Direct non-productive HIV-1 infection in a T-cell line is driven by cellular activation state and NFκB
© Dahabieh et al.; licensee BioMed Central Ltd. 2014
Received: 20 August 2013
Accepted: 4 February 2014
Published: 7 February 2014
Molecular latency allows HIV-1 to persist in resting memory CD4+ T-cells as transcriptionally silent provirus integrated into host chromosomal DNA. Multiple transcriptional regulatory mechanisms for HIV-1 latency have been described in the context of progressive epigenetic silencing and maintenance. However, our understanding of the determinants critical for the establishment of latency in newly infected cells is limited.
In this study, we used a recently described, doubly fluorescent HIV-1 latency model to dissect the role of proviral integration sites and cellular activation state on direct non-productive infections at the single cell level. Proviral integration site mapping of infected Jurkat T-cells revealed that productively and non-productively infected cells are indistinguishable in terms of genomic landmarks, surrounding epigenetic landscapes, and proviral orientation relative to host genes. However, direct non-productive infections were inversely correlated with both cellular activation state and NFκB activity. Furthermore, modulating NFκB with either small molecules or by conditional overexpression of NFκB subunits was sufficient to alter the propensity of HIV-1 to directly enter a non-productive latent state in newly infected cells. Importantly, this modulatory effect was limited to a short time window post-infection.
Taken together, our data suggest that cellular activation state and NFκB activity during the time of infection, but not the site of proviral integration, are important regulators of direct HIV-1 non-productive infections.
KeywordsHIV-1 Latency LTR CMV Promoter eGFP mCherry Double-label Silent-infection NFκB
Integrated HIV-1 provirus transcribes messenger and genomic RNA to produce progeny virions. However, the HIV-1 promoter can also exist in an inactive state, and the subsequent lack of viral products allows latently infected cells to escape both immune surveillance and viral cytopathic effects (reviewed in [1–3]). Importantly, latent HIV-1 remains functional and can be reactivated by cellular activation, for example. This results in proviral transcription and production of new virions . Thus, HIV-1 latency, which allows the virus to persist indefinitely during highly active antiretroviral therapy (HAART), is one of the most significant barriers to HIV-1 eradication.
HIV-1 latency is generally regarded as a product of proviral transcriptional silencing. Numerous silencing mechanisms have been characterized using in vitro latency models that require cellular activation and long-term culturing to identify and isolate latently infected cells. Given these requirements, the majority of known silencing mechanisms pertain to the progressive silencing of productive infections and the maintenance of a latent state. Nevertheless, known HIV-1 transcriptional silencing mechanisms include: 1) suboptimal T-cell activation, 2) low levels of transcriptional activator function, 3) restrictive chromatin structure at the site of integration, 4) transcriptional interference at the site of integration, 5) low pTEF-b (CDK9/Cyclin T1) levels, and 6) repressive HIV-1 LTR nucleosome positioning and histone post-translational modifications (reviewed in [1–3]).
Without the ability to identify latently infected cells early, and in the absence of activation stimuli, it is difficult to evaluate which HIV-1 transcriptional silencing mechanisms are critical for latency establishment in newly infected cells. Thus, we and others have recently developed double-labeled HIV-1 latency models that can detect both productive and non-productive proviral states early post-infection [5, 6]. Application of these models to both cell lines and activated primary CD4+ T-cells suggests that direct non-productive infections (latency) actually represent the majority of HIV-1 infections [5, 6]. This conclusion is further supported by other studies identifying silent/inducible infections early in infection [7, 8]. Taken together, these studies provide significant support for the role of direct silencing in HIV-1 latency establishment, and highlight the importance of studying establishment mechanisms in newly infected cells.
In this study, we use our doubly fluorescent HIV-1 reporter  to directly evaluate potential mechanisms responsible for the formation of direct non-productive states in newly infected Jurkat T-cells. We focus on two highly variable HIV-1 transcriptional regulatory mechanisms: 1) proviral integration site, and 2) cellular activation state and NFκB signaling. First, we show that direct non-productive infections occur at all sites of integration, thereby excluding a role for viral integration site locations. Instead, the occurrence of non-productive infections was inversely correlated with cellular activation state and NFκB activity. Moreover, modulating NFκB levels at the time of infection, either by small molecules or NFκB subunit overexpression, was sufficient to alter the occurrence of non-productive infection in newly infected cells. Taken together, our data suggest that the cellular level of NFκB activity at the time of infection, rather than the site of viral integration, controls the establishment of HIV-1 latency in newly infected T-cell lines. These findings are of relevance to HIV-1 eradication strategies since they may point to putative targets for therapeutic interventions minimizing HIV-1 latency establishment rather than latency reactivation.
Both productive and non-productive HIV-1 proviruses are integrated at similar locations
HIV-1 proviral integration sites are highly variable [1–3, 9, 10]. In some latency models, proximity to certain genomic features (alphoid repeats - , gene deserts - , and very highly expressed genes [7, 12]) has been associated with proviral transcriptional silencing. However, a recent meta-analysis of integration sites found that, in five distinct latency models using either cell-lines or primary T-cells, these associations are not universal properties of HIV-1 latency, but rather are specific to the models in which they were identified . Importantly, this study highlights the importance of characterizing the effect of integration site in each individual latency model. In this light, we sought to determine whether proviral integration sites were different between productively and non-productively RGH-infected Jurkat cells. We sorted total RGH infected cells into non-productively infected ‘red’ (eGFP- mCherry+; ~4% of total), and productively infected ‘yellow’ (eGFP+ mCherry+; ~2% of total) cell populations with more than 90% purity (Figure 1B). The ‘double negative’ population (eGFP- mCherry-) was also sorted and analyzed since we previously estimated that ~30% of all RGH infections result in direct repression of both the LTR and CMV promoters . To identify sites of viral integration, genomic DNA from each population was extracted, digested with MseI, and ligated to adapters . Nested PCR was used to amplify LTR-host chromosome junctions and resulting amplicons were sequenced by 454 pyrosequencing . Reads were filtered for quality and mapped to the human genome using the INSIPID pipeline .
