Temporal transcriptional response to latency reversing agents identifies specific factors regulating HIV-1 viral transcriptional switch

Multiple complementary factors are involved in reversal of latent HIV-1

Comparative studies using multiple activators to reactivate HIV-1 from latency have
resulted in different levels of virus reactivation 43]–46], suggesting a role for multiple factors in virus reactivation. Here we used a well-characterized
ACH-2 T cell line based HIV-1 latency model as our model cell line to validate the
Systems Biology approach to identify the cellular factors and pathways involved in
reversal of latent HIV-1 by SAHA, prostratin and TNF-?. ACH-2 cells were treated with
different concentrations of SAHA, prostratin, or TNF-? for 18 h. Alternatively, the
cells were also activated using ?CD3/?CD28 antibody for 3 days, and virus reactivation
was measured by intracellular p24 staining (Fig. 1a). Phorbol 12-myristate 13-acetate (PMA) was used as a positive control. In the presence
of vehicle (DMSO) control alone, 13–20 % of cells are positive for p24 Gag as measured
by flow cytometry. PMA (positive control), which is a potent activator of protein
kinase C (PKC) reactivated 90–95 % of the cells in 18 h, suggesting that the ACH-2
cells have a wide dynamic range (Fig. 1a) and can serve as a sensitive model for estimating the potency of HIV-1 latency
reactivators and to elucidate the contribution of cellular factors and signaling pathways
in HIV-1 transcription regulation. Addition of clinically relevant concentration of
SAHA (0.5 ?M) reactivated 57–65 % of ACH-2 cells as detected by positive p24 Gag staining,
and increasing concentrations (5 µM) of SAHA resulted in ~95 % of them positive for
p24 Gag (Fig. 1b). Similarly we observed a dose dependent increase in the reactivation of latent
HIV-1 in ACH-2 cells, with increasing concentrations of prostratin and TNF-? as well.
At 0.2 ?M concentration, prostratin activated 32–37 %, whereas, at 2 ?M, the percentage
positive cells increased to 87–94 %. TNF-?, a potent activator of latently infected
cells exhibited similar activation levels at 10 ng/ml concentration. Interestingly,
stimulation with and anti-CD3 and anti-CD28 antibodies failed to activate significant
amount of the latent virus in ACH-2 cells (Fig. 1b). Flow cytometry analyses of surface expression of CD3, CD28 and CD2 suggested that
only less than 15 % of ACH-2 cells expressed CD3 and CD28 molecules, and less than
10 % of the cells express CD2 (Additional file 1: Fig. S1) that are required for TCR mediated activation, respectively. These results
correlate with the absence/minimal response observed in ACH-2 cells when treated with
?CD3/?CD28 antibodies.

Fig. 1. Dose dependent reversal of latent HIV-1 in ACH-2 cells by SAHA, prostratin and TNF-?.
a ACH-2 cells were treated with different reactivating agents for 18 h and p24-Gag
was estimated by intracellular staining. A representative figure denoting intracellular
HIV-1 p24 Gag detected by flow cytometry (N = 4) is shown here. Viable cells were
gated based on side and forward scatter dot plot and the p24 positive cells were detected by anti-p24-FITC antibody b The percentage of HIV-1 Gag-p24 positive cells from multiple experiments (N = 4)
are shown, to demonstrate the dose dependent response of ACH-2 to different activating
reagents. Error bars represent standard deviation. c Effect of combination of reactivating agents. For comparison of results across samples
from multiple experiments, HIV reactivation observed upon PMA (100 nM) treatment minus
background (DMSO) was considered as 100 %. Error bars represent standard deviation (N = 4). *p  0.05 by student t test

To assess whether these activators could have an additive effect in reversal of latent
virus, ACH-2 cells were treated with a combination of submaximal concentrations of
these compounds to reactivate latent virus (Fig. 1c). Results indicate that combining SAHA and prostratin resulted in an increase in
percentage of cells expressing p24 Gag. Treatment with SAHA or prostratin alone, showed
57–65 and 32–37 % of p24 positive cells, respectively, whereas a combination of these
two activators resulted in 87–92 % of p24 positive cells. These results suggest an
additive effect, implying that independent cellular factors are involved in SAHA and
prostratin mediated latent HIV-1 reactivation and that these factors could complement
each other to potentiate the effect. A similar increase in percentage of p24 Gag positive
cells was observed when SAHA was combined with TNF-?, an increase from 23–28 to 83–87 %
p24 positive cells. Similarly, when prostratin and TNF-? were combined together, the
p24 positive cells increased to 46-62 %, a lesser extent than the additive effect,
suggesting that prostratin and TNF-? may share few common cellular factors and pathways
in virus reactivation. Combining SAHA, prostratin and TNF-? together resulted in maximum
reactivation (93–97 % p24 positive cells), similar to that observed in PMA treated
positive control. Collectively, these results suggest that HIV-1 reactivation from
latency is mediated by multiple pathways involving different cellular factors that
are complementary as well as common depending on the context of the activator used.

