Interferon-α Subtypes in an Model of Acute HIV-1 Infection: Expression, Potency and Effector Mechanisms

Summary

The therapeutic potential of recombinant IFNα against HIV-1 infection has been explored for 25 years, but its effectiveness was inconsistent. However, these clinical trials administered IFNα2, which is only one member of a 12-protein family of IFNα subtypes. More recently, IFNα was found to activate ‘restriction factors’–proteins that can directly inhibit HIV-1. To date, it remains unknown which IFNα subtypes are produced by professional IFNα producing cells known as plasmacytoid dendritic cells and which IFNα subtypes are more effective in inhibiting HIV-1 infection in the gastrointestinal tract, the primary site of early HIV-1 replication. Here, we show that weaker IFNα subtypes were more highly expressed following HIV-1 infection. Using an infection platform that captures important characteristics of early HIV-1 infection in the gut, several IFNα subtypes were found to be more effective at inhibiting HIV-1 than IFNα2. In particular, IFNα8 and IFNα14 more potently reduced the infectivity of HIV-1 virions, an activity that can be attributed to the APOBEC3 proteins. Our findings strongly support the evaluation of potent IFNα subtypes in currently evolving HIV-1 curative strategies.

Introduction

The type I interferons (IFNs) are critical players in the innate immune response against viral infections. Shortly after infection, these cytokines are rapidly induced, stimulating an antiviral state through the induction of hundreds of interferon-stimulated genes (ISGs) [1]. This family of cytokines include IFNα, the first cytokine produced through recombinant DNA technology and tested in clinical trials against many infectious diseases [2]. Notably, IFNα is a collective term for 12 unique IFNα proteins or subtypes expressed by 13 IFNA genes that are tandemly arrayed on human chromosome 9. However, most clinical trials only utilize recombinant IFNα2, the subtype that is currently licensed for the treatment of hepatitis B virus (HBV) and HCV infection. IFNα2 was also evaluated for reducing HIV-1 plasma viral loads during chronic infection. However, the variable levels of efficacy observed [3–6] and the advent of potent and safer antiretroviral drugs reduced enthusiasm for the use of IFNα in the clinical management HIV-1 infection. Two major developments in recent years renewed interest in IFNα as a therapeutic for HIV-1 infection: (1) the discovery of antiretroviral restriction factors, most of which are induced by IFNα [7]; and (2) the improved prospects in achieving functional HIV-1 cure, which may be advanced through IFNα-based therapies [8,9]. However, this renewed interest also raised unanswered questions on the basic biology of IFNα, including the biological consequences of having an expanded IFNA gene family [10,11]. In fact, the relative expression, antiviral potency and restriction factor mechanisms employed by the various IFNα subtypes against HIV-1 infection remains unclear.

One potential advantage for the expansion of the IFNA gene family could be the diversification of regulatory elements, which would allow the infected host to differentially express IFNA genes in response to diverse stimuli. Plasmacytoid dendritic cells (pDCs) are the primary producers of IFNα in vivo [12], and exposure of pDCs to HIV-1 or HIV-1 infected cells resulted in a dramatic rise in IFNα production [13,14]. Measurements of total IFNα proteins rely on antibodies that may have different binding affinities to the IFNα subtypes. Furthermore, antibodies that can distinguish the various IFNα subtypes are not yet available. IFNα expression is primarily regulated at the mRNA level [15]. Innate sensing of viruses, for example through Toll-like receptors (TLRs), results in a signaling cascade that leads to the activation and recruitment of transcription factors to the IFNA promoter(s) [16]. Thus, quantitative real-time PCR (qPCR) is a standard procedure used by many laboratories to measure IFNA gene expression, with increasing recognition on the importance of obtaining IFNA subtype distribution for understanding retroviral pathogenesis [17]. However, quantifying the expression of the different IFNA subtype genes is complicated by their high sequence homology (78 to 99%). Nevertheless, IFNA subtype expression profiles of pDCs were evaluated using quantitative real-time PCR assays developed for each IFNA gene [15,18–20]. Humanized mice exposed to TLR7 agonists showed prominent expression of IFNA2 and IFNA14 in pDCs [18] but other studies showed equal expression of all IFNA subtypes following TLR ligand stimulation [15,19]. These discrepancies suggested that measuring IFNA distribution by qPCR may be difficult to reproduce across laboratories. Moreover, performing 12 qPCR reactions for each IFNA subtype would not be ideal for limited biological samples. The lack of a robust method to quantify IFNA distribution is therefore a significant hurdle in understanding the role of IFNA subtypes in human health and disease.

Functional diversification may be another evolutionary advantage for an expanded IFNα gene family. Although all IFNα subtypes signal through the same type I interferon receptor (IFNAR), the IFNα subtypes exhibited different binding affinities for the IFNAR-1 and IFNAR-2 subunits [21,22]. This might result in different signaling pathways induced by IFNα subtypes [23] and in distinct expression patterns of ISGs in vitro [24]. In vivo, mouse IFNα subtypes exhibited different potencies against herpes simplex virus 1, murine cytomegalovirus, vesicular stomatitis virus (VSV), influenza virus and Friend retrovirus [11,25]. Altogether, the data indicate that the IFNα subtypes are not functionally redundant, raising the immediate question of which IFNα subtypes are most potent against HIV-1. An early study revealed that IFNα2 may be the most potent, but only 6 IFNα subtypes were evaluated against an X4-tropic, lab-adapted HIV-1 strain in the MT-2 T cell line [26], thereby raising issues regarding physiological relevance.