We mapped 2,900 and 4,271 unique integration sites in the ‘red’ and ‘yellow’ populations, respectively. Consistent with our previous characterization of ‘double negative’ RGH infected cells , we were also able to map 1,195 integration sites in this population, which represent proviruses in which both eGFP and mCherry markers were silenced directly upon infection.
We also compared the epigenetic landscape surrounding viral integration sites in the different RGH infected populations using INSIPID’s annotated epigenetic data from independent Jurkat and CD4+ T-cell experiments [15, 17–23]. No significant differences were observed between the ‘red’ and ‘yellow’ populations (Additional file 1: Figure S1A). Integrations in the ‘double negative’ population were, however, significantly less frequently associated with nucleosomes and histone post-translational modifications, as compared to the ‘red’ or ‘yellow’ populations (Additional file 1: Figure S1A). This result, and the increased association with genes for the ‘double negative’ population (Figure 3A), suggests some effect of integration site on transcriptional repression. However, this effect is small and likely does not explain the transcriptional differences between the different RGH infected populations. Moreover, this is likely not an HIV-1 specific effect since both the LTR and CMV promoters are silenced in the ‘double negative’ population.
In human cells, the occurrence of transcriptional regulation-associated histone marks is often correlated with nucleosome position relative to gene promoters and gene bodies . Therefore, we plotted RGH integration densities as a function of both the average distance across genes and the average distance from gene transcriptional start sites (TSS), however we observed no differences between cell populations in either case (Additional file 1: Figure S1B).
We next experimentally tested the effect of the epigenetic landscape on the productivity of RGH infection by utilizing an N74D capsid mutant that causes integration into regions of lower gene density and increased heterochromatin [24–26]. However, no differences were observed in the ratio of non-productive (‘red’) to productive (‘yellow’) infections between the RGH N74D capsid mutant and the wild-type RGH vector, further suggesting that epigenetic profiles surrounding integration sites are not major mediators of direct non-productive infection (Additional file 1: Figure S1C).
The orientation of proviral integrations within host genes has also been implicated in HIV-1 transcriptional regulation and latency [27–29]. However, another study did not observe a role for proviral orientation across multiple latency models . Therefore, we compared the frequency of parallel and anti-parallel genic integrations between the RGH infected cell populations. The frequency of parallel and anti-parallel intragenic orientations were similar (~40% of total integrations), and not significantly different between cell populations (Figure 3B). Supplementary to proviral orientation, we analyzed the nucleotide sequences around the site of integration in RGH infected cells. These sequences were similar between cell populations and consistent with previously described HIV-1 target sites  (Additional file 2: Figure S2A). Moreover, gene ontology analysis did not reveal any differences in the types of genes harboring integrated provirus between the RGH infected cell populations (Additional file 2: Figure S2B).
Taken together, our data suggests that integration sites fail to play a significant role in regulating direct non-productive RGH infections in newly infected Jurkat cells. Therefore, alternative mechanisms are likely to dictate this process in this model T-cell system.
Direct non-productive HIV-1 infection is associated with lower cellular activation and NFκB signaling
HIV-1 transcription is tightly linked to both cellular activation and the activity of signaling pathways downstream of the T-cell receptor (reviewed in [1, 3]). Moreover, the NFκB pathway is an important and potent regulator of HIV-1 transcription (reviewed in ), and has been previously implicated in mediating early productive HIV-1 infections . Given that direct non-productive RGH infection is independent of proviral integration sites (Figures 2 and 3, S1 and S2), we speculated that differences in cellular activation state and NFκB signaling around the time of infection could be responsible.
To specifically address the role of NFκB in the establishment of direct non-productive infections, we infected cells with RGH and examined NFκB levels four days post-infection by intracellular staining for the DNA-binding p50 subunit of NFκB and the activated form of the trans-activating p65 subunit (S529-phospho) (Figure 4B). Both NFκB subunit levels were positively correlated with active transcription, as the gated ‘red’ and ‘yellow’ populations expressed approximately 1.3 and 1.5 fold more of both subunits, respectively, whereas the ‘double negative’ population expressed the lowest levels of both subunits (Figure 4B). Of note, expression of p50 and p65-S529-phospho increased concomitantly in 'red' and 'yellow' cells, suggesting that productive infections are associated with higher cellular levels of the activating form of NFκB (p65-p50) rather than the inhibitory p50-p50 form (Figure 4B). Importantly, RGH infection does not appear to up regulate NFκB, as the total RGH infected population and mock infected cells expressed similar amounts of both NFκB subunits (Additional file 3: Figure S3B).
To futher evaluate the role of NFκB signaling in promoting productive infection in newly infected cells, we simultaneously monitored both HIV-1 transcription as well as NFκB signaling at the single cell level. We created an RGH isogenic clone bearing the blue fluorescent protein tagBFP in place of eGFP (Red-Blue-HIV-1, RBH), as well as five Jurkat NFκB reporter cell lines bearing integrated eGFP constructs under the control of an NFκB responsive promoter (Figure 4C). Infection of Jurkat cells with RBH resulted in an infection profile similar to that of RGH i.e. the majority of RBH infections resulted in direct non-productive infection (Figure 4D). Treatment of the NFκB-eGFP reporter cell lines with known NFκB agonists TNFα and PMA/Iono resulted in 2.2 and 3.7 fold increases in eGFP mean fluorescence intensity (MFI), respectively (Figure 4E, Additional file 4: Figure S4A). Infection of the NFκB reporter cell lines with RBH virus showed that the productively infected cells (tagBFP + mCherry+, ‘purple’) were characterized by higher eGFP MFI (indicative of active NFκB signaling) compared to non-productively infected cells (tagBFP- mCherry+ ‘red’ , or tagBFP- mCherry- ‘double negative’ , Figure 4F – clone 1, Additional file 4: Figure S4B – clones 2–4). Importantly, RBH infection itself did not up regulate NFκB, as we did not observe substantial differences in eGFP fluorescence intensity between RBH- and mock-infected total cells (Additional file 3: Figure S3C). These data are consistent with the results of the intracellular NFκB staining of RGH infected Jurkat cells, and lend further support to the role of NFκB in regulating early RGH productive infections.