Kinetics of latent HIV-1 reactivation in ACH-2 cells indicates that transcriptional
reactivation of viral RNA is an early event

The kinetics of latent viral reactivation were assessed to understand the timing and
sequence of events leading to the transcriptional switch from latency to productive
infection and virus release. ACH-2 cells were treated with SAHA, prostratin or TNF-?
and synthesis of viral transcripts and virus production/release was measured at 2,
4, 6, 8, 10, 12 and 18 h post activation (Fig. 2b–d). Gag (p24) positive cells were assessed by p24 intracellular staining and flow
cytometry (Fig. 2a). Intracellular staining for p24 Gag indicates that ACH-2 cells treated with SAHA
resulted in a shift from background level of ~20 to ~80 % 10 h post treatment and
reached the maximum of 95 % with associated increase in MFI, suggesting that SAHA
induced viral Gag production required 10 h. In case of prostratin, a less dramatic
shift in cells expressing p24 Gag was seen in 6–8 h, whereas, TNF-? exhibited a slow
increase at 6 h post stimulation and reached the maximum at 12 h. Together, these
results support that SAHA, prostratin and TNF-? might utilize diverse mechanisms,
with varying kinetics leading to reactivation of HIV-1 in ACH-2 cells.

Fig. 2. Time kinetics of p24 gag protein expression, synthesis of viral transcripts and virus
release. a HIV-1 Gag positive cells were detected by intracellular staining for p24-Gag protein
at indicated time points following treatment of ACH-2 cells with SAHA, prostratin
or TNF-?. b Multiply spliced HIV-1 RNA transcripts, and c Unspliced HIV-1 (Gag-pol) RNA transcripts were detected by Real time PCR at indicated
time points. The average fold changes in RNA transcripts over time zero observed in
two independent experiments done in triplicates is shown. RPLPO (large ribosomal protein)
was included for normalization. d The amount of virus released in the supernatant was detected by p24 ELISA and the
p24 value at time zero was subtracted from individual time points to calculate the
new virus released over time. The average of two independent experiments done in triplicate
is shown

To understand the time kinetics required for HIV-1 transcriptional reactivation in
these cells, multiply spliced viral transcripts and unspliced gag RNA was assessed
over time (Fig. 2b). Total RNA was extracted from part of the cells collected at the same time points
to assess MS-RNA and unspliced Gag RNA (Fig. 2b, c) and virus released in the supernatant was measured by p24 ELISA (Fig. 2d), as described in materials and methods. Results indicate that prostratin initiated
virus transcription within 2–4 h and that the peak expression of MS-RNA was reached
at 6 h. Six hours post treatment there was a gradual decrease in the multiply spliced
RNA and a corresponding increase in full-length gag transcripts occurred (Fig. 2b, c). The lesser fold change in cellular gag transcripts can be attributed to the
packaging of full length gag transcripts in virus particles and their release in the
supernatant, as measured by an increase in p24 virus Gag (Fig. 2d), resulting in sinusoidal curves of gag transcripts. In case of SAHA, we observed
an increase in multiply spliced viral transcripts around 4–6 h, which was to a lesser
extent than the fold change observed in prostratin. In case of gag transcripts, the
increase was also to a lesser extent in comparison to prostratin occurring around
10 h. This correlates with the detection of p24 Gag by flow cytometry. The release
of virus particles in SAHA as measured by an increase in p24 Gag protein in the supernatant
was observed around 10 h. TNF-? resulted in an increase in multiply spliced transcripts
by 6 h though the increase was very modest in multiple experiments; similarly, the
increase in gag transcripts was observed around 10 h. Virus released in supernatant
in TNF-? treatment of ACH-2 cells was similar to SAHA, where an increase in p24 was
detected by 10 h and continued to increase over time. These results suggest that HIV-1
reactivation at transcription level in ACH-2 cells is an early event occurring within
the initial 4–6 h post reactivation. Additionally, we noted that reactivation of latent
virus in ACH-2 cells is a sequential process similar to acute HIV-1 infection in T
cells, where the initial transcription initiation is followed by transcription of
multiply spliced viral RNA transcripts, and a subsequent shift to full length gag
transcripts and the final stage of viral RNA packaging and virus release.

