. Others, including CD8⁺ T cells and natural killer cells, exhibit perturbed function in the absence of direct infection with HIV. Dysregulation is greatest during acute infection. Differences in study design and data reporting limit comparability of existing research and do not as yet provide a coherent overview of gene expression in HIV. This review outlines the extraordinarily complex host response to HIV and offers recommendations to realize the full potential of HIV host transcriptomics.

INTRODUCTION

The advent of host transcriptomic profiling has changed the face of medicine, enabling unparalleled insight into biological conditions in three core areas. First, total gene expression profiling has allowed a deeper understanding of pathological mechanisms driving disease, which may aid the development of effective vaccines and novel treatments (1, 2). Second, the ability to practice patient-personalized medicine may predict rates of disease progression, treatment effectiveness, and susceptibility to complications (3–5). And third, at a population level, transcriptomic studies can contribute to improved diagnostics and epidemiological disease monitoring (6, 7).

Recent years have seen an exponential proliferation of studies reporting cellular gene expression in response to infectious diseases, including influenza (8), hepatitis B (9), and human immunodeficiency virus (HIV) (see Fig. 2). Since the first HIV-focused gene expression paper was published 20 years ago, transcriptomic profiling has become more accessible and affordable, with quality and reproducibility having also improved substantially (10). A review of microarray HIV studies by Giri et al. (2006) identified unifying concepts in pathogenesis and highlighted opportunities for application of this burgeoning technology (11). Mehla et al. (2012) published the second major review, with a focus on rates of disease progression, and offered suggestions on improved transcriptomic study design and data interpretation (12). Notably, both of these papers were published before RNA-seq technology came to the fore. More recently, Blazkova et al. (2016) and Li et al. (2017) have established online databases, allowing researchers easy access to differential gene expression data during HIV infection from all available sources (13, 14).

FIG 2.

Breakdown of the technologies used by year in the 459 articles reviewed in full.

Although fairly nascent compared with other applications of this technology, analyses of dysregulated gene expression offer more intricate insight into the pathogenesis of HIV and are the next frontier in the pursuit of definitive prevention and cure. This paper is a scoping review of the important immunological disease mechanisms at work during HIV infection, as illuminated by host transcriptomic profiling to date.

METHODS

In December 2018, a literature search was performed using a PubMed advanced search for terms “(HIV OR human immunodeficiency virus OR SIV OR simian immunodeficiency virus) AND (transcriptom* OR RNAseq OR RNA-seq OR rna seq OR microarray).” The records were screened and reviewed by two investigators as shown in Fig. 1, with exclusion criteria designed to identify only gene expression studies of HIV-infected human host samples. This specifically excluded cultured cell lines or primary cells from healthy donors later infected with HIV in vitro, both of which are less reflective of the breadth of the systemic host response. Further excluded were papers focusing on (i) the effects of various treatment regimes, (ii) specific coinfections or comorbidities where HIV infection alone was not a control group, (iii) differential vulnerability to and progression during HIV infection, and (iv) viral gene expression during HIV infection, topics worthy of review separately.

FIG 1.

Approach to screening and reviewing relevant articles identified by literature search.

The 459 articles reviewed in full clearly demonstrate the annual increases in publications and trends in the technologies utilized over time, from PCR and tag-based technologies such as serial analysis of gene expression (SAGE) to microarray to RNA-seq technology (Fig. 2). Recent years have seen increased reanalysis of previously generated transcriptome data, reflecting the ongoing emergence of improved methods of handling and analyzing the large-scale data sets. Table 1 lists the 62 records included in this scoping review, and Table 2 summarizes the key results identified per cell population or tissue type.

TABLE 1.