NFκB modulating drugs administered at the time of infection can alter the occurrence of productive RGH infection
Our data indicate that cellular activation and NFκB signaling may influence the occurrence of direct non-productive infections in RGH infected cells (Figure 4). Therefore, we hypothesized that modulating NFκB activity during infection would affect the formation of direct non-productive infections. To test this, we treated Jurkat cells with TNFα (NFκB signaling agonist), BMS-345541 (IκB kinase inhibitor), SAHA (HDAC inhibitor) or DMSO (control) during RGH infection. The infected cells were cultured for three days, treated with either DMSO or PMA/Iono for 24 hours, and then analyzed by flow cytometry.
Taken together, these results suggest that direct non-productive RGH infection is regulated by the action of NFκB signaling at the time of infection and that the propensity to form a non-productive infection can be modulated by NFκB agonists (TNFα and PMA/Iono) and antagonists (BMS-345541).
Specifically modulating NFκB is sufficient to modulate the occurrence of productive RGH infection
While the major target of TNFα signaling is NFκB, TNFα can also affect the stress response related JNK-MAPK pathway and its downstream factor AP-1 (reviewed in ). Although TNFα-mediated reduction of RGH latency can likely be attributed to NFκB (Figures 4 and 5), it is possible that other pleiotropic effects may be contributing to the observed results. To test NFκB signaling in a more specific and temporal fashion, we generated Jurkat cell-lines bearing doxycycline inducible versions of a dominant negative (DN) form of the IκBα repressor (S32A/S36A - ), or the NFκB p65 subunit to allow direct down- or up-regulation of NFκB signaling, respectively.
The determination of productive RGH infection occurs around the time of infection
To further explore the timing of RGH infection productivity, and to minimize the confounding impact of viral state on cellular outgrowth in a mixed population, we repeated the TNFα-treatment-recovery experiment with RGH infected Jurkat cells sorted into their constituent ‘double negative’ , ‘red’ , and ‘yellow’ subpopulations. In each of the ‘double negative’ and ‘red’ populations, TNFα treatment of sorted cells activated a substantial proportion of non-productive proviruses, as reflected in the increase in the number of ‘yellow’ cells (Figure 7B). As expected, TNFα treatment of the ‘yellow’ population had a minimal effect, as the majority of proviruses were already transcriptionally active (Figure 7B). Interestingly, when the sorted populations were left to recover for four days, the ‘double negative’ , ‘red’ , and ‘yellow’ TNFα treated cells all became indistinguishable from their matched DMSO treated pairs (Figure 7B). This data is consistent with results from the bulk RGH infected cells (Figure 7A). Of note, we did not observe major differences in the ratio of live cells (FSC/SSC) between DMSO and TNFα treatments, suggesting that HIV-induced cell-toxicity is not a substantial issue (data not shown). Furthermore, these findings collectively support the idea that non-productive RGH infection is established early (within four days post-infection) and permanently, such that TNFα treatment applied after the infection can no longer permanently alter the proportion of non-productively infected cells.
Despite extensive knowledge of individual mechanisms of HIV-1 transcriptional regulation, our understanding of the critical determinants for HIV-1 latency establishment in newly infected cells is limited. This knowledge gap is largely due to the inability to accurately identify latently infected cells early post-infection, and in their native state (i.e. without inducing cellular and viral activation). To circumvent these road-blocks, we and others have recent developed ‘double-labeled’ HIV-1 vectors incorporating constitutive markers of infection [5, 6]. Initial studies with these models have revealed that a large proportion of HIV-1 infections result in a direct latent state, however the mechanisms by which these infections form remains unknown. In this study we used a doubly labeled HIV-1 latency model  to show that the cellular activation state and NFκB activity around the time of infection, but not viral integration site, are important for regulating direct non-productive infections in Jurkat T-cells.
Although primary CD4+ T-cells are considered to be the gold standard for HIV-1 latency models, we note a number of technical issues precluding the precise and unbiased evaluation of direct non-productive RGH infection in resting CD4+ T-cells. Nevertheless, we previously observed that RGH infection of activated primary CD4+ T-cells from three donors results in a high degree of direct non-productive infection, comparable to Jurkat cells . Furthermore, another group also noted a high degree of latency in activated primary CD4+ T-cells . This suggests that activated primary CD4+ T-cell infections may be accurately recapitulated in Jurkat cells. Thus, the results obtained with Jurkat cells in this study are likely to hold in primary T-cells, especially if primary cells must be activated in order to render them permissive to infection.