Transcriptome analysis to identify specific factors involved in virus reactivation

In an effort to identify the cellular factors and signaling pathways that are modulated
following treatment of ACH-2 cells with different reactivating agents, transcriptome
analyses were performed at multiple time points following treatment of cells with
reactivating agents. Results indicate that multiple cellular transcripts were altered
over time (Fig. 3a; Table 1). Interestingly, significant changes in cellular transcripts were observed within
2 h post treatment, with the largest changes induced by SAHA—264 host cellular gene
transcripts were identified as significantly upregulated and 88 transcripts significantly
downregulated. Prostratin, significantly increased the transcripts for 14 genes while
none of the transcripts were significantly decreased by 2 h. For TNF-?, 83 gene transcripts
were significantly upregulated and 46 cellular transcripts were significantly downregulated
at this time point. Evaluation of cellular transcriptome data over time indicates
that, the changes in cellular transcripts are the greatest with SAHA and relatively
lesser with prostratin and least with TNF-?. Though the number of transcripts that
were altered in either direction (up/down regulated) were comparable across all treatments,
for SAHA the differential score for transcripts (a measure of statistical significance
of the change and fold change in transcript levels) was symmetrically distributed
in both the positive and negative direction, whereas, with prostratin and TNF-?, there
were more cellular transcripts that were significantly upregulated than those that
were down regulated.

Fig. 3. Analysis of whole genome transcriptome data. a Time kinetics of whole genome transcriptome analysis in ACH-2 cells treated with
SAHA, prostratin and TNF- ?. Genome Studio was used to analyze transcriptome data
obtained from Illumina HT-12 V4 array bead chips for ACH-2 cells treated with different
reactivating agents at indicated time points. A differential score of ±13 (dotted line) corresponding to a p  0.05 was used to identify the significant genes. Each data
point corresponds to a differential score of an individual transcript calculated from
results obtained in two independent experiments. b Dysregulated genes (p-value 0.05 and fold change ±2) from GenomeStudio transcriptome
analysis were analyzed using MSigDB version 4.0 (http://www.broadinstitute.org/gsea/msigdb/annotate.jsp). The stacked bar graph represents the number of genes in each functional category for different time points
in ACH-2 cells treated with SAHA, prostratin or TNF-?

Table 1. Differentially regulated cellular transcripts in ACH-2 at indicated time points following
treatment with SAHA, prostratin or TNF-?

A single copy of HIV-1 in ACH-2 is integrated in the intron between exons 5 and 6
in NT5C3A, variant 1 on chromosome 7 47]. Analysis of transcriptional changes in cellular genes around this region clearly
identifies that NT5C3 as the only host gene that is consistently induced with the
reactivation of the virus in ACH-2 cells to 7.4- 18.5 fold. None of the closely associated
gene transcripts namely RP9, BBS9, FKBP9 or RP9P changes more than 1.4 to 0.9 fold
with reversal of HIV-1 latency (Additional file 2: Table S1, Additional file 3: Fig. S2A). Time kinetics of changes in NT5C3 transcripts identifies that transcripts
are induced as early as 2 h following treatment of ACH-2 cells with prostratin and
TNF-?, and the levels continue to increase over time. With SAHA an increase in NT5C3
transcript is noticed at 4 h and this increase remains progressive and sustained,
finally resulting in highest fold change of 18.5 in comparison to prostratin (12.6
fold change) and TNF-? (7.4 fold change) (Additional file 3: Fig. S2B). Induction of NT5C3 transcripts as detected by RNA hybridization in Illumina
HT-12 chip was also detected at the protein level by western blot using NT5C3 specific
antibody (Additional file 3: Fig. S2C). It is observed that the NT5C3 protein is not present in the media control,
whereas detected upon treatment with prostratin, SAHA or TNF-?. Comparison of changes
in protein level over time with the parent cell line, A3.01 cells, indicates that
NT5C3 is expressed in A3.01 cells even in the absence of latency reactivating agents,
Additional file 3: Fig. S2C, lane 5, suggests that, the integration of HIV-1 virus and its latent state
in ACH-2 cells is associated with suppression of host cellular gene, NT5C3. With the
reactivation of latent HIV-1 with either prostratin, SAHA or TNF-? is associated with
reversal of this inhibition of NT5C3 expression.