Gene expression analyses included in the qualitative synthesis by tissue type^a

Tissue type	First author (yr)	Technology	Tissue	Stage/treatment (n)	Sex	Clade	FDR	P	FC	HM	DEG	PW	Reference
Whole blood	Schreiber (2011)	Microarray	Whole blood	cHIV+/ART− (14); HIV− (18)	B	U	✓	<0.05	>2	✓	✓	✓	19
	Gorenec (2016)	RT-PCR	Whole blood	aHIV+/ARTu (3); cHIV+/ARTu (3)	B	U	NA	<0.05	U		✓❋↑		27
	Huang (2018)	Microarray	Plasma	eHIV+/ART− (17); cHIV+/ART− (17); HIV− (17)	PM	U	✓	<0.05	>2		✓❋↑		31
PBMC	Motomura (2004)	Microarray	PBMC	HIV+ stage u/ARTu (21); HIV− (3)	B	U	✘	<0.05	U	✓	✓❋↑		36
	Boutboul (2005)	Microarray	PBMC	cHIV+/ART− (6); cHIV+/ART+ (12)	U	U	✘	<0.05	U	✓			25
	Ansari (2006)	Microarray	PBMC	cHIV+/ART− (4); cHIV+/ART+ (16)	B	U	NA	<0.05	>2		✓❋↑		24
	Montano (2006)	Microarray	PBMC	HIV+ stage u/ART− (25); HIV− (20)	F	C	✓	<0.05	>1.5	✓		✓	17
	Zhou (2012)	Microarray	PBMC	cHIV+/ART− (5); HIV− (5)	M	U	✘	<0.05	U		✓❋↑	✓	21
	Duskova (2013)	Microarray	PBMC	HIV+ stage u/ART+ (63); HIV− (36)	B	U	✓	<0.05	>2	✓	✓❋↑	✓	20
	Crothers (2016)	Microarray	Peripheral leukocytes	cHIV+/ART+ (9); HIV− (9)	M	U	✓	<0.01	U	✓		✓	23
	Pérez-Matute (2016)	RNA-seq	PBMC	cHIV+/ART+ (11); HIV− (7)	B	U	✓	≤0.05	>1		✓❋↑	✓	22
	Ballegaard (2017)	Microarray	PBMC	cHIV+/ART+ (40); HIV− (29)	PM	U	✓	<0.05	Tu	✓	✓❋↑	✓	37
	Zhang (2018)	RNA-seq	PBMC	eHIV+/ART− (3); HIV− (3)	M	U	U	≤0.05	>1.5		✓	✓	18
CD4+ and CD8+ lymphocytes	Chun (2003)	Microarray	CD4+ T cells	HIV+ stage u/ART− (4); cHIV+/ART+ (6); HIV− (4)	U	U	✓	U	U	✓	✓	✓	44
	Hyrcza (2007)	Microarray	CD4+ and CD8+ T cells	eHIV+/ART− (5); cHIV+/ART− (5); HIV+ LTNP/ART− (5); HIV− (5)	M	U	✓	U	U		✓	✓	30
	Sedaghat (2008)	Microarray	CD4+ T cells	cHIV+/ART− (21); cHIV+/ART+ (7); HIV− (17)	U	U	✓	<0.01	Tu	✓		✓	41
	Wu (2008)	Microarray	CD8+ T cells	cHIV+/ART+ (5); HIV+ LTNP/ART− (4); HIV− (5)	U	U	✓	<0.01	Tu	✓	✓❋↑	✓	52
	Rotger (2010)	Microarray	CD4+ T cells	cHIV+/ paired pre- and post-ART (37); cHIV+/ART+ (28); cHIV+/ART− (72); HIV+ EC/ARTu (16); HIV− (3)	U	U	✓	<0.01	U	✓	✓❋↑	✓	43
	Rotger (2011)	Microarray	CD4+ and CD8+ T cells	e/cHIV+ RP/ART− (27); cHIV+ LTNP/ART− (5); cHIV+ EC/ART− (9); cHIV+/ARTu (5)	PM	S*	✓	<0.05	Tu	✓		✓	32
	Vigneault (2011)	Microarray	CD4+ T cells	cHIV+ EC/ART− (12); cHIV+/ART+ (14); HIV− (9)	B	U	✓	<0.11	1.5	✓	✓❋↑	✓	46
	Wu (2011)	Microarray	CD4+ and CD8+ T cells	HIV+ stage u/ART+ (10); cHIV+ LTNP/ART− (4); HIV− (5)	M	G	✓	<0.1	>2	✓	✓❋↑	✓	45
	de Masson (2014)	Microarray	CD4+ T cells	cHIV+ EC/ART− (7); cHIV+ LTNP/ART− (7); HIV− (7)	PM	U	✓	<0.05	Tu	✓	✓		49
	Xu (2014)	Microarray	Reanalysis of Hyrcza (2007)	eHIV+/ART− (5); cHIV+/ART− (5); cHIV+ LTNP/ART− (5); HIV− (5)	M	U	✓	< 0.01	> 1	✓	✓	✓	42
	Olvera-Garcia (2016)	Microarray	CD4+ T cells	cHIV+/ART− (6); HIV− (9)	U	U	✓	<0.05	≥0.5	✓	✓❋↑	✓	48
	Chowdhury (2018)	Microarray	CD8+ T cells	cHIV+ EC/ART− (51); cHIV+/ART+ (32); HIV− (10)	PM	U	✓	<0.05	>1.5	✓	✓	✓	53
B lymphocytes	Moir (2004)	Microarray	B cells	cHIV+/ART+ (10); cHIV+ ART− (10); HIV− (10)	U	U	✓	U	2	✓	✓		54
	Kardava (2014)	RT-PCR	B cells	HIV+ stage u/ARTu (18)	PM	B	✓	<0.05	Tu	✓	✓❋↑		56
	Cotugno (2017)	RT-PCR	B cells	vHIV+/ART+ (23); HIV− (10)	B	U	✘	<0.05	Tu	✓	✓❋↑	✓	55
Dendritic cells	Griesbeck (2017)	RT-PCR	Dendritic cells	cHIV+/ART+ (14); HIV− (13)	M	U	✓	<0.05	2	✓	✓❋↑		60
Monocytes and macrophages	Pulliam (2004)	Microarray	Monocytes	HIV+ stage u/ART+ (23); HIV− (5)	U	U	U	<0.05	1.5		✓	✓	66
	Giri (2009)	Microarray	Monocytes	cHIV+/ART− (13); HIV− (12)	B	U	✓	≤0.05	≥2	✓	✓❋↑	✓	62
	Kaner (2009)	Microarray	Alveolar macrophages	HIV+ stage u/ART+ (5); HIV− (11)	PM	U	✘	<0.05	U		✓❋↑		67
	Van den Bergh (2010)	Microarray	Monocytes	HIV+ stage u/paired pre- and post-ART (29); HIV− (15)	U	U	✓	<0.01	≥1.5	✓	✓❋↑	✓	63
	Gordon (2013)	Microarray	Alveolar macrophages	HIV+ stage u/ART− (37); HIV− (41)	PM	U	✓	<0.0001	≥1.88		✓❋↑	✓	68
	Nagy (2013)	Microarray	Monocytes and dendritic cells	cHIV+/ART− (34); cHIV+/ART+ (63); HIV− (40)	PM	U	✓	≤0.05	≥1.5	✓		✓	64
	Wu (2013)	Microarray	Monocytes	cHIV+/ART+ (10); HIV− (4)	U	U	✓	<0.05	>2	✓	✓❋↑	✓	65
	Ginsberg (2018)	Microarray	Brain macrophages	cHIV+/ART on and off (10); HIV− (5)	B	A	✓	<0.01	Tu		✓	✓	69
Natural killer cells	Kottilil (2007)	Microarray	Natural killer cells	HIV+ stage u/ART− (5); HIV+ stage u/ART+ (5); HIV− (5)	U	U	✓	<0.01	U	✓	✓❋↑		72
	Boeijen (2018)	RNA-seq	Natural killer cells	cHIV+/ART− (6); HIV− (20)	M	U	✓	q<0.2	>1.5	✓	✓❋↑	✓	71
Lymphoid tissue	Guadalupe (2003)	Microarray	GALT	aHIV+/ART− (2); e/cHIV+/ART− (11); cHIV+ LTNP/ART− (3); cHIV+/ART+ (1); HIV− (6)	PM	U	✘	U	U	✓	✓❋↑		28
	Sankaran (2005)	Microarray	GALT	HIV+ stage u/ART− (4); cHIV+ LTNP/ART− (3); HIV− (4)	PM	U	U	<0.05	1.5	✓	✓❋↑	✓	76
	Guadalupe (2006)	Microarray	GALT	e/cHIV+/ART+ (3); cHIV+/ART+ (7); HIV− (4)	U	U	U	≤0.05	≥1.5	✓	❋↑	✓	33
	Li (2009)	Microarray	Lymph node	aHIV/ART− (9); e/cHIV+/ART− (9); cHIV+ AIDS/ART− (4); HIV− (5)	PM	U	U	<0.05	≥1.7	✓	✓❋↑	✓	29
	Lerner (2011)	Microarray	GALT	cHIV+/ post-ART interruption (3); HIV− (3)	M	U	U	≤0.05	>1.5	✓	✓	✓	78
	Colineau (2015)	RT-PCR	Spleen	cHIV+/ART− (4); HIV− (4)	U	U	U	<0.05	U		✓❋↑	✓	74
	Kis (2016)	Microarray	GALT	cHIV+/ART− (7); cHIV+/ART+ (6); HIV− (5)	PM	U	U	≤ 0.05	1.5	✓	✓❋↑	✓	75
	Xu (2017)	Microarray	GALT	cHIV+/ART− (3); HIV− (3)	M	U	U	<0.05	>2	✓	✓	✓	77
Secondary tissues	Kusko (2012)	Microarray	Muscle tissue	HIV+ stage u/mixed ART (9); HIV− (67)	M	U	✓	<0.05	Tu	✓	✓	✓	90
	Morse (2012)	Microarray	Muscle and adipose tissue	cHIV+/ART− (17); cHIV+/ART+ (29); HIV− (15)	PM	U	✘	≤0.05	≥0.5	✓	✓❋↑	✓	89
	Noorbakhsh (2010)	Microarray	Brain	cHIV+/ART− (3); cHIV+/ART+ (1); HIV− (4)	PM	U	U	<0.05	>2	✓	❋	✓	84
	Borjabad (2011)	Microarray	Brain	cHIV+/ART− (8); cHIV+/ART+ (7); HIV− (6)	PM	B	✓	<0.05	>1.5	✓	✓❋↑	✓	81
	Gelman (2012)	Microarray	Brain	cHIV+/ART+ (18); HIV− (6)	PM	U	✓	<0.05	>2	✓	✓❋↑	✓	82
	Polyak (2013)	RNA-seq	Brain	HIV+ stage u/ARTu (4); HIV− (4)	U	U	✓	<0.05	Tu			✓	83
	Sanna (2017)	Microarray	Brain	cHIV+/ART− (2); cHIV+/ART+ (16); HIV− (6)	PM	U	✓	<0.01	U	✓	✓	✓	79
	Solomon (2017)	Nanostring	Brain	cHIV+/mixed ART (6); HIV− (8)	PM	U	✓	<0.05	Tu	✓	✓❋↑		80
	Farhadian (2018)	RNA-seq	Brain (CSF and blood)	cHIV+/ART+ (2); HIV− (2)	M	U	✓	<0.01	U	✓			85
	Marcantoni (2018)	RNA-seq	Platelets	cHIV+/ART+ (6); HIV− (3)	B	U	✓	≤0.01	Tu	✓	✓❋↑	✓	91
	Kozak (2013)	Microarray	Retina	cHIV+/ART+ (10); HIV− (6)	M	U	✓	≤0.01	Tu	✓	✓	✓	92
	van der Kuyl (2002)	SAGE	Tumor tissue	cHIV+/ART+ (1)	M	U	✘	<0.01	Tu		✓❋↑		94
	Thapa (2011)	Microarray	Tumor tissue (tonsillar)	cHIV+ AIDS/ARTu (24); HIV− (4)	U	U	✘	<0.05	Tu		✓		96
	Tan (2014)	Microarray	Tumor tissue	cHIV+ AIDS/ARTu (4)	U	U	U	<0.05	>2	✓	✓	✓	93
	Tso (2018)	RNA-seq	Tumor tissue	cHIV+ AIDS/ART+ (4)	M	U	✓	<0.05	Tu	✓	✓❋↑	✓	95