IFNα is induced very early during HIV-1 infection [27], and blocking IFNAR signaling in the SIV/rhesus macaque model resulted in higher viral loads and pathogenesis [28]. The impact of the early IFNα response against HIV-1 most likely manifests in the gut-associated lymphoid tissue (GALT), as it is the major site of early HIV-1 amplification and spread that leads to a massive depletion of CD4+ T cells [29,30]. Prior success in infecting gut lamina propria mononuclear cells (LPMCs) with HIV-1 [31] led to the development of the Lamina Propria Aggregate Culture (LPAC) model [32,33]. The LPAC model allows for the robust infection of primary gut CD4+ T cells with CCR5-tropic HIV-1 strains, subsequently leading to CD4+ T cell depletion. Importantly, this model allowed for HIV-1 infection studies without the confounding effects of non-physiologic T cell activation, as HIV-1 can efficiently infect gut CD4+ T cells without exogenous mitogens [29–31]. Thus, the LPAC model is an ideal ex vivo platform to evaluate the relative potency of the various IFNα subtypes against HIV-1.

Identifying the key effectors behind the anti-HIV-1 activity of IFNα could pave the way for the design of novel IFNα-based therapeutics. The APOBEC3 proteins (A3G, A3F, A3D and A3H), Tetherin/BST-2 and Mx2 were considered as bona fide HIV-1 restriction factors [7,34–37]. These factors were proposed as effectors of the IFNα treatment effect based on correlative studies using IFNα clinical trial data [38,39] as well as cell culture data [35,40–42]. However, their regulation by diverse IFNα subtypes in mucosal CD4+ T cells has not yet been explored. APOBEC3 and Tetherin are counteracted by the HIV-1 Vif and Vpu, respectively [7], but it is important to note that these interactions are saturable. Induction of APOBEC3 and Tetherin expression may undermine the antagonism due to Vif and Vpu by offsetting the balance of these respective interactions. Tetherin and Mx2 inhibit HIV-1 in the infected cell, leading to a reduction in virus release [34–37]. In contrast, the APOBEC3 proteins are packaged into budding HIV-1 particles and inhibit replication in the next target cell by impeding reverse transcription and hypermutating reverse transcripts [43,44]. Thus, a strong case for APOBEC3 activity could be made if reduced HIV-1 virion infectivity and increased G→A hypermutation were both detected. We previously showed that treatment of Friend retrovirus-infected wild-type mice with IFNα reduced viral loads, but not in Apobec3 knock-out (KO) mice [45]. Given the longstanding evolutionary conflict between mammalian hosts and retroviruses [46], we hypothesized that the human APOBEC3 proteins may also act as effectors of IFNα treatment against HIV-1 in mucosal CD4+ T cells.

Here, we modeled the role of the IFNα subtypes during acute HIV-1 infection. Using a novel next-generation sequencing-based method, we quantified the relative expression of the IFNα subtypes following HIV-1 exposure in pDCs, and determined the relative antiviral potency of each IFNα subtype in the LPAC model. Moreover, we determined the induction profiles of known HIV-1 restriction factors following treatment with individual IFNα subtypes, and provide evidence that the APOBEC3 proteins may serve as key effectors for the antiviral activity of IFNα against HIV-1.

Results

IFNA subtype expression in HIV-1-exposed pDCs is linked to chromosomal position

Plasmacytoid DCs (pDCs) are the primary sources of IFNα in vivo, migrating to the GALT from the periphery during acute SIV infection [47] and accumulating in mucosal tissues during chronic HIV-1 infection stages [48,49]. To date, the IFNα subtypes produced by pDCs following HIV-1 sensing remain unknown. To determine the expression levels of each IFNα subtype, we designed 2 complementary assays using primers designed in the most conserved regions of the 13 IFNA genes (S1 Fig). Using these primers, total IFNA expression relative to the housekeeping gene GAPDH could be measured by qPCR, whereas IFNA subtype distribution could be quantified by next-generation sequencing. We used negative selection to enrich pDCs from PBMCs from 4 healthy donors and exposed the cells to HIV-1 virions (R5-tropic BaL strain) for 6 hrs (Fig 1A). A 6 hr timepoint was chosen to ensure the viability of the pDCs, which significantly decline by 24 h post-culture [50], while capturing the initial burst of IFNA expression following viral sensing. Total IFNA expression was induced 485-fold in pDCs following HIV-1 exposure, but not in PBMCs lacking pDCs, confirming that pDCs are the main producers of IFNα (Fig 1B).

We next quantified the relative abundance of each IFNα subtype in pDCs ± HIV-1. Primers in the conserved regions were modified with Illumina-sequencing adaptors, and the IFNA subtype designation for each sequence was determined based on the polymorphic regions in the amplicon. IFNA1 and IFNA13 encode identical proteins and had identical DNA sequences in the region amplified, so these genes were counted together as IFNA1/13. On average, 9,543 IFNA sequence reads were analyzed per donor per condition. The IFNA genes were aligned according to their relative genomic positions and their proportional expression values are shown (Fig 1C).

The proportional expression of different IFNA subtypes by pDCs from different donors was very consistent both in naïve cultures (Fig 1D) and following HIV-1 exposure (Fig 1E). Interestingly, there was a strong bias towards expression of IFNA genes at the centromeric half of the IFNA complex following HIV-1 exposure (Fig 1E). Five out of six IFNA genes in this genomic cluster accounted for >70% of the IFNA subtypes expressed by pDCs following HIV-1 exposure (Fig 1F). The exception was IFNA6, which decreased as a percentage of the total IFNA. The augmented IFNA subtype expression levels were independent of genomic orientation, as IFNA2 and IFNA8 were both highly expressed yet had opposite genomic orientations (Fig 1C). We then determined the absolute copy numbers of each IFNA subtype by multiplying the percentage values (Fig 1D and 1E) with the total copy numbers (Fig 1B). The absolute copy numbers of all IFNA subtypes increased in pDCs following HIV-1 exposure, though to varying degrees (S2 Fig). IFNA14, IFNA2 and IFNA10 were induced over 1000-fold following HIV-1 exposure of pDCs, whereas IFNA6 was induced by ~100-fold. Overall, the results revealed a pattern of IFNA gene induction after HIV-1 exposure that appeared to be linked to chromosomal position.