Gene expression data sets were assessed to identify the pathways or major functions
that are targeted by the reactivators using GSEA as described 37]. Results presented in Fig. 3b focused on time points corresponding to the expression of viral transcripts and
Gag synthesis/release indicate that the differentially expressed genes prior and/or
during the time point represent primarily genes belong to transcription factors, protein
kinases, cell differentiation markers and cancer related genes as well as cytokines
and growth factors.

Transcriptional regulators and pathways involved in the reactivation of HIV-1

Next, to identify the upstream regulators including the transcription factors and
their associated signaling molecules, the time series transcriptome data corresponding
to each of these treatments were used to reconstruct dynamic signaling and regulatory
networks using SDREM (Fig. 4a–f). Based on our observations from viral transcriptome analysis, the reactivation
of latent HIV-1 in ACH-2 cells was initiated as early as 4–6 h following treatment
with SAHA, prostratin or TNF-?. Hence for SDREM analysis, the transcriptome data were
divided into two sets—(1) initial reactivation phase, corresponding to time points
before the expression of viral transcripts; and (2) virus production phase, associated
with time points post viral transcripts synthesis. The transcriptome changes in the
initial reactivation phase are key for switching on the latent HIV-1 transcription
and are the consequence of reactivating agents, while changes in the virus production
phase are due to the combined effects of reactivating agents and viral products. Results
from the SDREM analyses corresponding to initial reactivation phase (6 h post treatment)
with SAHA suggest that TFs–JUN, NFATC1-4, HOXA4, TCF7L2, SRY, CEBPE, and FOXO4 are
responsible for changes observed in the cellular transcripts in the initial 2 h. Similarly
FOXO4, MTF1, NR3C1, NFE2L1, FOXO1, FOXL1, TBP, ATF2 and SRY were predicted to be responsible
for changes observed in the cellular transcripts observed between two and 4 h. No
additional changes in TFs were observed at 4–6 h. Identification of regulatory networks
using the transcriptome data from ACH-2 cells activated with SAHA is presented in
Fig. 4a. As SAHA is well characterized as an inhibitor of histone deacetylase enzyme (HDAC),
we included all isoforms of HDACs as the source molecules for the SDREM analysis.
The downstream nodes representing the signaling and regulatory components constructed
by SDREM are represented in Fig. 4b; Table 2. This includes the top 30 factors that are predicted to be involved in signal transduction
mediated downstream events of HDACs following treatment of ACH-2 cells with SAHA.

Fig. 4. Reversal of latent HIV-1 model—SDREM analysis. Whole genome transcriptome data in
ACH-2 cells at multiple time points were analyzed by SDREM for a, b SAHA; c, d Prostratin; e, f TNF-?. a, c, d represent the regulatory part of the model, where each path represents a collection
of gene expression profiles; x-axis denotes the time points for each treatment when
the gene expression was measured and the y-axis shows log
₂
fold change in expression. TFs that are predicted to control the split are included
at indicated time points (TFs are included only the first time they are active along
a regulatory path). TFs in red or blue indicate that transcripts of these TFs were also observed to be significantly increased
or decreased respectively. The size of the node indicates the relative number of genes
regulated. b, d, f represent oriented interaction network starting from upstream proteins (source; red), predicted signaling proteins (blue) and active TFs (green). The boldness of the edge between two nodes, indicate the number of pathways between the two

Table 2. List of top 30 cellular factors involved in SAHA, prostratin or TNF-? induced latent
HIV-1 reactivation in ACH-2 cells

Similar analyses were performed for prostratin induced reactivation in ACH-2 cells
(Fig. 4c, d). Results indicate that TFs—HNF1A, POU2F1, SPI1, ETS1, LEF1, POU2F1, CEBP, SMAD,
TCF7L2, RELA, GATA, STATs, BRCA1, ZEB1, FOXC1 and MZF1 are regulating the transcriptional
changes observed at 4 h post prostratin treatment (Fig. 4c). It is also noted that the expression levels of ETS1 and LEF1 are increased and
the expression of GATA3, STAT5A and CEBPB is reduced at this time point. As prostratin
is an activator of Protein Kinase C (PKC), PKC was included as a source and the regulatory
pathways and factors that were predicted to mediate the changes observed in the regulatory
models are identified in Fig. 4d. The top 30 factors predicted by SDREM as critical for prostratin effects in ACH-2
cells are also presented in Table 2. Results indicate that MAPK1, SMAD2/4, SRC, MAX, GSK3B, and LEF1 are identified as
unique factors involved in prostratin induced signaling and predict PRKCQ as the main
driving factor responsible for the changes observed in cellular transcripts during
early time duration of 0–4 h following prostratin treatment.