^aFDR, false discovery rate/adjustment for multiple comparisons specified; P, P value specified; FC, fold change specified; HM, heat map presented in results; DEG, differentially expressed gene analysis presented in results; PW, pathway analysis presented in results; Reference, corresponding reference in this paper; aHIV⁺, acute HIV infection; eHIV⁺, early HIV infection; cHIV⁺, chronic HIV infection; vHIV⁺, vertically transmitted HIV infection; stage u, unclear stage of infection; HIV⁻, HIV negative; ART⁺, on antiretroviral therapy; ART⁻, not on antiretroviral therapy; mixed ART, cohort has a mixture of treated and untreated individuals; ARTu, treatment status unspecified; LTNP, long-term nonprogressor; EC, elite controller; RP, rapid progressor; M, male; F, female; B, both male and female; PM, predominately male; U, unspecified/unclear; S*, subtypes stated in the paper, namely A, B, BD, BF, C, F, G, 01_AE 01_AG, 02_AG, 11_CPX, and/or U (though only a subset of these contributed gene expression results); ✓, analysis performed; ✘, data not presented; ❋, full list of differentially expressed genes presented; ↑, direction of differential expression specified; Tu, threshold unspecified; NA, not applicable, e.g., too few tests to warrant adjustment for multiple comparisons, or statistical methods for differential gene expression which do not depend on fold change and/or P value.

TABLE 2.

Summary of key findings per cell population or type^a

^aSee references 17 to 23, 29 to 33, 36, 37, 41 to 45, 48, 52, 54 to 56, 60, 62 to 65, 71 to 76, 80, 81, 83, and 89 to 96.

RESULTS: THE GENE EXPRESSION OF HIV-DRIVEN IMMUNOLOGICAL DAMAGE

HIV Impact on Gene Expression of Mixed Cell Populations

Peripheral whole blood is an informative source of biological data easily collectable in most research and field settings. Transcriptomic studies on whole blood enable examination of the overarching effects of HIV on its multiple target cells, as well as systemic effects via indirect intercellular networks, which may be overlooked when profiling single tissues or cell types. In addition, biomarkers that are replicable in peripheral blood are optimal for translation into effective point-of-care assays, which is especially important in the context of HIV, where much of the burden is focused on resource-limited settings. A limitation to this approach is the high erythrocyte globin mRNA content in whole blood, which can substantially interfere with gene expression profiling through the commandeering of cDNA synthesis capacity and obscuring the expression levels of biologically important transcripts (15). This issue is somewhat overcome by globin depletion methods (such as the PAXgene system), which are able to reduce the globin content of blood samples by around 80%, though are undermined by lower RNA yields overall (15).

The use of peripheral blood mononuclear cells (PBMC) circumvents the issue of globin RNA by separating the key cells of interest from other contents in blood. This requires basic laboratory equipment and skilled staff near the point of collection but allows higher RNA yields and isolation of immune cells, the targets of HIV. Transcriptomic analyses of whole blood and to a lesser extent PBMC are complicated by difficulties accounting for the various contributions of different cell types to the resultant profile (16). For example, massive dysregulation in one cell type may be masked by negligible or opposite changes in others. This is unimportant when the objective is global biomarker identification but may be troublesome for meaningful analysis of disease mechanisms. As HIV infection causes predictable alterations in known cell populations, statistical adjustment of the high-throughput sequencing data for these cell types is important for accurate interpretation, especially during longitudinal studies.

With respect to the aforementioned limitations, comparing differential gene expression in peripheral blood cells between untreated HIV-infected (HIV⁺) and HIV-uninfected (HIV⁻) individuals is a simple method to gain broad insight into the basic host response to infection. Despite the simplicity of design, few such studies have done this successfully, likely due to (i) the growing ubiquity of antiretroviral therapy (ART), limiting access to pretreatment samples, (ii) the failure to distinguish between treatment groups during data analyses, sometimes due to study size limitations, and (iii) the failure to present these basic comparisons due to a focus on hypothesis-specific data. The three analyses identified, one of whole blood and two of PBMC (using 14, 25, and 3 HIV⁺ subjects, respectively), identified significant gene expression dysregulation as a consequence of HIV infection (17–19). Specifically, 374, 1,733, and 2,049 genes, respectively, were found to be differentially expressed, including the upregulation of immune and inflammatory genes, particularly among known antiviral mechanisms and interferon-inducible genes (17, 19). STAT1 and IRF7 were important transcription factors modulating these hubs of activity (19). Genes associated with viral transcription, nucleosome assembly, and mRNA metabolism were also found to be upregulated (17, 18). Concordance of conclusions is limited, however, because two of the studies did not specify the duration of infection (17, 19).