IFNα subtypes differentially inhibit HIV-1 replication in the LPAC model

Since the GALT is the major site of early HIV-1 amplification and spread, we utilized LPAC as a physiologically relevant model to determine the relative anti-HIV potency of each IFNα subtype. In particular, we were interested in whether IFNα2, the subtype approved for clinical use, was the optimal IFNα subtype for inhibiting HIV-1. Fig 2A outlines the experimental infection protocol.

Analyzing the HIV-1 potency of all 12 IFNα subtypes at multiple doses was not feasible in the LPAC model because of the limited number of LPMCs available per donor. Thus, initial dose-response tests were performed with IFNα14, which potently inhibited HIV-1 in a pilot experiment. Following infection with HIV-1_BaL, LPMCs were rinsed with culture media and resuspended to various IFNα14 concentrations. Infection levels were evaluated at 4 days post-infection (dpi) to capture not only the impact of restriction factors that inhibit HIV-1 virus production, but also those that inhibit virion infectivity, which would decrease infection after one round of replication (S3 Fig). The percentage of infected CD4+ T cells was measured by detecting intracellular HIV-1 p24 capsid expression by flow cytometry, as we previously described [32,33]. To account for HIV-1 Nef and Vpu-mediated CD4 downregulation [51], we gated on CD3+CD8- cells. A screen of LPMCs from 7 donors revealed that IFNα14 restricted productive HIV-1 infection, and that the inhibition was saturable at higher concentrations (Fig 2B). The majority of the LPMC donors had similar sensitivity to IFNα14-treatment, with the exception of one donor who responded to lower concentrations. An IFNα concentration of 100 pg/ml was in the linear range of the dose response curve (~50% inhibition), and was chosen for the subsequent evaluation of all IFNα subtypes in 4 LPMC donors. This concentration was also within the range of IFNα levels in plasma following HIV-1 infection in vivo [52]. Majority of the cells in the LPMC donors used were CD3+ T cells (88% ± 3%). On average, 65% of the LP T cells were CD4+. Myeloid DCs and gamma-delta T cells account for <1% of the total LPMC subpopulations, respectively.

Recombinant IFNα subtypes were added to LPMCs (100 pg/ml) after spinoculation (Fig 2A). At 4 dpi, HIV-1 infected cells were quantified by detecting intracellular p24 by flow cytometry as in Fig 2B. There were clear differences in the potency of the IFNα subtypes in inhibiting productive HIV-1 infection (Fig 2C). IFNα8, IFNα14 and IFNα6 showed the highest levels of inhibition, whereas IFNα1 and IFNα2 had no significant effect. The supernatants were also tested for infectious HIV-1 titers using the TZM.bl assay (S3 Fig). Again, the same 3 IFNα subtypes were most potent, whereas IFNα1 remained the least potent (Fig 2D). The antiviral potency of the different IFNα subtypes as measured by p24 flow cytometry and the TZM.bl assay significantly correlated with each other (S4A Fig). Although IFNα2 had no significant effect on cellular HIV-1 infection (Fig 2C), it moderately reduced infectious titers (Fig 2D). Overall, the LPAC data revealed differences in the potencies of IFNα subtypes in inhibiting HIV-1 infection. IFNα2, the current subtype approved for clinical use, was one of the least potent subtypes.

HIV-1-exposed pDCs express high levels of IFNα subtypes with low antiviral activity

To investigate whether the IFNα response of pDCs following HIV-1 exposure was biased towards the expression of the most potent antiviral IFNα subtypes, we next determined the relationship between IFNα subtype expression levels and relative potency. Absolute IFNA subtype copy numbers were calculated by multiplying the total IFNA copies (Fig 1B) by the percentage of total IFNA for each subtype (Fig 1E). This provided an estimated copy number of each IFNA subtype per 10⁶ copies of GAPDH. Using these values, a significant inverse correlation was observed between IFNA subtype expression and potency (Fig 3A and S4B Fig). This correlation can be exemplified as follows. IFNα1 was highly expressed but ineffective at inhibiting HIV-1 replication. IFNα6, one of the most potent subtypes, was among the least abundant following HIV-1 exposure. IFNα2 showed a very high fold-increase following HIV-1 exposure relative to baseline but had weak antiviral efficacy. IFNα5 is expressed at higher relative abundance (Fig 1F) but was also weakly antiviral. These results revealed that the predominant IFNA subtypes produced by pDCs following HIV-1 exposure had low antiviral potency. Two notable exceptions were IFNα8 and IFNα14, which exhibited strong anti-HIV-1 activity and also had high expression in pDCs following HIV-1 exposure (Fig 3A). Exclusion of the IFNα8 and IFNα14 datapoints further strengthen the inverse correlation (R² = 0.62, p = 0.007).

Data from the Schreiber group [22] revealed that different IFNα subtypes exhibited variable binding affinities to IFNAR as estimated by surface plasmon resonance against each subunit, IFNAR-1 and IFNAR-2. We therefore determined if IFNα subtype anti-HIV-1 potency (Fig 2C and 2D) correlated with published binding affinity data to IFNAR [22]. There was a significant positive correlation between antiviral potency and binding affinity (K_A) to IFNAR-2 (Fig 3B and S4C Fig), but not the IFNAR-1 subunit (Fig 3C and S4D Fig). These analyses suggested that following HIV-1 exposure, pDCs produced IFNα subtypes with relatively low antiviral activity and lower binding affinity to IFNAR-2. In particular, IFNα1 was expressed at high levels by pDCs exposed to HIV-1 virions but had the weakest IFNAR-2 binding affinity and the lowest anti-HIV-1 potency in the LPAC model.