Unlike prostratin, TNF-? exhibited delayed reactivation kinetics in ACH-2 cells. SDREM
analysis of time kinetic data obtained from TNF-? treated cells presented in Fig. 4e, suggests that TFs- VDR, RUNX1, SP1 and SP3 are contributing to the differential
regulation of cellular transcripts observed at 2 h. TFs related to NF-?B—NF-?B1, NF-?B2,
RELA; JAK-STAT related factors—STAT4, STAT5A, STAT6; CEBPA, CEBPE, GATA3, CUX1, ETV4,
ETS1, FKBP4, CD40, ZEB1 are identified to be responsible for changes observed in the
cellular transcripts at 2 and 4 h. The regulatory pathways and factors likely responsible
for changes in TFs originating from TNFRSF1A and TNFRSF1B are indicated in Fig. 4f and Table 2. TFs, Jun, Myc, RelA, STAT1/3, TP53, NR3C1, CEBPB, and CREBBP, which are known to
bind to HIV-1 LTR were present in all three treatments. Together these results suggest
that specific transcription factors and their associated cellular signaling pathways
are involved in HIV-1 reactivation in the ACH-2 cell line. It should be noted that
SDREM identifies the upstream regulatory factors as the most probable factors responsible
for changes observed in the transcripts at the identified time point and hence changes
(activation/inhibition) in these upstream factors occur prior to the time points when
the changes in transcripts were evaluated.

Validating the predicted cellular factors and TFs in latent HIV-1 reactivation in
ACH-2 cells by specific inhibitors

SDREM analysis has identified both common and unique TFs as the key regulators of
cellular transcripts in ACH-2 cells, when treated with SAHA, prostratin and TNF-?.
Based on these predictions we performed follow up experiments to validate the role
of predicted factors in reactivation of latent HIV-1 in ACH-2 cells. First, we evaluated
the ability of I?K2 inhibitor V (NF-?B inhibitor); Tacrolimus (FK506), Cyclosporin
A (CsA)—NFAT inhibitors; SP600125 (JNK inhibitor); SB203580 (p38 inhibitor); U0126
and AZD6244—ERK1/2 inhibitor; WP1066 (JAK-STAT inhibitor); and Rottlerin (PKC inhibitor)
to inhibit SAHA, prostratin and TNF-? mediated reactivation of HIV-1. ACH-2 cells
were pretreated with inhibitors for 4 h and activated with SAHA, prostratin or TNF-?
and the p24 positive cells were assessed by flow cytometry (Fig. 5a). It is observed that both Rottlerin and SP600125 completely abrogate SAHA induced
virus reactivation suggesting that the PKC?JNK?JUN/ATF pathway with related downstream
TFs, has a major role in SAHA induced HIV-1 reactivation in these cells, as predicted
by our model (Fig. 4a, b; Table 1). Interestingly, p38 inhibitor, SB203580 further increased the percentage of reactivated
HIV-1 virus (Fig. 5a), suggesting that SB203580 reactivates latent HIV-1 using cellular pathways much
different from those used by SAHA.

Fig. 5. Specific cellular signaling pathways with associated host cellular factors are involved
in reactivation of latent HIV-1 in ACH-2 cells. a ACH-2 cells were pretreated with specific inhibitors I?K2 inhibitor V (50 µM), FK506
(10 µM), Cyclosporin A (10 µM), SP600125 (50 µM), SB203580 (50 µM), U0126 (50 µM),
AZD6244 (10 µM), WP1066 (5 µM), Rottlerin (50 µM), or vehicle control (DMSO), and
4 h later activated with SAHA (1 µM, white bars), prostratin (1 µM, grey bars) or TNF-? (0.1 ng/ml, bars with diagonal line upwards). HIV reactivation was estimated at 12 h following reactivation, by intracellular
p24 Gag staining by flow cytometry. For comparison of results across samples from
multiple experiments, HIV-1 reactivation observed in vehicle control pretreatment
was considered as 100 % and the background (no reactivating agent) as 0 %. Error bars represent standard deviation (N = 3), *p  0.05. b At the end of 16 h following addition of inhibitors, the live cells were evaluated
by Trypan blue staining. The percentage of viable cells was calculated by subtracting
the dead cells from total cells divided by total cell count. c The amount of p24-Gag positive cells was estimated by intracellular p24-Gag staining
and flow cytometry. Error bars represent standard deviation (N = 3), *p  0.05