More studies (one RNA-seq and four microarray) have captured HIV⁺ subjects already receiving ART and made comparisons with HIV⁻ individuals; notably, these studies also have modest sample sizes, with 63 HIV⁺ subjects (20) being the largest. Although the interference of ART substantially limits insight into pathogenic mechanisms, similar patterns emerged. Innate immunity pathways and antiviral factors were variably regulated, while inflammatory factors and cytokines were more consistently upregulated (20, 21). Antigen processing and presentation (21–23), T cell activation and cellular defenses (22), and several key signaling pathways (including chemotactic signaling [22], interferon [IFN] signaling, interleukin 12 [IL-12] and STAT4 pathways [23], and mitogen-activated protein kinase [MAPK], transforming growth factor beta [TGFβ], and neurotrophin signaling [21]) were all reported to be upregulated in HIV⁺ individuals. Various descriptions of downregulated genes include those involved in immunoglobulin responses (22) and ribosomal, translation, and transcription processes (23). Three studies noted that the gene expression profiles of individuals with treated HIV infection correlated with viremia, with greater upregulation of inflammatory and cytokine/chemokine pathway genes in highly viremic individuals (20, 24), as well as the gene encoding perforin, one of the key enzymes involved in lysis of infected cells (25). IL7R upregulation demonstrated an inverse correlation with viremia (25).

Alongside expression profiling between HIV-positive and -negative individuals, the study of differential gene expression between HIV⁺ individuals at different stages of infection provides a greater depth of information during the course of HIV-related illness. Untreated HIV infection typically progresses through three clinical stages (Fig. 3). Acute HIV infection is the period between transmission and detection of HIV-specific antibodies in blood (seroconversion), a duration of 3 to 12 weeks, as determined using standard Western blotting (26). This stage is characterized by a rapid increase in HIV viral load, an early decline in CD4⁺ T cells, acute inflammation, and often a transient flu-like illness. In practical terms, definitions of acute (27–29), early (18, 30–32), and primary (33) HIV are not absolute and vary greatly in the literature. Overall, there is a paucity of information regarding transcriptomic dysregulation during the earliest stages of HIV infection, including acute HIV infection; this is reflective of the nonspecific presentation of the earliest stages of HIV and the diagnostic and recruitment dilemmas this poses. The second stage, chronic asymptomatic infection, ranges in duration from 4 to 12 years (34) and is characterized by steady viral load, a gradual decline in CD4⁺ T cells, and continued (partially successful) adaptive immune response against HIV. In the absence of treatment, immune compromise permits development of the opportunistic infections and comorbidities that constitute AIDS; the mean time between transmission and AIDS-related death is 10 to 11 years (34, 35).

FIG 3.

The three stages of HIV infection, as distinguished by biomarkers in peripheral blood.

Only two studies investigating the differential gene expression across the stages of HIV infection were identified, and notably, neither used paired or longitudinal samples from the same patients and both had modest sample sizes. Motomura et al. (2004) used cDNA microarray to compare CDC classification C patients (late-stage disease, n = 7) with classification A patients (early disease, n = 10) and found upregulation of 20 genes associated mainly with immature T lymphocyte differentiation, apoptosis signaling, and active HIV replication (36). Late-stage patients demonstrated downregulation of various cell membrane markers of mature T lymphocytes, possibly reflecting advanced immunological destruction (36). Although limited by small sample size (n = 6) and PCR array methods, Gorenec et al. (2016) demonstrated higher expression of various interferon genes, interleukin genes, TNF family genes, and growth factor genes in the late stages of infection than in the early stages (27).

While the majority of studies interrogate mRNA, the contribution of microRNA (miRNA) and other noncoding or regulatory RNA types to the modulation of gene expression during HIV infection has been increasingly recognized. Four studies investigated the patterns of these molecules in peripheral blood during HIV infection. In PBMC, Zhang et al. (2018) identified 1,365 circular RNAs (involved in immune response, inflammatory response, and virus defenses) and 1,304 mature miRNAs (most significantly, miR-novel-chr21_21352, miR-101-3p, and miR-31-5p) differentially expressed during untreated HIV infection, in comparison with HIV⁻ controls (18). Three miRNAs were differentially expressed during treated HIV infection (miR-210, miR-7, and miR-331), all independently associated with markers of systemic inflammation (37). In some cases, miRNA expression in PBMC appears dependent on viral load; of the 191 differentially expressed miRNAs identified by Duskova et al. (2013), four were consistently different between clinical groups based on viral load, with miR-1262 independent of viral load but miR-1275, miR-483-5p, and miR-650 showing particular upregulation during high viremia (20). In plasma, differential expression of circulating miR-3162-3p was able to distinguish acute HIV infection from chronic infection and healthy controls; other molecules differentially expressed between these clinical groups were not discussed further (31).

HIV Impact on Gene Expression of Single-Cell Populations

CD4⁺ T lymphocytes.

CD4⁺ T lymphocytes are the primary target of HIV-1 infection. Within the first weeks of infection, HIV-1 causes a striking loss of cells in lymphoid tissue, preferentially from gut-associated lymphoid tissue (GALT), where biopsy studies have shown a 60 to 80% depletion of resident CD4⁺ T cells (28, 38). There is also a well-described early drop in the numbers of circulating CD4⁺ T lymphocytes; populations partially recover within several months of infection, before a predictable long-term decline in the absence of treatment (39). In addition to a reduction in quantity, CD4⁺ T cells exhibit a severely impaired HIV-specific immune response, with effector cells rapidly expanding to exhaustion (40). Given the major role of CD4⁺ T cells in coordinating the humoral immune response of T and B cells, these effects lead to progressive and ultimately fatal immunological failure. The CD4⁺ T cell interaction with HIV is thus of obvious interest; however, this is one area where the field of transcriptomics appears to have added relatively little information. Existing studies fail to capture CD4⁺ T cells from contexts other than peripheral circulation and fail to capture longitudinal changes. Most fail to provide baseline transcriptomic comparisons between HIV⁺ and uninfected CD4⁺ T cell subsets, and all struggle with sample size and power limitations.