Previous reports have implicated integration site variability as a determinant for HIV-1 latency. Most notably, latency was correlated with integration into gene deserts, highly transcribed genes (high transcriptional interference) and alphoid repeats [11, 12, 37]. However, high-throughput analysis of HIV-1 integration sites indicates that integrations into such regions are highly disfavored [9, 10]. Instead, most proviruses are located within actively transcribed genes that are enriched for histone marks associated with active chromatin (H3K4me3, lysine acetylation), and depleted for marks associated with repressive chromatin (H3K9me3, H3K27me3) [9, 10, 12, 38, 39]. We speculate that latency models using cellular activation and long-term culturing to identify and establish latency could select for the most strongly repressed latent proviruses, thereby resulting in an over-representation of such disfavored integration locations. Our analysis of proviral locations in RGH infected Jurkat cells shows little evidence for integration sites regulating the difference between ‘red’ (eGFP- mCherry+) and ‘yellow’ (eGFP+ mCherry+) cells (Figures 2 and 3, S1, and S2). Furthermore we did not find any evidence for enrichment of the aforementioned rare types of integration sites. Moreover, the frequency at which these types of integrations occur is incompatible with the degree of direct non-productive infections observed in the RGH model  and by other groups [6–8]. Our conclusions are in agreement with a recent meta-analysis of HIV-1 integration sites in five primary and cell line latency models . In this study, the authors found no genomic predictors of latency and, interestingly, only little overlap of chromosomal features between latency models . This highlights the intrinsic mechanistic variability of HIV-1 latency models, as well as the need to fully characterize determinants of latency in each model.
Our data indicate that direct non-productive infections are established around the time of infection, and that this process is fundamentally different from latency in which productive infections are silenced over time (Figures 5, 6, 7, and 8). We note that treatments with NFκB agonists and antagonists early during infection could profoundly alter the occurrence of non-productive infection days later, whereas treatment four days post-infection did not result in long term modulation (Figures 7 and 8). This indicates that once a non-productive state is established during initial infection, it becomes ‘imprinted’, possibly through subsequent epigenetic modifications (Figure 8).
Our results contribute to an emerging body of work that links cellular activation state and transcription factor availability with the formation of HIV-1 latency. Although evidence is mounting that NFκB contributes to latency determination in newly infected cells (this study and ), we cannot exclude the actions of other transcription factors and/or upstream regulators in modulating latency. Most notably, the factors SP1 , AP1 , and the Jun N-terminal protein kinase (JNK) [47, 48] have all been implicated in HIV-1 latency. It will be of great benefit to reconcile these studies and develop a comprehensive understanding of how individual factors/mechanisms act cumulatively to establish latency in newly infected cells. Indeed, fully understanding this process is paramount to successfully devising biologically relevant model systems suitable for screening novel latency modulating therapeutics.
HIV-1 infection of Jurkat T-cells results in both productive and non-productive proviral states shortly after infection. Our data indicate that the differences between productive and non-productive infections are not caused by the location or orientation of viral integrations. Instead, the cellular activation state and NFκB activity around the time of infection determine the outcome of viral infections and, in turn, early latency.
Viral vectors and constructs
The Red-Green-HIV-1 (RGH) molecular clone was used as previously described . To construct the gag-N74D RGH clone, the mutation was created by PCR mediated site directed mutagenesis and cloning of the amplicon into the BspQI/ApaI sites of the previously described RGH construct . The Red-Blue-HIV-1 (RBH) molecular clone was created by cloning a synthesized tagBFP construct (GeneWiz) into the SapI/SphI sites of RGH.
pTRIPz-EV, pTRIPz-DN-IκBα and pTRIPz-p65 are derivatives of the commercial doxycycline-inducible lentiviral vector pTRIPZ-Ctrl (Thermo Fisher). pTRIPz-EV (empty vector) was created by digestion with AgeI/MluI, blunting with Klenow polymerase, and re-ligation. pTRIPz-DN-IκBα contains the S32A/S36A mutant version of the IκBα repressor PCR amplified from pSVK3-IKBα-2N , which was cloned into the AgeI/MluI sites of pTRIPz-Ctrl. pTRIPz-p65 contains a PCR amplified NFκB p65 open reading frame cloned into the AgeI/MluI sites of pTRIPz-Ctrl.
Cell culture, virion production, and transduction
Jurkat E6-1 , HEK293T (ATCC), and derivative cell lines created in this study were cultured as previously described . VSV-G pseudotyped viral stocks were created by transfecting HEK293T cells with envelope deleted viral molecular clones and pHEF-VSVg  in a 10:1 ratio as previously described . Unless otherwise indicated, Jurkat E6-1 cells were spinoculated as previously described . Briefly, 5 ×1 05 cells in 1 mL culture media (+ 4 μg/mL polybrene) were spin-infected (1.5 hr, 500 × g, room temperature) with 25 μL of viral stock, so as to yield an average infection rate of than 10-15% and ensure single-copy integrations.
NFκB-eGFP viral stocks were produced in HEK293T cells by co-transfecting pGreenFire1-NF-κB (Systems Biosciences), pHEF-VSVg , pLP1-gag/pol, pLP2-Rev, and pcDNA3.1+-Tat (2 μg each, 30 μg polyethylenimine reagent). Purified and concentrated viral stocks were prepared as previously described . NFκB reporter cell lines were created by transducing Jurkat cells with NFκB-eGFP virus (MOI ~ 4), followed by puromycin selection (1 μg/mL - Clontech). Resistant cells were subsequently maintained in complete media supplemented with 0.5 μg/mL puromycin.
pTRIPz viral stocks and stable cell lines were produced as described above for NFκB-eGFP, except that the lentiviral vectors pTRIPz-EV, pTRIPz-DN-IκBα or pTRIPz-p65 were used.
Flow cytometry and staining
Analysis of infected cells by flow cytometry and live cell sorting were performed as previously described . Of note, in all experiments, analysis was limited to live cells by FSC/SSC gating at the time of data acquisition. Unless otherwise stated, infected cells were analyzed four days post-infection. Jurkat E6-1 cells were stained and analyzed for CD69 as previously described  except that antibodies were conjugated to PE-Cy7 and 1 μL of antibody was used per 1 × 105 cells (BD Biosciences). Jurkat cells were stained with PE-Cy7-NFκB p65 (pS529) (BD Biosciences) and NFκB p50 (Abcam) with Pacific Blue conjugated secondary antibody (Life Technologies) as previously described .