In the case of prostratin, IKK2 inhibitor V, SP600125, U0126, AZD6244 and Rottlerin
have a significant effect in preventing virus reactivation, suggesting that prostratin
uses multiple alternate pathways involving NF-?B, JNK, ERK1/2 and PKC to induce HIV-1
reactivation (Fig. 4c, d). These results correlate with the SDREM prediction for prostratin suggesting
a major role for ERK1/2. Similarly, SDREM analysis was accurate in identifying factors
involved in TNF-? induced HIV-1 reactivation, which involves NF-?B, JNK and PKC, but
not NFAT. p38 inhibitor did not increase prostratin or TNF-? mediated HIV-1 reactivation.
The percentage of cells expressing p24 was significantly below the background expression
observed with DMSO, suggesting that the JAK ? STAT pathway is involved in regulating
HIV-1 LTR activity. Irrespective of reactivation agents, STATs were also identified
as a common factor in SDREM analysis for all these activating agents. Furthermore,
it is important to note that no cellular toxicity was observed in these cells with
these inhibitors, with the exception of WP1066 and Rottlerin, which showed 40–60 %
reduction in cell viability (Fig. 5b); therefore only live cells were gated in flow cytometry based assessment of p24
positive cells. Additionally, dose dependent inhibitory effects can be observed with
WP1066 and Rottlerin, and consistent inhibition was noted at lower concentration,
when the associated cellular toxicity was minimal (Additional file 4: Fig. S3).

ACH-2 cells were incubated with the inhibitors in the absence of reactivating agents,
to evaluate whether these inhibitors could potentially modulate HIV-1 transcription.
Results indicate that only the p38 inhibitor–SB203580 and JAK-STAT inhibitor–WP1066
significantly altered the HIV-1 transcription (Fig. 5c). WP1066 reduces the basal level HIV-1 transcription in ACH-2 cells suggesting a
role for STAT in HIV-1 transcription. SB203580 increased HIV-1 transcription from
15 to 42 % at 16 h post treatment, suggesting that p38 inhibitor SB203580 could directly
reactivate latent HIV-1 in ACH-2 cells.

Validation of signaling pathways and regulatory networks in J-Lat latent cells

To understand whether the TFs identified in ACH-2 cells are specific to these cells
or commonly shared in other HIV-1 latent cells, we validated these TFs in other HIV-1
latent cell lines, J-Lat cells that were developed by Jordan et al. 29]. First, the dose of reactivating agents that resulted in maximum response in ACH-2
cells was tested in J-Lat cells, along with PHA-M. PMA was included as a positive
control. Results indicate that the cell lines FL10.6, TGA1 and TGA2 had a wide dynamic
range with minor variations in their response to different reactivators (Fig. 6a). Cell line FL10.6 is highly reactive to prostratin, TNF-? and PMA reaching 60–80 %
reactivation in comparison to SAHA and PHA-M (35–45 %); whereas TGA1 cells showed
significant response (85–90 %) to prostratin and PMA but not to SAHA, TNF-? and PHA-M
(35–60 %). The cell line TGA2 showed minimum activation with PHA-M (14–18 %) but the
response to SAHA, prostratin, TNF-? and PMA were comparable to each other (~43–72 %).
As previously reported these cell lines had different reactivity to different reactivators
28].

Fig. 6. Host signaling pathways and regulatory cellular factors involved in reactivation of
HIV-1 transcription in J-Lat cells. a Eight different clones of J-Lat cells were treated with different reactivating agents
or DMSO for 18 h and the percentage of GFP positive cells was estimated by flow cytometry.
Error bars represent standard deviation (N = 3). Cell lines b J-Lat FL10.6, c J-Lat TGA1, and d J-Lat TGA2 cells were pretreated with specific inhibitors I?K2 inhibitor V (50 µM),
FK506 (10 µM), Cyclosporin A (10 µM), SP600125 (50 µM), SB203580 (50 µM), U0126 (50 µM),
AZD6244 (10 µM), WP1066 (5 µM), Rottlerin (50 µM), or vehicle control (DMSO), and
4 h later activated with SAHA (1 µM, white bars), prostratin (1 µM, grey bars) or TNF-? (0.1 ng/ml, bars with diagonal line upwards) or PHA-M (5 mg/ml, bars with spheres). HIV-1 reactivation was estimated at 12 h following reactivation, by
evaluating for GFP positive cells by flow cytometry. For comparison of results across
samples from multiple experiments, HIV-1 reactivation observed in vehicle control
(DMSO) pretreatment was considered as 100 % and the background (no reactivating agent)
as 0 %. Error bars represent standard deviation (N = 3), *p  0.05

