Extensive proteomic and transcriptomic changes quench the … · 2020-06-29 · 28 population...

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4 Extensive proteomic and transcriptomic changes quench the TCR/CD3

5 activation signal of latently HIV-1 infected T cells

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9 Eric Carlin1, Braxton Greer1, Alexandra Duverger1, Frederic Wagner1, David Moylan1, Alexander Dalecki1,

10 Shekwonya Samuel1, Mildred Perez1, Kelsey Lowman1, Steffanie Sabbaj1, Olaf Kutsch1*

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12 1 Department of Medicine, Division of Infectious disease, University of Alabama at Birmingham, Birmingham,

13 Alabama

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15 *Corresponding author

16 Email: [email protected]

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.CC-BY 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted June 30, 2020. . https://doi.org/10.1101/2020.06.29.177394doi: bioRxiv preprint

https://doi.org/10.1101/2020.06.29.177394

http://creativecommons.org/licenses/by/4.0/

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22 ABSTRACT

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24 Although the ability of HIV-1 to reside in a latent state in CD4+ T cells constitutes a critical hurdle to a

25 curative therapy, the biomolecular mechanisms by which latent HIV-1 infection is established and maintained

26 are only partially understood. Ex vivo studies have shown that T cell receptor/CD3 stimulation only triggered

27 HIV-1 reactivation in a fraction of the latently infected CD4+ T cell reservoir, suggesting that parts of the T cell

28 population hosting latent HIV-1 infection events are altered to be TCR/CD3-activation-inert. We provide

29 experimental evidence that HIV-1 infection of primary T cells and T cell lines indeed generates a substantial

30 amount of TCR/CD3 activation-inert latently infected T cells. HIV-1 induced host cell TCR/CD3 inertness is

31 thus a conserved mechanism that contributes to the stability of latent HIV-1 infection. Proteomic and genome-

32 wide RNA-level analysis comparing CD3-responsive and CD3-inert latently HIV-1 infected T cells, followed

33 by software-based integration of the data into protein-protein interaction networks (PINs) suggested two

34 phenomena to govern CD3-inertness: (i) the presence of extensive transcriptomic noise that affected the

35 efficacy of CD3 signaling and (ii) defined changes to specific signaling pathways. Validation experiments

36 demonstrated that compounds known to increase transcriptomic noise further diminished the ability of

37 TCR/CD3 stimulation to trigger HIV-1 reactivation. Conversely, targeting specific central nodes in the

38 generated PINs such as STAT3 improved the ability of TCR/CD3 activation to trigger HIV-1 reactivation in T

39 cell lines and primary T cells. The data emphasize that latent HIV-1 infection is largely the result of extensive,

40 stable biomolecular changes to the signaling network of the host T cells harboring latent HIV-1 infection

41 events. In extension, the data imply that therapeutic restoration of host cell TCR/CD3 responsiveness could

42 enable gradual reservoir depletion without the need for therapeutic activators, driven by cognate antigen

43 recognition.


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44 AUTHOR SUMMARY

45 A curative therapy for HIV-1 infection will at least require the eradication of a small pool of CD4+ helper T

46 cells in which the virus can persist in a latent state, even after years of successful antiretroviral therapy. It has

47 been assumed that activation of these viral reservoir T cells will also reactivate the latent virus, which is a

48 prerequisite for the destruction of these cells. Remarkably, this is not the case and following application of

49 even the most potent stimuli that activate normal T cells through their T cell receptor, a large portion of the

50 latent virus pool remains in a dormant state. Herein we demonstrate that a large part of latent HIV-1 infection

51 events reside in T cells that have been rendered activation inert by the actual infection event. We provide a

52 systemwide, biomolecular description of the changes that render latently HIV-1 infected T cells activation inert

53 and using this description, devise pharmacologic interference strategies that render initially activation inert T

54 cells responsive to stimulation. This in turn allows for efficient triggering of HIV-1 reactivation in a large part

55 of the latently HIV-1 infected T cell reservoir.