Differential induction of antiretroviral ISGs by IFNα subtypes

The correlation between antiviral potency and IFNAR binding affinity suggested that the more potent IFNα subtypes might trigger higher ISG induction. To test this hypothesis, we quantified the mRNA expression levels of the IFNα-inducible HIV-1 restriction factors Mx2, Tetherin and APOBEC3 in LP CD4+ T cells after stimulation with representative IFNα subtypes. We focused on CD4+ T cells, the major cellular targets of HIV-1 replication in the GALT, but not intestinal macrophages, which are non-permissive to HIV-1 infection [53]. We selected IFNα8 and IFNα14 as potent IFNα subtypes due to their high affinity, highest antiviral potency in the LPAC model and high expression level in pDCs. IFNα1 and IFNα2 were selected as weak IFNα subtypes due to their relatively low affinity, weaker antiviral activity in the LPAC model (with IFNα2 being more potent than IFNα1), but high expression level in HIV-1-exposed pDCs (IFNα1 and IFNα2). IFNα2 was also chosen because of its clinical relevance. LPMCs were infected with HIV-1_BaL and 100 pg/ml IFNα was administered. After 24 hr, CD4+ T cells were negatively selected and ISG mRNA expression was evaluated by qPCR (Fig 4A).

The magnitude of ISG induction was donor-dependent so the data for each donor are presented. (Fig 4B to 4E). The ISG expression that best correlated with the relative antiviral activities of the IFNα subtypes was Mx2 (Fig 4B). IFNα8 (3 of 3 donors) and IFNα14 (2 of 3 donors) more significantly induced Mx2 compared to IFNα1 and IFNα2. IFNα2, which showed moderate antiviral activity (Fig 2D), more significantly induced Mx2 compared to IFNα1 in 3 of 3 donors. Tetherin induction exhibited trends similar to Mx2, but the differences were not as consistent between donors (Fig 4C and 4D). Overall, the more antiviral IFNα subtypes induced Mx2 and Tetherin to higher levels. In contrast, A3G (Fig 4E) was not significantly induced by any of the IFNα subtypes. A3F and A3D expression were induced in a few cases with IFNα treatment (S6 Fig), but the induction levels did not correlate with the relative anti-HIV-1 potency of the IFNα subtypes.

Potent IFNα subtypes inhibit HIV-1 virion infectivity

We previously demonstrated that mouse Apobec3 was the primary effector of IFNα treatment against Friend retrovirus infection despite not being transcriptionally induced [45]. We therefore investigated the potential contribution of human APOBEC3 proteins to the IFNα-treatment effect. The APOBEC3 proteins A3G, A3F, A3D and A3H do not inhibit HIV-1 in the producer cell. Instead, these proteins get packaged into HIV-1 virions and inhibit replication in the next target cell. Thus, non-infectious virion release is a distinguishing feature of APOBEC3-mediated retrovirus restriction [54,55]. By contrast, most restriction factors such as Mx2 and tetherin inhibit virus particle production in the infected cell [7]. Virion infectivity is typically measured by determining the ratio of infectious titer as measured by the TZM.bl assay and the total viral particles released in the supernatant as measured by HIV-1 p24 ELISA (S3 Fig).

LPMCs from 6 donors were infected with HIV-1_BaL and were treated with IFNα1, IFNα2, IFNα8 and IFNα14. At 4 dpi, all 4 IFNα subtypes inhibited virus particle release to the same extent (Fig 5A). By contrast, the infectious titers were reduced significantly more by IFNα8 and IFNα14 compared to IFNα1 and IFNα2 (Fig 5B). Thus, inhibition of virion infectivity correlated with the antiviral efficacy of the IFNα subtypes (Fig 5C). In particular, IFNα8 and IFNα14 were the most potent at inhibiting virion infectivity whereas IFNα1 had no significant effect. In order to confirm that the findings were not specific to HIV-1_BaL, LPMCs were infected with transmitted/founder (T/F) HIV-1 strains, which are infectious molecular clones reconstructed from acute HIV-1 infection samples [56–58]. In 6 LPMC donors, the antiretroviral activity of IFNα1 and IFNα8 against the T/F HIV-1 strains CH470, CH40, and CH58 were compared. IFNα1 and IFNα8 inhibited virus particle release to similar extents for CH40 and CH58 (Fig 5D), whereas CH470 particle release was slightly more inhibited by IFNα8. In virion infectivity assays, IFNα8 more potently inhibited the 3 T/F HIV-1 strains (Fig 5E). We also evaluated the impact of IFNα8 in 13 additional T/F HIV-1 strains in 2 LPMC donors. IFNα8 treatment resulted in a highly significant (~4-fold) decrease in virion infectivity (Fig 5F). IFNα14 treatment also significantly inhibited the virion infectivity of these T/F HIV-1 strains (S5 Fig). These data indirectly suggested that the more potent IFNα subtypes augmented APOBEC3-mediated restriction of multiple HIV-1 strains.