Infected cells were treated with the various compounds for the times and durations indicated in individual experiments. Compounds were added at the listed concentrations to complete media. Unless otherwise stated, compounds were used at the following concentrations: TNFα, 10 ng/mL (Sigma); SAHA, 0.5 μM ; PMA, 4 ng/mL (Sigma); Ionomycin, 1 μM (Sigma), BMS-345541, 5 μM (Sigma).
Pyrosequencing of integration sites
HIV-1 integration sites were analyzed by 454 deep-sequencing as previously described . Briefly, Jurkat cells were infected with RGH and sorted into constituent populations three days post-infection (eGFP- mCherry-, eGFP- mCherry+, eGFP+ mCherry+). Genomic DNA was extracted from ~ 5 × 105 cells of each population, digested with MseI and ligated to adaptors. Nested PCR with adapter and LTR specific primers was performed to amplify the HIV-host genome junctions. After gel extraction of 100–600 bp fragments, amplicons were subjected to pyrosequencing on a 454 GS Junior machine (Roche). Data was analyzed using the Integration Site Pipeline and Database (INSIPID) web tool (Bushman Lab - http://microb215.med.upenn.edu/Insipid/ - [10, 15], Circos , SeqMonk software (http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/), WebLogo3 (http://weblogo.threeplusone.com/create.cgi), and the R/Bioconductor package ‘goProfiles’ (http://bioconductor.org/packages/2.11/bioc/html/goProfiles.html).
RGH infected Jurkat cells were lysed in NP-40 lysis buffer (50 mM Tris, pH 8.0, 150 mM NaCl, 1% (v/v) NP-40, 0.1% (w/v) SDS) supplemented with 1x protease inhibitor cocktail (Roche). Lysates were cleared by centrifugation (10 min, 16000 × g, 4°C), mixed with 4× SDS-PAGE sample buffer, and boiled for 5 min. Whole cell extracts (40 μg) were separated on a 12% SDS-PAGE gel and then transferred to nitrocellulose membrane. Membranes were blocked with 2% (w/v) BSA in PBS-Tween (0.05% v/v) and then incubated with primary antibody overnight at 4°C. Antibodies used were as follows: IκBα – Abcam 32518 [1:5000], NFκB p65 – Abcam 7970 [1:500], GAPDH – Abcam 9484 [1:4000]. After washing and incubation with HRP conjugated secondary antibody, membranes were washed and signal was developed with SuperSignal West Femto chemi-luminescent substrate (Thermo Fisher).
Unless otherwise stated, experiments were performed in biological triplicate. Where appropriate, statistical inference was performed on quantitative data. Two group testing was performed using the Student’s T-test, while comparison between multiple groups was made using one-way-ANOVA followed by pairwise two-group testing (Student’s T-test). Statistical analysis was performed in R 2.15.1 (http://www.r-project.org/). Integration site analysis heatmaps were created using the INSIPID pipeline (Figures 3A and Additional file 1: Figure S1B) utilizing previously described statistical methodology [10, 15]. Briefly, for each identified integration site, matched random controls were created in silico. This pairing of experimental and control sites allows for computation of relative enrichment and de-enrichment profiles using a receiver operating characteristic framework. Comparisons between sets of integration sites (samples) for statistical significance are performed by calculating Wald-type test statistics, which are then tested using Chi Square methods.
We thank Andy Johnson and Justin Wong of the UBC Flow Cytometry Facility for live cell sorting and analysis. We thank Winnie Dong and Dennison Chan for assistance with pyrosequencing. We thank Nirav Milani for help with the INSIPID pipeline. We gratefully acknowledge Pauline Johnson for the lentiviral packaging accessory plasmids and Amy Saunders for assistance with CD69 staining. We thank Jacob Hodgson, Adam Chruscicki, Kevin Eade, Benjamin Martin, and Nicolas Coutin for thoughtful discussions and review of this manuscript.
The following reagents were obtained through the AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH: Jurkat Clone E6-1 from Dr. Arthur Weiss, SAHA (Vorinostat), and pHEF-VSVG from Dr. Lung-Ji Chan.
This work was supported by Canadian Institute of Health Research (CIHR) grants to I.S. (MOP-77807, HOP-120237), NIH/NIAID grants to V.S. (AI064001, AI104406, AI90935). M.S.D is supported by a CIHR fellowship (CGD-96495). P.R.H. is supported by a CIHR/GSK chair in clinical virology at the University of British Columbia.