Sedaghat et al. (2008) demonstrated that activated CD4⁺ T cells (CD25⁺) from 11 untreated HIV⁺ individuals exhibit upregulation of cell cycle and type 1 interferon-related genes, as well as substantial modulation of pro- and antiapoptotic genes, in comparison with 9 HIV⁻ individuals, suggesting that chronic activation and a hyperproliferative state contribute mechanistically to cell depletion (41). Hyrcza et al. (2007) found that T cell transcriptomic profiles are established early in infection and persist, but clinical groups were combined to power further analyses (30). A later reanalysis of the same data, performed by Xu et al. in 2014, found that CD4⁺ T cells from 5 subjects with early HIV infection showed upregulation of cell cycle and immune response genes in comparison with 5 HIV⁻ subjects (42). Compared with subjects with aviremic infections, CD4⁺ T cells from viremic subjects had enriched expression of many type I interferon-stimulated and cell cycle- and differentiation-related genes (30, 43) and genes involved in transcription regulation, RNA processing and modification, and various steps of protein trafficking/vessel transport (44), ubiquitin-proteasome degradation machinery (43), and nonlytic complement activation (45). Individuals able to sustain adequate virological control with treatment (elite controllers) often had CD4⁺ T cell gene expression signatures similar to those of ART-treated subjects (46).

Resting CD4⁺ T lymphocytes make up the majority of CD4⁺ T cells and are susceptible to invasion by HIV, but unlike their activated counterparts, are largely nonpermissive to HIV replication due to multiple factors reviewed in detail elsewhere (47). In the absence of active replication, these cells serve as latent viral reservoirs of HIV, even despite effective treatment. Olvera-Garcia et al. (2016) found that CD4⁺ central memory cells of untreated HIV⁺ individuals were not more differentiated toward an effector fate than HIV⁻ controls but were more polarized toward Th1 functions (48). These cells also had heightened interferon signaling and other inflammatory factors (48). Individuals with controlled viremia had greater proportions of resting cells, upregulated T cell receptor and costimulatory signaling genes, and downregulation of interferon-related genes than those in whom viremia persisted (49).

No known HIV restriction factors or transcription inhibitors were identified in these transcriptomic studies, although Blimp-1, a known repressor of type I IFN-related genes, was overexpressed and hypothesized to play a role in repressing active replication in resting cells (49). Chun et al. (2003) addressed the question of whether the resting T cell reservoirs are able to contribute to chronic inflammation through virion release (44). They found that plasma viremia may generate favorable conditions of metabolic energy and upregulation of secretory pathways in resting cells, adequate for completion of viral assembly and secretion in the absence of activation stimuli (44). Finally, through the novel approach of examining the regulation of genes involved at different cell cycle stages, Olvera-Garcia et al. (2016) proposed that HIV⁺ resting central memory cell death appears to be driven by increased entry into the cell cycle, followed by cycle arrest at G₂ and/or M and subsequent programmed cell death (48).

CD8⁺ T lymphocytes.

In contrast to the depleted and deficient CD4⁺ response, CD8⁺ T lymphocytes are not direct targets of HIV and are able to generate anti-HIV responses that are robust and specific, exerting selective pressures that contribute to viral control in both the early and chronic stages of infection (50). The main effector mechanisms of these T cells include the lysis of virus-infected cells and the generation of cytokines and chemokines to augment the humoral response. However, in the end, virus-specific CD8⁺ T cells are unable to control viral replication, likely due to a culmination of intrinsic factors and systemic dysfunction, particularly impaired dendritic cell (DC) and macrophage antigen presentation (51). Two studies have identified a greater number of differentially expressed genes in CD8⁺ T cells than in CD4⁺ T cells (more than 21- and 22-fold, respectively) during HIV infection (30, 32). Compared with HIV⁻ subjects, untreated HIV⁺ individuals have CD8⁺ T cell transcriptional profiles that are enriched for cell cycle, mitosis, and virus response genes (42). The CD8⁺ T cells of HIV⁺ viremic individuals demonstrate enriched interferon responses, mitochondrion and cell proliferation, and differentiation factors (52), as well as upregulation of genes involved in complement activation, cell signaling, and antigen processing and presentation (45), in comparison with aviremic groups. The subset of downregulated genes in these cells is not apparently driven by any single upstream factors but is inclusive of many known immune regulatory genes (30). Unlike their CD4⁺ counterparts (46), CD8⁺ T cell transcriptome profiles of elite controllers of HIV more closely resemble uninfected individuals than treated individuals (53).

B lymphocytes.

B cells are key elements of humoral immunity, with roles in antigen presentation and production of antibodies, among others. These cells have the capacity to interact directly with HIV antigens at the cell surface via CD21 receptors but are not targets of invasion and viral replication. Nevertheless, B cells undergo significant perturbation and aberrant activation during HIV infection. Three studies have attempted to characterize the influence of HIV on the transcriptome of B cells isolated from PBMC. Chronic viremic HIV infection caused B cells to transcribe significantly more interferon-stimulated genes in fairly generic nonspecific patterns of infection response and inflammation (54). HIV also pushed B cells toward a path of terminal differentiation, as evidenced by the heightened expression of various surface markers, proteins of secretory pathways, and immunoglobulin-related genes (54). The memory B cell population was skewed from a predominance of resting memory cells (as is the case in healthy individuals) toward tissue-like and activated memory B cells, upregulating genes associated with HIV-induced cellular exhaustion, apoptosis, and inflammation (55, 56). This is hypothesized to impair the ability of HIV⁺ individuals to mount responses to known stimuli and maintain long-term responses to vaccines and enhances vulnerability to malignancy and other comorbidities (55). Furthermore, these perturbations in the memory component persist despite ART-induced virological control (55).

DCs.

Dendritic cells (DCs) in mucosal tissues are among the first cells to encounter HIV posttransmission and are integral in the establishment and maintenance of host infection. DCs are responsible for detection of microbial antigens, stimulation of an initial immune response, antigen presentation, and coordination of effector cell responses. HIV has evolved ways to exploit DC function to propagate infection and evade the host response. Significant amounts of HIV-1 virus captured by DCs are endocytosed and destroyed within 24 h, leaving a small yet important proportion available for de novo replication and transfer to T cells (57). HIV-1 exerts substantial influence on the DC transcriptome in vitro, through the altered expression of lysosomal enzymes and de novo production of virus (58, 59). Only one study examined peripheral DCs from 14 individuals with treated HIV infection and did so in the context of providing a control group for investigation of hepatitis C coinfection (60). The paper makes note of significant upregulation of STAT1 and IRF7 mRNA in HIV⁺ individuals, indicating persistent activation of peripheral DC IFN-α pathways despite suppressive ART (60).

Monocytes and macrophages.