IFNα8 and IFNα1 treatment promotes APOBEC3G-mediated HIV-1 hypermutation

The APOBEC3 proteins A3F, A3D and A3H mutated HIV-1 reverse transcripts with a preferred TC context, leading to GA→AA mutations in the retroviral plus strand, whereas A3G preferentially mutated in the CC context, leading to proviral GG→AG mutations [59]. Thus, the magnitude of retroviral mutations in the GA→AA versus GG→AG context could be used to determine the APOBEC3 members responsible for HIV-1 G-to-A hypermutation and to provide additional evidence of APOBEC3 involvement in HIV-1 restriction. To quantify APOBEC3-mediated retroviral mutations, we recently developed a next-generation sequencing approach to quantify mouse retrovirus hypermutation [60]. To extend this method to HIV-1, we designed barcoded Illumina primers encompassing gp41/nef (420–450 bp depending on the strain), a region that may be more susceptible to APOBEC3-mediated deamination due to longer retention in single-stranded form during reverse transcription [61]. We initially tested the method by infecting LPMCs with WT HIV-1 NL4-3 and NL4-3ΔVif, which cannot counteract the effects of APOBEC3. The percentage of GG→AG and GA→AA mutations were computed against the mutations at C or G bases, which are directly modified by deaminases. As expected, there was a significant increase in GG→AG and GA→AA mutations in NL4-3ΔVif compared to WT at 4 dpi (Fig 6A). Thus, A3G and A3F/A3D/A3H actively mutated HIV-1ΔVif in gut CD4+ T cells.

Following the validation of the next-generation sequencing method, we next analyzed proviral HIV-1 sequences for evidence of GG→GA and GA→AA mutations following treatment with IFNα8 or IFNα1. LPMCs were infected with T/F HIV-1 strains CH470, CH40, and CH58. These strains were derived from infectious molecular clones and therefore allow for straightforward mutational analysis. These 3 HIV-1 strains also had reduced virion infectivity following IFNα8 but not IFNα1 treatment (Fig 5E). Untreated and IFNα-treated infected cells were harvested at 4 dpi. Sequences were pooled for each of the HIV-1 CH470, CH40, and CH58 strains, respectively, to allow for a thorough analysis of mutational patterns. A 2×2 contingency analysis was performed to test if IFNα had any effect on A3F/D/H-type (GA→AA) or A3G-type mutations (GG→AG) relative to the total number of C or G mutations. Following IFNα8 treatment, both GG→AG and GA→AA mutations significantly increased in CH470 (Fig 6B). GG→AG mutations also significantly increased in CH40, and to a lesser extent in CH58 (Fig 6B). Surprisingly, IFNα1 treatment also increased GG→AG mutations in CH40, CH58 and CH470 (Fig 6C). Thus, both IFNα8 and IFNα1 treatment increased proviral DNA mutations that were associated with A3G deaminase activity.

Discussion

Acute HIV-1 infection is characterized by extensive virus replication in the GALT, suggesting that the innate immune response could have a considerable impact on early HIV-1 spread in this compartment. In particular, IFNα exhibited potent anti-HIV-1 properties in vitro and was one of the first cytokines induced during acute HIV-1 infection [27]. Blocking type I IFN signaling in the SIV/rhesus macaque model resulted in more severe pathogenesis [28]. T/F HIV-1 strains exhibited higher resistance to type I IFNs than counterpart chronic strains, suggesting that type I IFNs exerted a strong selective pressure during acute HIV-1 infection [57,58]. These studies suggested that the initial IFNα response may serve as a roadblock for HIV-1 replication and spread in the GALT. However, there were 12 IFNα subtypes, and to date, it remained unknown which IFNα subtypes were produced by pDCs, the professional IFNα-producing cells that rapidly migrate and reside in the GALT following HIV-1/SIV infection [47–49]. Moreover, only one subtype, IFNα2, was evaluated in clinical trials to reduce HIV-1 viremia. In fact, the clinical use of IFNα2 was largely driven by its status as the first IFNα subtype cloned for large-scale production [2], and not from a systematic evaluation of antiviral potencies in physiologically-relevant target cells. Thus, the current study was undertaken to investigate the relative expression of the different IFNα subtypes in pDCs and their antiviral potency in the LPAC model.

A major finding from this work was that IFNα8, IFNα6 and IFNα14 were the most effective at inhibiting HIV-1 replication in gut CD4+ T cells. By contrast, the antiviral activity of IFNα2 was weak at best. IFNα8, IFNα6, and IFNα14 exhibited strong binding affinities to IFNAR-2 [22]. This suggests that binding affinity to IFNAR-2, proposed as the first IFNAR subunit that binds IFNα [62], may contribute to the differential potencies of the IFNα subtypes. This notion was corroborated by the higher ISG induction profile for IFNα8 and IFNα14 compared to IFNα2. Sequence analyses of IFNα8, IFNα6 and IFNα14 in human populations revealed that DNA polymorphisms in these subtypes tend to preserve the amino acid sequence (e.g., purifying selection) [63], suggesting that these IFNα subtypes may have essential roles in vivo. Moreover, IFNα8 exhibited strong antiviral activity against other viruses [64]. Interestingly, using a novel method to quantify IFNA subtype distribution, we observed an inverse correlation between IFNα subtype expression in HIV-1-exposed pDCs and anti-HIV-1 potency. IFNα6 fit this trend–it was one of the least expressed IFNα subtypes in HIV-1-exposed pDC cultures. IFNα6 was also weakly expressed by pDCs stimulated with TLR ligands [15]. However, IFNα8 and IFNα14 were both potent and more abundantly produced by pDCs exposed to HIV-1. IFNα8 and IFNα14 were encoded within the centromeric half of the IFNA complex, suggesting that epigenetic mechanisms may regulate their expression. The data suggest that IFNα8 and IFNα14 may constitute the most potent antiviral fraction of the initial IFNα response against HIV-1 infection. However, it should be noted that IFNα8 and IFNα14 only account for ~20% of the total IFNA transcripts produced by pDCs following HIV-1 exposure.