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57 INTRODUCTION

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59 Antiretroviral therapy (ART) efficiently suppresses HIV-1 replication below detection levels of

60 diagnostic assays, but ART does not eliminate latent viral reservoirs, enabling HIV-1 to persist for the lifetime

61 of a patient and rebound whenever ART is interrupted [1]. The most comprehensive evidence for viral

62 persistence has been presented for latent HIV-1 infection events residing in long-lived, resting CD4 memory

63 T cells [2-4]. Intuitively, this would explain the stability of the latent HIV-1 reservoir, as T cell memory can

64 persist for the life-time of an individual. However, while immunological memory can persist for a life-time,

65 individual memory T cells are relatively short lived. The half-life of individual CD4+ central memory T cells

66 (TCM cells) that are thought to serve as host cells of latent HIV-1 infection events ranges between 20 and ~100

67 days and is generally shorter in HIV patients than in healthy individuals, with most studies suggesting a half-

68 life 1/2 <50 days [5-8]. With an assumed half-life of 1/2 = 50 days and an initial reservoir consisting of 1x106

69 latently HIV-1 infected CD4+ TCM cells, it should take less than three years after the onset of ART for the last

70 latently infected TCM cell to disappear. This is obviously not the case and evidence has been provided that

71 preferential or homeostatic proliferation of latently HIV-1 infected T cells in the absence of reactivation can


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72 contribute to the stability of the latent reservoir [9, 10]. While homeostatic T cell proliferation must contribute

73 to the stability of the latent HIV-1 reservoir, it is only one contributor to lifelong immunological memory. The

74 second component, repeated exposure to cognate antigen and subsequent re-expansion of the specific

75 memory T cells are also crucial components of lifelong memory maintenance. How the latent HIV-1 infection

76 pool in the memory CD4+ T cell population remains stable despite the expected and likely required exposure

77 of latently HIV-1 infected T cells to their cognate antigen, and the resulting potent biological activation of the

78 host T cells, has not been detailed. Over time, encounters with cognate antigen should cause a continuous

79 contraction of the latent HIV-1 reservoir [11]. The absence of a measurable reservoir decay despite the

80 expected continuous encounter of cognate-antigen and subsequent T cell activation could be explained by

81 the idea that memory T cells hosting latent HIV-1 infection events have been altered to exhibit an activation-

82 inert phenotype. TCR/CD3 activation-inertness would explain the inability of early therapeutic interventions

83 (e.g. IL-2 or anti-CD3 mAb OKT3) to trigger a meaningful decrease in the size of the latent HIV-1 reservoir

84 [12-14]. An activation-inert host cell phenotype would also explain findings that a significant part of the latent

85 HIV-1 reservoir is resistant to ex vivo T cell activation, in the absence of a repressive chromatin environment

86 at the reactivation-resistant viral LTRs [15].

87 Due to the rarity of latently HIV-1 infected T cells in vivo, and specifically the inability to identify or

88 isolate TCR/CD3 activation-inert latently HIV-1 infected T cells from patient samples, the molecular basis of

89 this phenomenon cannot be studied in primary T cells derived from HIV patients on ART (HIV/ART patients).

90 Following the demonstration that TCR/CD3-inertness also occurs in population-based in vitro models

91 of latent HIV-1 infection that use either primary T cells or T cell lines, we opted to describe the molecular basis

92 of this phenomenon in these models utilizing a systems biology approach that describes CD3-inertness at the

93 transcriptome and proteome level. RNA-seq analysis data revealed extensive changes to the gene expression

94 signature of latently infected T cells, regardless of their CD3-response status. Surprisingly, network analysis

95 software was not able to assign any specific network motif to these data, let alone recognize the specific

96 TCR/CD3 signaling deficiency, suggesting that nonspecific transcriptomic noise maybe a hallmark of latently

97 HIV-1 infected cells. This would be consistent with reports that used single cell RNA-seq analysis to describe

98 high levels of heterogeneity between latently HIV-1 infected T cells [16, 17]. In contrast, network analysis

99 based on protein-level data (kinome array analysis) that described the expression and phosphorylation status


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100 of a central set of proteins detected the impairment of the TCR/CD3 signaling pathway and identified specific

101 targets that could be pharmacologically manipulated to largely restore TCR/CD3 induced HIV-1 reactivation.

102 Based on these data we discuss the role of both, transcriptomic noise and stable protein level changes of the

103 host cell signaling network for the control of latent HIV-1 infection.

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105


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106 RESULTS

107 Impairment of the TCR/CD3 activation response in T cells from HIV/ART patients. As a result of the

108 scarcity of latently HIV-1 infected T cells in vivo, it is evident that a comprehensive description of the molecular

109 basis of TCR/CD3 inertness, in particular at the protein level cannot be done using primary T cell material

110 from HIV/ART patients. Nevertheless, to ensure relevance of any such effort in other model systems of latent

111 HIV-1 infection, it would be important to guide such efforts by data that are at least derived from a primary T

112 cell population that acts as a relevant experimental surrogate system. Thus, we initially sought to identify T

113 cell populations in samples from HIV/ART patients that would exhibit an impaired response to TCR/CD3

114 stimulation. Given the substantial body of literature describing an impaired ability of T cells from HIV/ART

115 patients to respond to stimulation, we reasoned that anti-CD3/CD28 mAb-induced proliferation would allow

116 for the identification of a TCR/CD3 activation impaired surrogate T cell subpopulation [18-26].