- Donahue DA, Wainberg MA: Cellular and molecular mechanisms involved in the establishment of HIV-1 latency. Retrovirology. 2013, 10: 11-10.1186/1742-4690-10-11.PubMed CentralView ArticlePubMedGoogle Scholar
- Siliciano RF, Greene WC: HIV latency. Cold Spring Harb Perspect Biol. 2011, 1: a007096-Google Scholar
- Karn J, Stoltzfus CM: Transcriptional and posttranscriptional regulation of HIV-1 gene expression. Cold Spring Harb Perspect Biol. 2012, 2: a006916-Google Scholar
- Finzi D, Hermankova M, Pierson T, Carruth LM, Buck C, Chaisson RE, Quinn TC, Chadwick K, Margolick J, Brookmeyer R, Gallant J, Markowitz M, Ho DD, Richman DD, Siliciano RF: Identification of a reservoir for HIV-1 in patients on highly active antiretroviral therapy. Science. 1997, 278: 1295-1300. 10.1126/science.278.5341.1295.View ArticlePubMedGoogle Scholar
- Dahabieh MS, Ooms M, Simon V, Sadowski I: A doubly fluorescent HIV-1 reporter shows that the majority of integrated HIV-1 is latent shortly after infection. J Virol. 2013, 87: 4716-4727. 10.1128/JVI.03478-12.PubMed CentralView ArticlePubMedGoogle Scholar
- Calvanese V, Chavez L, Laurent T, Ding S, Verdin E: Dual-color HIV reporters trace a population of latently infected cells and enable their purification. Virology. 2013, 446: 283-292. 10.1016/j.virol.2013.07.037.PubMed CentralView ArticlePubMedGoogle Scholar
- Duverger A, Jones J, May J, Bibollet-Ruche F, Wagner FA, Cron RQ, Kutsch O: Determinants of the establishment of human immunodeficiency virus type 1 latency. J Virol. 2009, 83: 3078-3093. 10.1128/JVI.02058-08.PubMed CentralView ArticlePubMedGoogle Scholar
- van der Sluis RM, van Montfort T, Pollakis G, Sanders RW, Speijer D, Berkhout B, Jeeninga RE: Dendritic cell-induced activation of latent HIV-1 provirus in actively proliferating primary T lymphocytes. PLoS Pathog. 2013, 9: e1003259-10.1371/journal.ppat.1003259.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang GP, Ciuffi A, Leipzig J, Berry CC, Bushman FD: HIV integration site selection: analysis by massively parallel pyrosequencing reveals association with epigenetic modifications. Genome Res. 2007, 17: 1186-1194. 10.1101/gr.6286907.PubMed CentralView ArticlePubMedGoogle Scholar
- Brady T, Agosto LM, Malani N, Berry CC, O'Doherty U, Bushman F: HIV integration site distributions in resting and activated CD4+ T cells infected in culture. AIDS. 2009, 23: 1461-1471. 10.1097/QAD.0b013e32832caf28.PubMed CentralView ArticlePubMedGoogle Scholar
- Jordan A, Bisgrove D, Verdin E: HIV reproducibly establishes a latent infection after acute infection of T cells in vitro. EMBO J. 2003, 22: 1868-1877. 10.1093/emboj/cdg188.PubMed CentralView ArticlePubMedGoogle Scholar
- Lewinski MK, Bisgrove D, Shinn P, Chen H, Hoffmann C, Hannenhalli S, Verdin E, Berry CC, Ecker JR, Bushman FD: Genome-wide analysis of chromosomal features repressing human immunodeficiency virus transcription. J Virol. 2005, 79: 6610-6619. 10.1128/JVI.79.11.6610-6619.2005.PubMed CentralView ArticlePubMedGoogle Scholar
- Sherrill-Mix S, Lewinski MK, Famiglietti M, Bosque A, Malani N, Ocwieja KE, Berry CC, Looney D, Shan L, Agosto LM, Pace MJ, Siliciano RF, O'Doherty U, Guatelli J, Planelles V, Bushman FD: HIV latency and integration site placement in five cell-based models. Retrovirology. 2013, 10: 90-10.1186/1742-4690-10-90.PubMed CentralView ArticlePubMedGoogle Scholar
- Ciuffi A, Barr SD: Identification of HIV integration sites in infected host genomic DNA. Methods. 2011, 53: 39-46. 10.1016/j.ymeth.2010.04.004.View ArticlePubMedGoogle Scholar
- Berry C, Hannenhalli S, Leipzig J, Bushman FD: Selection of target sites for mobile DNA integration in the human genome. PLoS Comput Biol. 2006, 2: e157-10.1371/journal.pcbi.0020157.PubMed CentralView ArticlePubMedGoogle Scholar
- Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, Wiltshire T, Orth AP, Vega RG, Sapinoso LM, Moqrich A, Patapoutian A, Hampton GM, Schultz PG, Hogenesch JB: Large-scale analysis of the human and mouse transcriptomes. Proc Natl Acad Sci. 2002, 99: 4465-4470. 10.1073/pnas.012025199.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang Z, Zang C, Rosenfeld JA, Schones DE, Barski A, Cuddapah S, Cui K, Roh T-Y, Peng W, Zhang MQ, Zhao K: Combinatorial patterns of histone acetylations and methylations in the human genome. Nat Genet. 2008, 40: 897-903. 10.1038/ng.154.PubMed CentralView ArticlePubMedGoogle Scholar
- Schones DE, Cui K, Cuddapah S, Roh T-Y, Barski A, Wang Z, Wei G, Zhao K: Dynamic regulation of nucleosome positioning in the human genome. Cell. 2008, 132: 887-898. 10.1016/j.cell.2008.02.022.View ArticlePubMedGoogle Scholar
- Jothi R, Cuddapah S, Barski A, Cui K, Zhao K: Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Acids Res. 2008, 36: 5221-5231. 10.1093/nar/gkn488.PubMed CentralView ArticlePubMedGoogle Scholar