The majority of the IFNα subtypes expressed by pDCs following HIV-1 exposure had relatively weak antiviral activity (IFNA1, 2 and 5 account for >40% of IFNA transcripts). In particular, the most expressed IFNα subtype, IFNα1, had the weakest antiviral activity. IFNα1 also exhibited very weak activity against VSV and HCV, and the lowest binding affinity for IFNAR-2 [22,64–66]. IFNα2 was also highly induced in pDCs post-HIV-1 exposure, consistent with another study showing IFNα2 was upregulated in HIV-1-infected individuals [67]. We speculate that IFNα1 and IFNα2 induction may be a strategy used by HIV-1 to evade a more potent IFNα response. However, the rationale for why humans evolved weakly antiviral IFNα subtypes in the first place remains unknown. One possibility is that weakly antiviral IFNα subtypes may be better at modulating other immunological processes. If true, then these IFNα subtypes could potentially elicit more adverse effects if administered therapeutically. IFNα2 therapy was long known to have undesirable clinical side-effects including fever, fatigue and lymphopenia [2]. Moreover, in an intriguing paradox, high IFNα expression levels during chronic HIV-1 infection correlated with disease progression [52,68]. This led some to propose blocking IFNα signaling in chronic HIV-1-infected individuals to reduce immune activation [69]. However, the IFNα subtypes responsible for the link between IFNα and chronic immune activation remains unknown. The development of the IFNA subtyping method described here should facilitate revisiting this phenomenon. In addition, further studies would be required to evaluate the tolerability profile of IFNα8, IFNα6 and IFNα14 relative to IFNα2 and IFNα1.

One possible strategy to harness the antiviral properties of IFNα for the design of safer HIV-1 therapeutics is to focus on its downstream antiviral effectors. Many ISGs were reported to have inhibitory activity against HIV-1 in vitro [70], but transcriptional induction levels may not predict the most potent antiviral effectors of IFNα [45]. In this study, the more antiviral IFNα subtypes induced Mx2 and Tetherin to a greater extent. Mx2 and Tetherin act on the producer cell, decreasing viral production. Thus, if the IFNα subtypes were acting through these restriction factors to inhibit HIV-1 replication, we would expect higher inhibition of virus production by the more potent IFNα subtypes. Surprisingly, this was not the case: IFNα1 inhibited virus particle production to a similar extent as IFNα8 and IFNα14. Thus, Mx2 or Tetherin may not be mediating the differences in antiviral potencies between the IFNα subtypes. In other words, the differential induction of Mx2 and Tetherin expression by potent versus weak IFNα subtypes may just reflect the magnitude of IFNAR signaling and not necessarily indicate the mobilization of these effector mechanisms.

The IFNα subtypes did not significantly upregulate A3G, A3F and A3D transcription in gut CD4+ T cells, consistent with previous data using IFNα in PBMCs [71,72]. Nonetheless, the relative potencies of the IFNα subtypes were associated with reduced virion infectivity, thus pointing to the APOBEC3 proteins as a significant antiviral effector of IFNα. The notion that the APOBEC3 proteins could act as significant effectors of potent IFNα subtypes makes evolutionary sense based on our studies in mice [45]. However, the mechanism for how IFNα improved APOBEC3 function without transcriptional induction remains to be determined. Surprisingly, both the potent (IFNα8) and weak (IFNα1) subtypes induced retroviral GG→AG hypermutation, suggesting that the deaminase-dependent activity of A3G did not correlate with the relative antiretroviral potencies of the IFNα subtypes. A3G inhibits HIV-1 through a deaminase-independent and deaminase-dependent mechanism. The deaminase-independent mechanism acts upstream by inhibiting the elongation of reverse transcripts, thereby preventing the production of single stranded DNA substrates for deamination [43]. Our results raise the intriguing possibility that IFNα subtypes may differentially activate deaminase-independent and deaminase-dependent activities of the APOBEC3 proteins. Notably, several studies suggested that A3G deaminase activity could be a double-edged sword, as A3G may not only restrict HIV-1 replication but also promote viral evolution to evade antiretroviral drugs and adaptive immunity [73–76]. About 16% of transmitted/founder HIV-1 strains exhibit signatures of G→A hypermutation [56], and APOBEC3-linked mutations in rapidly evolving sites may be linked to CTL escape [77]. Thus, the induction of weakly antiviral subtypes such as IFNα1 by pDCs during acute HIV-1 infection may have important consequences for early HIV-1 evolution.

In conclusion, the differential expression, potency and restriction factor induction by the human IFNα subtypes suggest that these evolutionarily related cytokines play non-redundant roles during HIV-1 infection. These findings are particularly timely with respect to ongoing clinical trials that aim to leverage IFNα2 therapy as a potential HIV-1 curative strategy (clinicaltrials.gov identifiers NCT00594880, NCT01295515, NCT01285050 and NCT01935089). Our results suggest that evaluating IFNα subtypes that more potently augmented APOBEC3-mediated deaminase-independent restriction may yield better clinical outcomes on the road to a functional HIV-1 cure.

Materials and Methods

Ethics statement

Blood collection from self-identified HIV-negative donors was approved by the Colorado Multiple Institutional Review Board (COMIRB) at the University of Colorado Anschutz Medical Campus. The use of discarded, macroscopically normal human jejunum tissue samples was granted exempt status by COMIRB and patients signed a pre-operative consent form allowing its unrestricted use for research purposes. Protected patient information was de-identified to laboratory personnel.