117 As we compared the proliferation response of T cells from healthy control donors and HIV/ART

118 patients, we were surprised that impairment of cell proliferation was not limited to a small subpopulation of T

119 cells, but rather was observed for the majority of CD4+ T cells from HIV/ART patients. Following CD3/CD28

120 mAb stimulation CD4+ T cells from most healthy control donors (8 of 11) showed a coordinated, discrete

121 proliferation response profile. In contrast, the proliferation response of T cells from the majority of tested virally

122 suppressed HIV/ART patients (8 of 12) was not necessarily quantitatively reduced, but qualitatively impaired,

123 uncoordinated and asynchronous (Figure 1A; Supplemental Figure 1). These data suggested that a large

124 portion of the T cells from patients would have an altered biomolecular phenotype that seemed to affect the

125 TCR/CD3 stimulation induced proliferation response. Given the global nature of this phenotype, we decided

126 to forgo any enrichment of specific T cell subpopulations and use unsorted T cell populations for the initial

127 systems biology analysis.

128 As proteins are the ultimate mediators of the TCR/CD3 signaling pathway, we hypothesized that data

129 describing the altered proteomic state of T cells of patients could help to prioritize data describing the altered

130 state of latently HIV-1 infected T cells. Also, the TCR/CD3 signal is generally not regulated or transmitted

131 through gene regulation, but by protein phosphorylation effects. Accordingly, we used Kinexus antibody

132 arrays that interrogate significant parts of the proteome at the level of protein expression and protein

133 phosphorylation. We chose two experimental conditions, (i) unstimulated and (ii) overnight exposure to IL-2,


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134 a simple experimental surrogate of conditions that T cells would encounter in an immunologically more active

135 environment such as lymph nodes. More than 600 signals were found higher than the predetermined intensity

136 cut-off. Z-ratios were used to identify signals that differed between two conditions. At baseline, the kinome

137 array experiments identified a total of 78 differences (37 up-/41 down-regulated) between T cells from healthy

138 controls and HIV/ART patients, but 147 differences (87 up/60 down) following overnight exposure to IL-2,

139 suggesting that T cells from HIV/ART patients are hyper-responsive (Figure 1B). A focused analysis of the

140 data for significantly altered proteins that have been reported to be involved in TCR/CD3 signaling

141 demonstrated the impairment of this pathway in T cells from HIV/ART patients, in particular following IL-2

142 exposure, but also at baseline (Figure 1C).

143 Ranking the importance of proteins within a signaling network by the magnitude of the observed signal

144 changes would bias the analysis against the possible contribution of low amplitude signals. Instead we used

145 a direct interaction algorithm from the MetaCore network analysis software to generate protein-protein

146 interaction networks that exclusively use the identified, altered proteins (seed nodes). The resulting network

147 allows to prioritize proteins based on their functional importance as indicated by the numbers of interactions

148 within the network, rather than just signal amplitude. A total of 83% of the 230 seed nodes were integrated in

149 the direct interaction network (Supplemental Figure 2), a sign of the quality of the generated data, as we have

150 previously demonstrated that a random list of genes is not linked by this algorithm [27]. 54 nodes had more

151 than 20 interactions in the network, suggesting an overall high degree of linkage between the nodes and a

152 relatively flat signaling hierarchy. Within this network the central proteins (hubs) were p53 (108 interactions),

153 STAT3 (93 interactions), Src (73 interactions), c-Jun (68 interactions), and ß-catenin (67 interactions), all

154 factors that play key roles in TCR/CD3 signaling (Table 1) [28-35]. Consistent with the observed functional

155 phenotype of T cells from HIV/ART patients that indicates an impairment of cell cycle activity following

156 TCR/CD3 stimulation (Figure 1A), as well as the experimental conditions (IL-2 stimulation), network

157 enrichment analysis ranked G1-S growth factor regulation (p=9.91E-25), IL-2 signaling (p= 1.26E-21) and

158 TCR signaling (p= 3.60E-19) in the top five altered network motifs (Table 2). Primarily, these data validate the

159 ability of kinome analysis in conjunction with MetaCore networking software to accurately detect and describe