Monocytes are targets of HIV and play several key roles during infection (61). Unlike their CD4⁺ T cell counterparts, the monocyte population is not depleted in this context of ongoing active viral replication. Giri et al. (2009) identified a stable pattern of gene expression supporting monocyte survival during HIV infection, that is, a downregulation of proapoptotic genes, and an upregulation of antiapoptotic genes, predominantly in the p53, CD40L, tumor necrosis factor (TNF), and MAPK networks (62). A signature of 38 apoptosis-related genes was able to distinguish HIV⁺ from HIV⁻ individuals with 97% accuracy (62). Van den Bergh et al. (2010) supported this finding of significant modulation of apoptosis and cell cycle, also noting a perturbation of lipid metabolism, proteasome activity, and protein trafficking during HIV infection (63). Monocytes are thus able to act as a cellular virus reservoir, which can persist long term following differentiation into tissue macrophages. Although able to maintain numbers, monocytes exhibit aberrant activation and functional impairment. Chemotaxis pathway suppression in monocytes of HIV⁺ individuals suggests that monocyte-led immune cell recruitment is impaired (63). One study of CD11c⁺ antigen-presenting cells (predominantly monocytes, with some myeloid DCs) demonstrated an upregulation of Toll-like receptors during untreated HIV infection; these recognize bacterial ligands and viral RNA and possibly contribute to a chronic inflammatory state during HIV illness (64). Proinflammatory genes show a mixed picture of increased and decreased expression in monocytes, some of which are correlated with viremia and remain dysregulated despite ART (63). The interferon-induced NAMPT gene was noted to be upregulated in correlation with viral load and demonstrated successful inhibition of HIV-1 viral binding in vitro (63). In comparison with aviremic infection in HIV⁺ individuals, viremic infection is associated with higher expression of innate immune pathways, including chemokine signaling, IgA production, complement cascade, phagocytosis, and antigen presentation (65). Viremia has also been associated with increased expression of MCP-1 (63, 66), which may coordinate the migration of HIV-infected monocytes to the brain, playing a role in HIV-associated neurodegeneration.

Transcriptomic studies of macrophages infected with HIV in vivo remain limited, largely due to difficulties in isolating or biopsying tissue macrophages in sufficient numbers. Two microarray studies collected macrophages via bronchoalveolar lavage (67, 68), and one collected brain microglia (the resident macrophages of the central nervous system) at autopsy (69). Findings revealed that alveolar macrophages express significant upregulation of classical activation pathways during HIV infection (68). Matrix metalloproteinases, enzymes involved in the breakdown of extracellular matrices, including those of the lung architecture, were upregulated in alveolar macrophages of HIV⁺ smokers compared to those in HIV⁻ smokers (67). Microglia exhibit significantly altered gene expression during HIV infection, with several disrupted pathways (stress- and immune-related factors, neurotrophic factors, kinases, caspases, and apoptosis-related genes) correlating with various degrees of neurological impairment (69). Differentiated macrophages in other tissues have not been examined.

NK cells.

Natural killer (NK) cells in peripheral circulation act as a unique bridge between innate and adaptive immune responses, with the ability to recognize malignant or virally infected cells, and enact immediate effector mechanisms (including direct cytotoxic effects). Upon activation, they generate cytokines and chemokines that mediate the type and strength of the adaptive immune response that follows, yet during HIV-1 infection, they experience aberrant function, which aids viral evasion of immune surveillance. NK cells may be activated via systemic inflammatory signals and CCR5- or CXCR4-stimulated intracellular signaling but are also susceptible to direct HIV-1 infection in a CD4-dependent manner (though most lack CD4 surface expression) (70). Boeijen et al. (2018) performed RNA-seq on NK cells from 6 untreated HIV⁺ individuals in comparison with 8 healthy controls and found 241 downregulated genes, predominantly in pathways related to ribosome assembly, inhibitory receptor, and cytotoxicity, as well as 31 upregulated genes, in IFN-, STAT1-, and STAT2-driven patterns (71). Earlier work by Kottilil et al. (2007) demonstrated a role for HIV viremia in NK cell activation and activation-induced cell death, with HIV⁺ viremic individuals (n = 5) overexpressing genes associated with promotion of apoptosis, response to biotic stimulus, and cell communication, compared to both HIV⁺ aviremic (n = 5) and uninfected (n = 5) individuals (72).

HIV Impact on Gene Expression of Mixed Cell Lymphoid Tissues

Lymph nodes.

Li et al. (2009) used microarray technology to profile inguinal lymph node tissue from HIV⁺ individuals at different stages of infection (29). Sample sizes were modest (9 acute stage, 9 asymptomatic stage, 4 AIDS stage, and 5 uninfected); however, the clinical stage-based classifications were able to reveal striking differences in gene expression profiles throughout the course of HIV disease (29). The authors argued that lymphoid tissue best captures the site of virus production and pathology and is likely to best reflect host-pathogen interactions relevant to pathogenesis. However, as lymphoid tissue is a mixed cell sample, it would be helpful to describe the contributions of constituent cell types (29). The acute stage had a 60% greater number of differentially expressed genes than any other stage of infection (429 upregulated and 107 downregulated). Upregulated genes were related to immune activation, immune defense, apoptosis, metabolism, and tissue repair, followed by a relative quiescence in the asymptomatic stage, with return of expression levels to baseline in most pathways (29). The AIDS stage reflected a further shift, with 58% of differentially expressed genes downregulated, specifically those involved in immune activation, apoptosis, and tissue repair (29). Only 46 genes remained differentially expressed throughout the course of infection, including the CD38 gene, as well as genes associated with NK cells, IFNs, CD8⁺ T cell cytotoxicity, and antiviral chemokines; these are hypothesized to be a key set of genes required by the host to partially contain the virus (29). Further analysis of these results by the same authors found that host gene expression patterns in lymph node tissues were significantly associated with viral load, with 95% of differentially expressed genes negatively correlated and only 5% (predominantly associated with innate and adaptive immune defenses) positively correlated (73).

Spleen.

The spleen has also demonstrated changes in immune cell transcriptomics during HIV infection. Despite HIV infection causing an increase in splenic populations of follicular helper T cells (Tfh) and germinal center (GC) Tfh cells, their ability to promote signaling for B cell maturation and differentiation was impaired, as evidenced by downregulation of surface molecules OX40, CD40L, and ICOS, as well as cytokines IL-4, IL-10, and IL-21 via multiplex reverse transcription-PCR (RT-PCR) (74). Consequently, B cells in HIV⁺ spleens experienced halted differentiation at the pre-GC and GC stages, at the expense of memory B cell formation (74). The HIV⁺ splenic cell transcriptome was biased toward Th1 functions, possibly driven by defective STAT-3 expression (74). This dysfunction was further enhanced by HIV-induced expansion of Foxp3⁺ T regulatory cells (74).

GALT.