Viral stocks

HIV-1BaL stocks (AIDS Research Reagent Program/ARRP Catalogue #4984) were prepared by passage in MOLT4-CCR5 (ARRP #510) cells for 9 days. Virus containing supernatants were ultracentrifuged at 76,800g. T/F HIV-1 infectious molecular clones CH470, CH40, and CH58, as well as AD17, CH106, CH607, REJO, RHPA, THRO, STCOr1, STCOr2, WARO, MCST, RHGA, TRJO and WITO were generously provided by Beatrice Hahn [57,58]. NL4-3 and NL4-3ΔVif were obtained from ARRP. CH470, CH40 and CH58 plasmids were re-transformed and amplified in Stbl3 cells (Invitrogen) and purified using Qiagen maxi kit. T/F maxi-preps were sequence-verified using 13 HIV-specific primers to cover the entire genome. 40 μg of T/F plasmids were used to transfect 293T cells in a T175 flask. Four flasks were transfected by CaCl2 transfection method for each virus [78]. Virus-containing supernatants were collected at 48 hrs, concentrated by ultracentrifugation at 76,800g over a 20% sucrose cushion. Virus stocks were titered using an HIV-1 Gag p24 ELISA kit (Perkin Elmer).

Isolation and exposure of pDCs to HIV-1

pDCs were isolated from peripheral blood of 4 healthy donors who self-identified as HIV-1-uninfected. All subjects voluntarily gave written, informed consent. This study was approved by the Colorado Multiple Institutional Review Board (COMIRB) at the University of Colorado Anschutz Medical Campus. pDCs were negatively selected using the EasySep plasmacytoid cell enrichment kit according to the manufacturer’s instructions. Purity was determined by flow cytometry. On average, the pDC-enriched fraction was 76% (range: 53–92%) BDCA-2+. The other cell subpopulations were significantly depleted, with 2% CD3+ (from 65%), 0.2% CD14+ (from 6%), 0.7% CD19+ (from 2%) and 1.1% CD56+ (from 11%). Zombie Aqua Viability Dye (Biolegend) exclusion was used to identify viable cells, and anti-BDCA2-PE (Miltenyi) was used to identify pDCs. pDCs or PBMCs with pDCs removed were resuspended to 10⁶ cells/ml in complete RPMI (RPMI with 10% human AB serum, 1% penicillin/streptomycin/glutamine, 500 μg/ml Zosyn). Cells were spinoculated with 250 ng/ml of cell-free HIV-1_BaL for 2 hrs at 1700 rcf at room temperature. Cells were washed 1x with complete RPMI, resuspended to 10⁶ cells/ml, and incubated at 37°C for 4 hr. Cells were then harvested and RNA extracted using Qiagen RNAeasy Micro kit.

Quantitative PCR for total IFNA transcripts

Primers were designed in conserved regions of the IFNA subtypes: Forward primer 5’TCCATGAGVTGATBCAGCAGA and reverse primer 5’ ATTTCTGCTCTGACAACCTCCC (S1A Fig). cDNA was transcribed from RNA using random hexamers in the Qiagen Quantitect Reverse Transcription kit. cDNA was diluted 1:5 and 10 μl added to make a final concentration of 1× Quantitect SYBR green PCR reagent containing 8 pmol of each primer. qPCR was run on Biorad CFX96 real-time PCR machine under the following conditions: 95°C for 15 min followed by 40 cycles of 94°C 15 s, 55°C 30 s, 72°C 30 s. Specificity was determined by melt curve analysis. qPCR data was analyzed with CFX Manager software (Biorad). Copy number was interpolated using a standard curve with 108–102 copies of IFNA8 plasmid. Copies of GAPDH were determined by Taqman primer/probe assay (S1 Table).

IFNA subtype determination by Illumina sequencing

RNA from pDCs was reverse transcribed with Quantitect reverse-transcription kit (Qiagen) using a primer from a conserved region in the IFNA subtype alignment (S1A Fig). RT primer: 5’-GATCTCATGATTTCTGCTCTGAC. cDNA was added to a PCR reaction containing Phusion Hi Fidelity Taq (New England Biolabs) according to manufacturers instructions containing 8 pmol of the following Illumina primers containing random nucleotides (N) and 6-bp barcodes (INDEX#).

Forward primer: 5’AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGAT

CT NNNN INDEX1 TGCGTCTCCATGAGVTGATBCAGCAGA

Reverse primer: 5’CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT NNNN INDEX2 ATTTCTGCTCTGACAACCTCCC

PCR was run at the following conditions: 98°C for 30 min, followed by 35 cycles of 98°C 10 s, 58°C 15 s, 72°C 15 s, and a final elongation of 72°C 5 min. Sequences reads were generated in the Illumina MiSeq as recommended by the manufacturer. Resultant sequences were compared to a reference sequence database containing cDNA sequences from all members of IFNA gene family and identified as a particular IFNA subtype with a threshold of 90% identity. IFNA gene distribution was calculated based on a percentage of the total IFNA counts. IFNA1 and IFNA13 DNA sequences were identical in the amplified region and encode an identical protein and so were referred to as IFNA1/13. For simplicity the recombinant protein was noted as IFNα1.

Recombinant IFNα subtypes

All 12 recombinant IFNα subtypes were purchased from PBL Assay Science, Cat. No. 11002–1. The proteins were resuspended in PBS containing 0.1% BSA to 5.31 μg/ml according to product insert and stored at –80°C as single use aliquots.

LPMC collection and processing

Macroscopically normal human jejunum tissue samples were obtained from patients undergoing elective abdominal surgery. The use of discarded tissue was granted exempt status by COMIRB and patients signed a pre-operative consent form allowing its unrestricted use for research purposes. Protected patient information was de-identified to laboratory personnel. LPMCs were obtained and processed as previously described [32,33]. Briefly, LP mucosa was separated from muscularis mucosa, EDTA was used to separate epithelial cells, and collagenase D treatment released LPMCs. Cells were cryopreserved in RPMI + 10% DMSO + 10% FBS.