Next, we validated the specific TFs and regulatory networks in J-Lat based latency
cell lines using inhibitors as described in our methods. Results indicate that pathways
are similar in J-Lat and ACH-2 cells, with minor differences (Fig. 6b). JAK-STAT inhibitor, WP1066 and PKC inhibitor—Rottlerin have major effects in inhibiting
reactivation of latent HIV-1 in J-Lat clones (close to background GFP level), suggesting
that these signaling pathways are critical for HIV-1 LTR activity (Additional file
5: Fig. S4). ERK1/2 inhibitor U0126 and AZD6244 have a significant effect in inhibiting
HIV-1 reactivation specifically in prostratin (~74–82 %), suggesting that ERK1/2 may
be the major pathway involved downstream of PKC in prostratin mediated latent HIV-1
reactivation in J-Lat clones. Similarly NF-?B is the critical factor for TNF-? mediated
reactivation in J-Lat cells (57–47 % inhibition with I?K2 inhibitor V in J-Lat FL10.6
cells, 67–83 % inhibition in J-Lat TGA1 cells and 90–94 % inhibition in J-Lat TGA2
cell line). However, JNK exhibits a central role in activating HIV-1 LTR with SAHA,
prostratin or TNF-? in J-Lat cells. Interestingly, it is also noted that I?K2 inhibitor
V inhibited SAHA mediated reactivation in J-Lat cells suggesting a role for NF-?B
(~62–83 %). This correlates with the relatively reduced ability of SP600125 to inhibit
the SAHA mediated reactivation in comparison to reactivation observed ACH-2 cells
(60–70 % inhibition in J-Lat cells versus 85–95 % inhibition in ACH-2 cells). This
suggests that when JNK mediated signaling is blocked in J-Lat, NF-?B related pathways
could still lead to reactivation of latent HIV-1.

Time dependent inhibition of signaling pathways and regulatory networks in J-Lat cells
identify unique role for JAK- STAT early in latent virus reactivation

The above results identified the requirement of specific cellular signaling pathways
and regulatory network in J-Lat cells upon activation by various reactivating agents.
To identify the relationship between these specific regulatory networks and their
role in latent HIV reactivation, we blocked the specific regulatory network following
reactivation of latent virus in a time dependent manner. J-Lat clone FL 10.6 and TGA1
were used for these experiments where the cells were stimulated with SAHA, prostratin,
TNF-? or PHA-M followed by blocking of specific signaling network by using small molecule
inhibitors at multiple time points of 2 h intervals. Cells treated with inhibitor
4 h prior to stimulation was included as controls, and cells stimulated with latency
reactivating agents with no inhibitor were included as positive control and was normalized
to 100 %. Results indicate that including the inhibitors at 4 h prior to stimulation
identified specific pathways involved in latent virus reactivation as in earlier experiments
(Fig. 6b, c), where we noticed 95 % inhibition with STAT inhibitor, WP1066 in both the tested
J–Lat cell lines, with all the tested latency reactivating agents (Fig. 7a–h, unfilled open bars). Whereas, when WP1066 was added 2 h post stimulation, it
can be noticed that the inhibition of virus reactivation was reduced to 40–50 % in
both SAHA and prostratin. Including WP1066 after 4 h or later post treatment with
SAHA or prostratin resulted in no effect on HIV-1 latency reversal (Fig. 7a, b, e, f). In case of TNF-? and PHA-M, the ability of WP1066 to block virus reactivation
is lost by 4 and 6 h, respectively (Fig. 7c, d, g, h).

Fig. 7. Time dependent inhibition of cellular signaling pathways in J-Lat cells. J-Lat FL10.6
(a–d) and J-Lat TGA1 (e–h) cells were activated with SAHA (1 µM), prostratin (2 µM) or TNF-? (0.1 ng/ml) or
PHA-M (5 ?g/ml) and specific inhibitors targeting STAT, ERK1/2, PKC, NF-?B and JNK
were included at multiple time points, either 4 h prior to activation (white bars) or at every 2 h intervals till 8 h post activation (2 h: bars with diagonal downward lines; 4 h: bars with diagonal crossing lines; 6h: bars with white and black checkered pattern; 8 h: black bars). HIV reactivation was estimated at 18 h following reactivation, by evaluating for
GFP positive cells by flow cytometry. For comparison of results across samples from
multiple experiments, HIV-1 reactivation observed in vehicle control (DMSO) pretreatment
was considered as 100 % and the background (no reactivating agent) as 0 %. Error bars represent standard deviation (N = 3)