- Robertson AG, Bilenky M, Tam A, Zhao Y, Zeng T, Thiessen N, Cezard T, Fejes AP, Wederell ED, Cullum R, Euskirchen G, Krzywinski M, Birol I, Snyder M, Hoodless PA, Hirst M, Marra MA, Jones SJM: Genome-wide relationship between histone H3 lysine 4 mono- and tri-methylation and transcription factor binding. Genome Res. 2008, 18: 1906-1917. 10.1101/gr.078519.108.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang Z, Zang C, Cui K, Schones DE, Barski A, Peng W, Zhao K: Genome-wide mapping of HATs and HDACs reveals distinct functions in active and inactive genes. Cell. 2009, 138: 1019-1031. 10.1016/j.cell.2009.06.049.PubMed CentralView ArticlePubMedGoogle Scholar
- Cui K, Zang C, Roh T-Y, Schones DE, Childs RW, Peng W, Zhao K: Chromatin signatures in multipotent human hematopoietic stem cells indicatethe fate of bivalent genes during differentiation. Stem Cell. 2009, 4: 80-93.Google Scholar
- Meylan S, Groner AC, Ambrosini G, Malani N, Quenneville S, Zangger N, Kapopoulou A, Kauzlaric A, Rougemont J, Ciuffi A, Bushman FD, Bucher P, Trono D: A gene-rich, transcriptionally active environment and the pre-deposition of repressive marks are predictive of susceptibility to KRAB/KAP1- mediated silencing. BMC Genomics. 2011, 12: 378-10.1186/1471-2164-12-378.PubMed CentralView ArticlePubMedGoogle Scholar
- Koh Y, Wu X, Ferris AL, Matreyek KA, Smith SJ, Lee K, Kewalramani VN, Hughes SH, Engelman A: Differential effects of human immunodeficiency virus type 1 capsid and cellular factors nucleoporin 153 and LEDGF/p75 on the efficiency and specificity of viral DNA integration. J Virol. 2013, 87: 648-658. 10.1128/JVI.01148-12.PubMed CentralView ArticlePubMedGoogle Scholar
- Schaller T, Ocwieja KE, Rasaiyaah J, Price AJ, Brady TL, Roth SL, SEP H e, Fletcher AJ, Lee K, Kewalramani VN, Noursadeghi M, Jenner RG, James LC, Bushman FD, Towers G: HIV-1 capsid-cyclophilin interactions determine nuclear import pathway. Integration targeting and replication efficiency. PLoS Pathog. 2011, 7: e1002439-10.1371/journal.ppat.1002439.PubMed CentralView ArticlePubMedGoogle Scholar
- Ocwieja KE, Brady TL, Ronen K, Huegel A, Roth SL, Schaller T, James LC, Towers GJ, Young JAT, Chanda SK, Konig R, Malani N, Berry CC, Bushman FD: HIV integration targeting: a pathway involving Transportin-3 and the nuclear pore protein RanBP2. PLoS Pathog. 2011, 7: e1001313-10.1371/journal.ppat.1001313.PubMed CentralView ArticlePubMedGoogle Scholar
- Han Y, Lin YB, An W, Xu J, Yang H-CC, O'Connell K, Dordai D, Boeke JD, Siliciano JD, Siliciano RF: Orientation-dependent regulation of integrated HIV-1 expression by host gene transcriptional readthrough. Cell Host Microbe. 2008, 4: 134-146. 10.1016/j.chom.2008.06.008.PubMed CentralView ArticlePubMedGoogle Scholar
- Lenasi T, Contreras X, Peterlin BM: Transcriptional interference antagonizes proviral gene expression to promote HIV latency. Cell Host Microbe. 2008, 4: 123-133. 10.1016/j.chom.2008.05.016.PubMed CentralView ArticlePubMedGoogle Scholar
- Shan L, Yang HC, Rabi SA, Bravo HC, Shroff NS, Irizarry RA, Zhang H, Margolick JB, Siliciano JD, Siliciano RF: Influence of host gene transcription level and orientation on HIV-1 latency in a primary-cell model. J Virol. 2011, 85: 5384-5393. 10.1128/JVI.02536-10.PubMed CentralView ArticlePubMedGoogle Scholar
- Chan JKL, Greene WC: NF-κB/Rel: agonist and antagonist roles in HIV-1 latency. Curr Opin HIV AIDS. 2011, 6: 12-18. 10.1097/COH.0b013e32834124fd.PubMed CentralView ArticlePubMedGoogle Scholar
- Lopez-Cabrera M, Munoz E, MV B z, Ursa MA, Santis AG, Sanchez-Madrid F: Transcriptional regulation of the gene encoding the human C-type lectin leukocyte receptor AIM/CD69 and functional characterization of its tumor necrosis factor-alpha-responsive elements. J Biol Chem. 1995, 270: 21545-21551. 10.1074/jbc.270.37.21545.View ArticlePubMedGoogle Scholar
- Archin NM, Espeseth A, Parker D, Cheema M, Hazuda D, Margolis DM: Expression of latent HIV induced by the potent HDAC inhibitor suberoylanilide hydroxamic acid. AIDS Res Hum Retrovir. 2009, 25: 207-212. 10.1089/aid.2008.0191.PubMed CentralView ArticlePubMedGoogle Scholar
- Archin NM, Liberty AL, Kashuba AD, Choudhary SK, Kuruc JD, Crooks AM, Parker DC, Anderson EM, Kearney MF, Strain MC, Richman DD, Hudgens MG, Bosch RJ, Coffin JM, Eron JJ, Hazuda DJ, Margolis DM: Administration of vorinostat disrupts HIV-1 latency in patients on antiretroviral therapy. Nature. 2012, 487: 482-485. 10.1038/nature11286.PubMed CentralView ArticlePubMedGoogle Scholar
- Van Lint C, Emiliani S, Verdin E: The expression of a small fraction of cellular genes is changed in response to histone hyperacetylation. Gene Expr. 1996, 5: 245-253.PubMedGoogle Scholar
- Chen G, Goeddel DV: TNF-R1 signaling: a beautiful pathway. Science. 2002, 296: 1634-1635. 10.1126/science.1071924.View ArticlePubMedGoogle Scholar
- Kwon H, Pelletier N, DeLuca C, Genin P, Cisternas S, Lin R, Wainberg MA, Hiscott J: Inducible expression of IκBα repressor mutants interferes with NF-κB activity and HIV-1 replication in Jurkat T cells. J Biol Chem. 1998, 273: 7431-7440. 10.1074/jbc.273.13.7431.View ArticlePubMedGoogle Scholar