HIV-1 infection of LPMCs

Cryopreserved LPMCs were thawed by gradual addition of thaw media (90 ml RPMI + 10% FBS + 1% penicillin/streptomycin/glutamine + 100 μl DNAse). LPMCs were resuspended to 2.5×10⁶ cells/ml in complete RPMI. HIV-1 (10 ng p24/ml for Ba-L and T/F HIV-1 strains) was added and spinoculated at 1700 rcf for 2 hr at room temperature. Cells were washed 1× in complete RPMI, resuspended, and plated onto V-bottom 96 well plates at a concentration of 10⁶ cells/ml. IFNα subtypes (PBL Assay Science) were added once at a final concentration of 100 pg/ml immediately post-infection. Cells were incubated for 4 days at 37°C, then were harvested at 4 dpi for flow cytometry as previously described [32,33]. Zombie Aqua Viability Dye (Biolegend) exclusion was used to identify viable cells. The antibodies used for flow cytometry were: CD3-ECD (Beckman Coulter) or CD3-PerCP-Cy5.5 (Tonbo Biosciences), CD8-APC (BD Pharmingen), HIV-1 p24-PE (Beckman Coulter). Data were collected on a Gallios 561 flow cytometer (Beckman Coulter) and analyzed using Kaluza version 1.2 (Beckman Coulter).

Supernatants were harvested at 4 dpi and infectious titer was determined in TZM.bl reporter cells. TZM.bl cells (1 x 10⁴) were plated in a 96-well plate in 160 μl culture media (DMEM + 10% FBS + 1% PSG) with dextran sulfate (100 ng/ml). 4 dpi supernatants (40 μl) were added directly to individual wells and incubated for 48 hrs at 37°C. Half of the media was removed and cells were lysed with 100 μl Britelite luciferase reagent (Perkin Elmer), incubated for at least 1 minute, and Relative Light Units (RLU) of luminescence were determined in a VictorX5 plate reader (Perkin Elmer). The supernatants were also titered using an HIV-1 Gag p24 ELISA kit (Perkin Elmer).

ISG qPCR

Taqman primer probe combinations were used to quantify A3G, A3D, A3F, Tetherin and Mx2 relative to GAPDH (S1 Table). GeneExpression Mastermix (Life Technologies) was used according to instructions and contained 10 pmol of each primer and probe. Thermocycling conditions were as follows: 50°C 2 min and 95°C 10 min, then 40 cycles of 95°C 15 s and variable annealing temperatures (GAPDH: 64.5°C 45 s; Mx2: 62.5°C 45 s; BST-2: 60.8°C 45 s; and A3G: 56°C 40 s; A3F: 59°C 90 s; A3D: 60°C 45 s). Plates were run in the Biorad CFX96 real-time PCR machine.

Mutation analysis of proviral HIV-1 DNA

Infection of LPMCs with HIV-1 T/F or NL4-3 virus stocks with or without IFNα treatment was performed as above. At 4 dpi, cell pellets were harvested and DNA extracted using Qiagen DNAEasy kit. Amplification of the gp41/nef region was performed by nested PCR assembled as Phusion Taq reaction according to manufacturer protocol containing 10 pmol of the following primers. External PCR: Forward 5’-TTGCTCTGGAAAACTCATYTGCAC; Reverse 5’-TCAGGGAAGTAGCCTTGTGTGT. Thermocycling conditions included 98°C for 30 min and 35 cycles of 98°C 10 s, 59.5°C 20 s, 72°C 35 s and final elongation at 72°C 7 min. Following preamplification, Phusion Taq nested PCR with MiSeq-configured primers was performed:

Forward: 5’ATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT NNNN INDEX1 AGCAGTAGCTGARGGRACAGAT

Reverse: 5’CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT NNNN INDEX2 AGTGAAYTARCCCTTCCAGTCC

with the following conditions: 98°C for 30 min, 35 cycles of 98°C 10 s, 56.6°C 15 s, 72°C 17 s and final elongation at 72°C 7 min. Amplicons were sequenced by Illumina MiSeq following standard protocol. Sequences with >80% identity were matched to the corresponding reference T/F HIV-1 sequences and total, GG→AG and GA→AA mutations were evaluated using custom Perl scripts [60,79].

Statistical analysis

Data were analyzed using Prism 5.0 (GraphPad). For comparisons of data with over 2 variables (e.g., IFNα subtypes) obtained from the same donors (matched observations), repeated measures ANOVA was used for statistical analyses, followed a Dunnett’s multiple comparison test. For data with non-Gaussian distribution (evaluated using the Kolmogorov-Smirnov normality test), a nonparametric ANOVA using Friedman test was implemented followed by a Dunn’s posthoc pairwise analysis. For comparisons of two datasets, a two-tailed Student’s t-test was performed. Correlations between two datasets were determined by linear regression and evaluated by Pearson r. To compare the relative proportions of specific dinucleotide mutations, a 2 × 2 contingency analysis with Yates’ correction was used. For all statistical tests, P values < 0.05 were considered significant.

Accession numbers

Next-generation sequencing data were deposited at the NCBI Sequence Archive Bioproject PRJNA284609. Accession numbers for IFNA genes used in this work are as follows. IFNA1, NM_024013.2; IFNA2, NM_000605.3; IFNA4, NM_021068.2; IFNA5, NM_002169.2; IFNA6, NM_021002.2; IFNA7, NM_021057.2; IFNA8, NM_002170.3; IFNA10, NM_002171.2; IFNA13, NM_006900.3; IFNA14, NM_002172.2; IFNA16, NM_002173.2; IFNA17, NM_021268.2; IFNA21, NM_002175.2.

Supporting Information

Attachment 7