Gut-associated lymphoid tissue (GALT) is a site of severe CD4⁺ T cell depletion during primary HIV infection and an ongoing site of viral replication. Microarray studies of jejunal biopsy specimens have identified an upregulation of a multitude of genes in response to HIV infection, including those coding for inflammatory cytokines, stress responses, lymphocyte activators, and mediators of the cellular inflammatory response (33, 75, 76). Subjects with higher viral load had gene expression profiles reflecting chronic lymphocyte activation and inflammation in GALT (76). Genes involved with chemotaxis, cell adhesion, and mediation of T cell migration are also upregulated during HIV infection, likely reflecting enhanced lymphocyte trafficking to GALT (76). Among downregulated genes were those involved in cell cycle regulation, epithelial barrier, lipid metabolism, nutrient absorption, and amino acid metabolism (33, 76). Several miRNAs (hsa-miRNA-32-5p, hsa-miRNA-195-5p, hsa-miRNA-20b-5p, and hsa-miRNA-590-5p) may have roles in regulating the colonic epithelial barrier and CD4⁺ T cell activation (77). The findings reflect the structural and functional changes in the gastrointestinal tract resulting from HIV infection, which manifest as symptomatic intestinal dysfunction, nutrient malabsorption and deficiency, and chronic inflammation from epithelial barrier degeneration and microbial translocation. Increased expression of cellular trafficking and chemoattractant genes and dysregulation of cell cycle mediators after ART commencement suggest that recruitment rather than local proliferation is responsible for the eventual restoration of GALT cell populations (28). Subsequent interruption of ART results in a GALT gene expression profile most consistent with untreated chronic infection (78).

Sequelae of HIV Infection

HIV is associated with a broad array of comorbidities in the longer term, increasingly visible as life expectancies have risen in the ART era, yet causal relationships are hard to define. Direct infectious functions are compounded by immunosuppressive functions, systemic inflammatory effects, and the effects of HIV-specific treatments, among others.

From a gene expression perspective, neurocognitive disorders are the most widely studied, offering several key lessons. First, neurological tissues of HIV⁺ subjects demonstrate distinct gene expression profiles in comparison to those of their uninfected counterparts, even in the absence of clinically evident neurocognitive impairment (79). Second, although consistent ART may help to normalize gene expression, evidence of subclinical immune activation and neuronal injury persists during active treatment (79–81). Third, subjects with HIV-associated neurocognitive disorders (HAND) have gene expression profiles that are distinct from those of HIV⁺ individuals without HAND (81). Fourth, elements of gene expression dysregulation appear to correlate with the severity of HIV-related neurological disease (79), the presence or absence of HIV encephalitis (82), and HIV-related neurological complications at death despite ART (81, 83). Fifth, dysregulation of miRNAs during HIV infection may contribute to neuropathogenesis through the modulation of genes involved in immune response and inflammation, nucleotide metabolism, cell cycle, and cell death (84). And finally, HIV⁺ subjects with neurocognitive impairment exhibit transcriptomic changes in myeloid cells in cerebrospinal fluid (CSF) (85), circulating monocytes, PBMC, and plasma miRNA (86–88).

There is a body of work around the combined hepatic effects of HIV/hepatitis C virus (excluded in this review); however, other chronic health changes in the context of HIV are less studied. Adipose tissue from untreated HIV⁺ individuals exhibits a gene expression profile distinct from those of both treated individuals and uninfected controls, indicating that HIV likely plays a role in lipodystrophy independently of the known side effects of ART (89). Similarly, muscle biopsy specimens of HIV⁺ men reveal a gene expression signature consistent with premature muscle aging and sarcopenia (90). Circulating platelets of treated HIV⁺ individuals have heightened levels of secretory, inflammatory, and immune cell trafficking activities, which may contribute to increased cardiovascular risk (91). Autopsied retinal biopsy specimens of HIV⁺ subjects demonstrate unique disruption of phototransduction processes within visual perception pathways, likely contributing to the retinopathy with visual loss commonly seen during chronic HIV infection (92). And finally, there are several studies (three of Kaposi sarcoma [93–95] and one of non-Hodgkin’s lymphoma [96]) examining the gene expression profiles of AIDS-defining malignancies, which is important given the advances of personalized medicine in the field of oncology. Transcriptomic explanations of why HIV is associated with myriad other chronic health complaints remain a gap in the literature.

DISCUSSION AND FUTURE DIRECTIONS

HIV infection influences gene expression in a dynamic manner, altering expression patterns according to stage of infection, viremia, and cell types: targets of infection, bystander immunological cells, and nonimmunological secondary tissues. Acute HIV infection is the least understood stage but seems to exhibit the greatest breadth and depth of dysregulated gene expression. In the chronic phase of infection, most immunological cells studied (with CD8⁺ T cells and NK cells the exceptions) experience a hyperinflammatory state, with heightened expression of interferon pathways and chemokine signaling in particular. Different immune cell types display evidence of specific functional impairments in the context of HIV infection. For example, CD4⁺ T cells and B cells experience hyperactivation and increased entry into the cell cycle, with a subsequent increase in programmed cell death (30, 31, 33). Monocytes, in comparison, exhibit gene expression changes designed to maintain numbers but are functionally depressed and unable to recruit effector cells to manage infectious stimuli (46, 48). In addition to specific cell types, levels of viremia affect the transcriptome of PBMC broadly (20, 21, 24). Impacts in some secondary tissues appear to result from generalized chronic inflammation (66, 69, 70), while others yet are very tissue specific (71).

From a procedural perspective, RNA-seq technology is likely to remain the cornerstone of transcriptomics. RNA-seq has several documented advantages over microarray, discussed in detail elsewhere (97, 98). In addition to its sensitivity and range advantages over microarray, RNA-seq allows reduced technical variation and improved concordance between platforms (99). Given that HIV research is characterized by an interest in numerous viral subtypes, many important subpopulations (for example, different ethnic groups, different progressor types) and a great breadth of affected tissue types (some very difficult to obtain), sample size and power issues are common. This field will benefit from the ability to easily combine and cross-analyze multiple data sets, a feat that has not been simple in the microarray era.

There are several lessons from the past regarding study design and statistical and analytical approaches. Limitations in comparing existing data and directions for future studies are summarized here and then detailed below.

Limitations in comparing existing data are differences in:

• comparison groups (e.g., HIV⁺ versus HIV⁻, early versus chronic infection, viremic versus not viremic, etc.)

• definitions of differentially expressed genes (FC, P value, both, none)

• naming conventions for genes and functional categories/pathways

• descriptions of results; some describe genes versus gene sets versus functional categories, etc.