- Jordan A, Defechereux P, Verdin E: The site of HIV-1 integration in the human genome determines basal transcriptional activity and response to Tat transactivation. EMBO J. 2001, 20: 1726-1738. 10.1093/emboj/20.7.1726.PubMed CentralView ArticlePubMedGoogle Scholar
- Han Y, Lassen K, Monie D, Sedaghat AR, Shimoji S, Liu X, Pierson TC, Margolick JB, Siliciano RF, Siliciano JD: Resting CD4+ T cells from human immunodeficiency virus type 1 (HIV-1)-infected individuals carry integrated HIV-1 genomes within actively transcribed host genes. J Virol. 2004, 78: 6122-6133. 10.1128/JVI.78.12.6122-6133.2004.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang GP, Levine BL, Binder GK, Berry CC, Malani N, McGarrity G, Tebas P, June CH, Bushman FD: Analysis of lentiviral vector integration in HIV + study subjects receiving autologous infusions of gene modified CD4+ T cells. Mol Ther. 2009, 17: 844-850. 10.1038/mt.2009.16.PubMed CentralView ArticlePubMedGoogle Scholar
- Raj A, van Oudenaarden A: Nature, nurture, or chance: stochastic gene expression and its consequences. Cell. 2008, 135: 216-226. 10.1016/j.cell.2008.09.050.PubMed CentralView ArticlePubMedGoogle Scholar
- Raser JM, O'Shea EK: Noise in gene expression: origins, consequences, and control. Science. 2005, 309: 2010-2013. 10.1126/science.1105891.PubMed CentralView ArticlePubMedGoogle Scholar
- Burnett JC, Miller-Jensen K, Shah PS, Arkin AP, Schaffer DV: Control of stochastic gene expression by host factors at the HIV promoter. PLoS Pathog. 2009, 5: e1000260-10.1371/journal.ppat.1000260.PubMed CentralView ArticlePubMedGoogle Scholar
- Nelson DE, Ihekwaba AEC, Elliott M, Johnson JR, Gibney CA, Foreman BE, Nelson G, See V, Horton CA, Spiller DG, Edwards SW, McDowell HP, Unitt JF, Sullivan E, Grimley R, Benson N, Broomhead D, Kell DB, White MRH: Oscillations in NF-κB signaling control the dynamics of gene expression. Science. 2004, 306: 704-708. 10.1126/science.1099962.View ArticlePubMedGoogle Scholar
- Coiras M, Lopez-Huertas MR, Rullas JIN, Mittelbrunn M, Alcami J, Lopez-Huertas MIAR, Rullas JIN, Mittelbrunn M, Alcami JE: Basal shuttle of NFκB/IκBα alpha in resting T lymphocytes regulates HIV-1 LTR dependent expression. Retrovirology. 2007, 4: 56-10.1186/1742-4690-4-56.PubMed CentralView ArticlePubMedGoogle Scholar
- Arenzana-Seisdedos F, Turpin P, Rodriguez M, Thomas D, Hay RT, Virelizier JL, Dargemont C: Nuclear localization of IκBα promotes active transport of NF-κB from the nucleus to the cytoplasm. J Cell Sci. 1997, 3: 369-378.Google Scholar
- Duverger A, Wolschendorf F, Zhang M, Wagner F, Hatcher B, Jones J, Cron RQ, van der Sluis RM, Jeeninga RE, Berkhout B, Kutsch O: An AP-1 binding site in the enhancer/core element of the HIV-1 promoter controls the ability of HIV-1 to establish latent infection. J Virol. 2013, 87: 2264-2277. 10.1128/JVI.01594-12.PubMed CentralView ArticlePubMedGoogle Scholar
- Wolschendorf F, Bosque A, Shishido T, Duverger A, Jones J, Planelles V, Kutsch O: Kinase control prevents HIV-1 reactivation in spite of high levels of induced NF-κB activity. J Virol. 2012, 86: 4548-4558. 10.1128/JVI.06726-11.PubMed CentralView ArticlePubMedGoogle Scholar
- Duverger A, Wolschendorf F, Anderson JC, Wagner F, Bosque A, Shishido T, Jones J, Planelles V, Willey C, Cron RQ, Kutsch O: Kinase control of Latent HIV-1 Infection: PIM-1 Kinase as a Major Contributor to HIV-1 Reactivation. J Virol. 2014, 88: 364-376. 10.1128/JVI.02682-13.PubMed CentralView ArticlePubMedGoogle Scholar
- Weiss A, Wiskocil RL, Stobo JD: The role of T3 surface molecules in the activation of human T cells: a two-stimulus requirement for IL 2 production reflects events occurring at a pre-translational level. J Immunol. 1984, 133: 123-128.PubMedGoogle Scholar
- Chang LJ, Urlacher V, Iwakuma T, Cui Y, Zucali J: Efficacy and safety analyses of a recombinant human immunodeficiency virus type 1 derived vector system. Gene Ther. 1999, 6: 715-728. 10.1038/sj.gt.3300895.View ArticlePubMedGoogle Scholar
- Bernhard W, Barreto K, Saunders A, Dahabieh MS, Johnson P, Sadowski I: The Suv39H1 methyltransferase inhibitor chaetocin causes induction of integrated HIV-1 without producing a T cell response. FEBS Lett. 2011, 585: 3549-3554. 10.1016/j.febslet.2011.10.018.View ArticlePubMedGoogle Scholar
- Grupillo M, Lakomy R, Geng X, Styche A, Rudert WA, Trucco M, Fan Y: An improved intracellular staining protocol for efficient detection of nuclear proteins in YFP-expressing cells. Biotechniques. 2011, 51: 417-420.View ArticlePubMedGoogle Scholar
- Marks PA, Breslow R: Dimethyl sulfoxide to vorinostat: development of this histone deacetylase inhibitor as an anticancer drug. Nat Biotechnol. 2007, 25: 84-90. 10.1038/nbt1272.View ArticlePubMedGoogle Scholar
- Krzywinski MI, Schein JE, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA: Circos: An information aesthetic for comparative genomics. Genome Res. 2009, 19: 1639-1645. 10.1101/gr.092759.109.PubMed CentralView ArticlePubMedGoogle Scholar
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