With ERK1/2 inhibitor, we observed a more gradual loss of inhibition over time (Fig. 7b, d, f, h) when prostratin and PHA-M are included to reactivated the virus. The inhibition
is 95 % when AZD 6244 was included 4 h prior to activation, this inhibition gradually
decreased to 40–60 % at 8 h post stimulation. JNK inhibitor, SP600125, also demonstrated
consistent inhibition around 40–60 % with both the cell lines with all the tested
reactivating agents but for prostratin in J-Lat TGA1cells (Fig. 7f), when included 4 h prior to stimulation. This inhibitory effect reduced progressively
with time over to 40 % especially in J-Lat cell line FL10.6 (Fig. 7a–d) but did not change drastically till 8 h in J-Lat cell line TGA1. Rottlerin showed
consistent inhibition (~80–90 %) over time till 8 h in both the cell lines when stimulated
with SAHA, TNF-? and PHA-M, but with prostratin, the inhibitory effect of rottlerin
progressively reduces to ~20 % at 8 h post stimulation (Fig. 7b, f). NF-?B inhibitor was able to consistently inhibit virus reactivation (~60–90 %)
that remains stable over time in both the cells with all the tested reactivators but
for in J-Lat cells TGA1 with prostratin (Fig. 7f). These results suggest that JAK-STAT has a role in early stage of reactivation,
in the initial 2–4 h, and inhibiting JAK-STAT pathway after this interval does not
inhibit latent virus reactivation with all the tested activators, though with delayed
kinetics for TNF-? and PHA-M. And this early role of JAK-STAT pathway is essential
for viral reactivation which cannot be complemented by other signal or factors. With
other regulatory factors like NF-?B, PKC, JNK and ERK1/2, the activation of these
factors are essential at all-time points for effective reactivation, though at later
time points, the inhibition of these activated factors can complement each other,
which leads to gradual loss of inhibitory activity.

Role of signaling pathways and regulatory networks in latent virus reactivation in
primary T cells

Next we evaluated the role of identified signaling pathways and regulatory factors
and the relevance of the two-phase reactivation process in primary resting CD4
⁺
T cells. A total resting CD4
⁺
T cell based HIV latency model was used to study the role of specific signaling pathways
30], and ?CD3/?CD28 or prostratin were used to reactivate latent virus. Small molecule
inhibitors targeting ERK1/2, NF-?B, JNK, STAT and p38 were included either 4 h prior
to stimulation or 4 h post stimulation. The concentration of inhibitor, which induced
minimal cytotoxicity (Additional file 6: Fig. S5) was included and were tested in triplicates in three independent donors
(Fig. 8a–d). Results confirm a consistent critical role for JAK-STAT and NF-?B with both
?CD3/?CD28 and prostratin in all the three donors, where we observed a consistent
inhibition of more than 80 %. Overall, inhibition of virus reactivation by JNK inhibitor,
SP600125, or ERK1/2 inhibitor AZD6244, or P38 inhibitor, SB203580 were not statistically
significant in resting T cells; however, JNK inhibitor, SP600125, inhibited virus
reactivation by 40–55 % in two of the three donors tested with both ?CD3/?CD28 and
prostratin. Similarly ERK1/2 inhibitor, AZD6244 had inconsistent effect where ~55–60 %
inhibition was observed in one donor with both the tested reactivators. Inhibiting
specific pathways either 4 h prior to or after addition of reactivating agents did
not consistently alter the inhibition of virus reactivation. WP1066 and I?K2 inhibitor
V consistently inhibited virus reactivation 80 % in all the tested donors (Fig. 8a–d), suggesting that JAK-STAT, NF-?B are required also at 4 h following stimulation
with prostratin or ?CD3/?CD28 for latent virus reactivation in primary T cells.

Fig. 8. Role of specific cellular signaling pathway in reactivation of HIV-1 primary resting
CD4
⁺
T cells. HIV-1 latency reversal was measured by quantification of viral RNA in the
culture supernatant 7 days post-stimulation with either ?CD3/?CD28 (a, c) or Prostratin (b, d). Latently infected resting CD4
⁺
T cells were treated with one of five pathway inhibitors: ERK1/2, NF-?B, JNK, STAT
and p38, for (a, b) 4 h before stimulation or (c, d) 4 h post stimulation. Data are normalized to virus production following stimulation
with (a, c) CD3/?CD28 only or (b, d) prostratin only from three independent experiments performed in duplicate are included.
P values were determined using a paired t test. Error bars represent the standard error of the mean. *p  0.05, **p  0.005, ***p  0.0005