• extents of results published; some focus on 1 or 2 genes of interest, while others publish everything, creating difficulties in evaluation

Directions for future mechanistic studies include:

• Recruiting subjects as early as possible from HIV transmission and following them longitudinally

• Including HIV-negative subjects as controls; where gene expression profiling is performed for subjects with HIV coinfection, include HIV monoinfection to account for the contribution of HIV

• Describing the clinical stage and treatment status of HIV⁺ study participants

• Ensuring that comparison groups include individuals alike for key variables

• Clearly identifying included participants when gene expression profiling is performed on a subset

• In mixed cell samples, providing estimates on proportions of constituent cell types

• Using gold standard platforms for measuring gene expression

• Outlining the criteria used to define differentially expressed genes and pathways

• Documenting adjustment for confounding variables and for multiple comparisons/FDR

• Using standardized naming conventions for genes, polymorphisms, and pathways

• Publishing a full list of differentially expressed genes, inclusive of significance and direction of dysregulation, and providing a functional analysis to look at the effects of the dysregulation

First, comparison groups should be clearly stated and alike for key variables, and their covariates should be well described. Without these details, it is difficult to understand the contribution of an individual research paper to the long-term and varied illness that is HIV-1 infection. Gene expression correlations with viral load can be very useful; however, given what we now know about stage-specific changes in gene expression, it is prudent to avoid patient classifications based on viremia alone. Grouping otherwise dissimilar groups (e.g., acute- and late-stage patients or long-term nonprogressor [LTNP] and ART-treated patients) makes little sense. When it comes to selecting controls, it is important to match or adjust for factors known to influence gene expression, for example, intrinsic factors such as age, sex, and ethnicity, as well as the technician performing RNA extractions, the timing of extraction (for extended study periods), and the batch or platform where relevant (99–102). One interesting finding, novel to the HIV field but not others, is the substantial seasonal variation in physiological gene expression (103); hence, season of recruitment matching of cases and controls should be done where possible.

Opportunities to enhance the knowledge base have been missed in existing studies through the exclusion or nonpublishing of simple comparisons. Where possible, researchers looking at the transcriptome of the cells of HIV⁺ individuals should aim to include a subset of comparable HIV⁻ controls and report HIV-dependent gene expression differences as a baseline, even if this is not the major hypothesis. Similarly, in studies of coinfection with HIV, HIV monoinfection is a common control group; however, inclusion of HIV⁻ controls allows a fuller understanding of the contributions of HIV to the clinical picture. Regrettably, these comparator groups often exist but the analyses are not published.

Second, there must be increased consistency in the definition of differential expression. The definition of a differentially expressed gene usually encompasses significance criteria (typically a P value generated via any test for comparison of means or permutation-based methods built into software packages) and often threshold criteria (typically a fold change statistic, between >1.5 and >2 in the articles reviewed here). Of particular importance in significance calculations is the false discovery rate (FDR) to account for multiple testing. Among the 62 articles reviewed, only 40 explicitly mention their approach to FDR adjustment (one further study intentionally did not adjust and gave justification); Benjamini-Hochberg and Bonferroni correction methods remain the most popular.

Third, once differentially expressed genes are identified, the approach to presenting results has varied in the studies. Different naming conventions, different result descriptions (some choose to describe genes, others describe gene sets or functional categories), and different extents of results (some focus their investigation on a small number of genes or pathways of interest, whereas others provide complete lists) all limit comparability. A valuable standard would be to provide, as a minimum, a statement regarding the extent of gene expression dysregulation (the number of genes up- and downregulated between comparator groups) and a list of the most differentially expressed genes with fold changes and P values (with a full list in an appendix if too cumbersome for the main text). Some studies go on to discuss the biological relevance of their differentially expressed genes, typically through functional annotations, pathway analyses, and/or gene set enrichment algorithm methods, among others, which should be encouraged. In future studies, a uniformity of approach may enhance comparability and ease of interpretation.

Despite many years of research, we still do not have a clear understanding of the host’s acute and ongoing immunological response to HIV. Many HIV-related gene expression studies have used cultured cell lines and in vitro infection methods, often reflecting infection durations of mere hours, which in no way reflects the interactions and complexities of the evolving human host response to HIV infection over the course of months and years. In general, studies have relatively few subjects and the time from onset of infection varies, such that current knowledge represents mere pieces of the metaphorical puzzle.

Academically, there are several gaps in our knowledge that would benefit from further research attention. Pre-ART samples, and particularly those from the acute phase, remain scarce, and would offer great insight into the early host response. Similarly, transcriptomic analyses of longitudinal samples from the same individual throughout the course of HIV infection are almost nonexistent, barring limited analyses of paired pre- and post-ART samples (59), and would be highly valuable. There is a paucity of transcriptomic information regarding the behavior of noncirculating CD4⁺ T cells and T cell subsets during HIV infection, as well as that of dendritic cells, NK cells, and nonalveolar macrophages.

HIV host transcriptomics still has much to contribute. Transcriptome-based biomarker signatures are yet to be reliably useful in the HIV arena, and opportunity exists to mirror breakthroughs in other fields, for example, identification of biomarkers for disease progression and potential response to therapy. Interrogation of the transcriptome may yield improved diagnostics and improved incidence assays and may allow us to better understand disease mechanisms and future drug and vaccine targets. This paper serves as a timely update of our progress in this rapidly advancing field but also emphasizes the need for larger, more tightly controlled studies with respect to duration of infection to provide a better understanding of this important disease.

In summary, this review demonstrates the extraordinary complexity of the gene expression response to HIV infection. Inconsistencies in study design methodology and data reporting leave little coherence in the findings, and the most important questions remain unanswered. With attention to the issues outlined above, transcriptomic studies can fulfil their promise and advance knowledge of HIV toward elimination of the virus.

Biographies

Melinda Judge, B.Sc. (Hons), is a Ph.D. candidate based in Perth, Western Australia, who studied at the University of Western Australia. Her research involves facets of infectious disease in sub-Saharan African countries, including host gene expression during early HIV-1 infection, and statistical modelling of childhood malnutrition and infection. Melinda has been working in this area since 2013.

Erica Parker, M.B.B.S., B.Sc. (Hons), is a medical doctor completing specialty training in Public Health Medicine. She trained at the University of Western Australia and is a Ph.D. scholar with the same. She currently works at Metropolitan Communicable Disease Control (MCDC) in Perth. Erica has interests in infectious diseases and health in resource-limited settings and has worked on HIV research in southern Africa since 2013.

Denise Naniche, M.P.H., Ph.D., is a Research Professor at the Barcelona Institute for Global Health (ISGlobal). She trained at the University of California, Los Angeles, the Université Claude Bernard Lyon 1, and the San Diego State University. She has more than 15 years of experience conducting basic and translational research in public health, with a particular focus on HIV/AIDS diagnosis, treatment, and control in the sub-Saharan African setting. She also coordinates and teaches modules in HIV/AIDS and vaccinology in the Masters of Tropical Medicine and Masters of Global Health courses at the University of Barcelona.

Peter Le Souëf, M.B.B.S. (WA), M.R.C.P. (UK), F.R.A.C.P., F.E.R.S., M.D., is a Professor of Paediatrics at the University of Western Australia and a Respiratory Physician at Perth Children’s Hospital. He is an awarded international research leader with interests in infectious diseases and global health. Peter was head of the UWA School of Paediatrics for 20 years and has supervised more than 30 higher research degree students. He has established major research partnerships across six continents, with a focus on the immunogenetics of acute infections, including HIV, in southern Africa since 2012. More recently, he has worked to establish a collaborative group of international research experts, looking at future child health in the context of environmental decline and increasing world population.