160 experimental conditions (IL-2 signaling) and phenotypes (TCR signaling, cell cycle impairment). The data

161 suggest that permanent changes to the signaling network of T cells obtained from HIV/ART patients, may not


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162 be limited to latently HIV-1 infected T cells. Under the assumption that latently infected T cells may only

163 represent an extreme phenotype of a T cell dysfunctionality spectrum, possibly a long-term consequence of

164 the initial infection and the widespread destruction of the immune system during the initial infection, the

165 kinomic data describing the altered molecular phenotype of primary T cells from HIV/ART patients should

166 provide an efficient tool to distinguish between HIV-1 infection induced changes that are shared by all T cells

167 (primary and immortalized), and changes that are limited to latently HIV-1 infected T cell lines.

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169 HIV-1 infection generates a TCR/CD3 activation inert population of latently infected T cells. Next, we

170 sought to confirm previous reports that a fraction of the latently HIV-1 infected primary T cell population would

171 be inert to TCR/CD3 stimulation, at least regarding the ability of this stimulus to trigger HIV-1 reactivation [15,

172 36]. For this purpose we used a modification of a well-established primary T cell model of HIV-1 latency [36-

173 42]. Briefly, activated CD4+ T cells were infected with a HIV-1 reporter virus and RT inhibitors were added

174 beginning 72hr post infection. At four weeks post infection, the infection cultures were sorted to remove all

175 actively infected and therefore GFP-positive T cells to lower the signal background. The CD4+T cells were

176 then left unstimulated, stimulated with anti-CD3/CD28 mAb coated beads (ImmunoCult) or stimulated with

177 PMA/ionomycin. The latter is an experimental means to trigger potent activation and proliferation in primary

178 T cells while completely bypassing the TCR/CD3/CD28 pathway. 72hr post activation the samples were

179 subjected to flow cytometric analysis. As stimulation was performed in the presence of RT inhibitors, an

180 increase in the frequency of GFP-positive T cells over background infection in the control sample would reflect

181 reactivated, previously latent HIV-1 infection events. In line with the idea that a fraction of the host T cells of

182 latent HIV-1 infection events have been rendered inert to TCR/CD3 activation, stimulation of CD4+ T cell

183 infection cultures generated by using T cell material from three different donors with a highly potent

184 PMA/ionomycin combination consistently triggered higher levels of HIV-1 reactivation levels than antibody

185 mediated CD3/CD28 stimulation (Figure 2A).

186 While these results confirm that CD3-inertness could be a contributing factor for the stability of the

187 latent HIV-1 reservoir, the primary T cell model of HIV-1 latency is not conducive to comprehensive high

188 resolution systems biology analysis of biomolecular changes in the signaling networks of latently HIV-1

189 infected T cells. The relatively small number of latently HIV-1 infected T cells that can be generated in the


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190 model limits analysis to single cell RNA-seq. Protein level analysis which is likely central to the identification

191 of pertinent blocks in the signaling pathways at the phosphorylation level would be completely impractical due

192 to the large amount of required cell material. As for primary T cell models from patients, the relatively small

193 number of latently HIV-1 infected T cells in these populations can only be identified after a stimulus that

194 triggers HIV-1 reactivation is provided, which (i) still would not allow to discover activation inert subpopulations

195 and (ii) make studies of the baseline signaling networks impossible.

196 Given that Jurkat T cells, despite some shortcomings, were used to decipher the fundamental

197 molecular biology of TCR/CD3 signaling, the effect of CD3 activation on HIV-1 replication [43-46] and are the

198 most commonly used CD4+ T cell line to study HIV-1 latency [47-49], we next explored whether the

199 phenomenon of HIV-1 infection induced CD3-inertness would also occur in HIV-1 infection cultures of Jurkat

200 T cells. Similar to previous efforts made to generate latently HIV-1 infected reporter T cell clones, we infected

201 Jurkat T cells with a HIV-1 reporter virus and followed the establishment of latent HIV-1 infection at the

202 population level [27, 38, 39, 48-50]. After 6 weeks of culture in the presence of RT inhibitors, active

203 background infection was reduced to <2% (Control) and addition of PMA triggered reactivation in ~10% of the

204 T cell population (Figure 2B). Antibody mediated stimulation of CD3/CD28 only triggered HIV-1 reactivation

205 in ~5% of the cells. This phenomenon could be reproduced in reporter T cell populations (J-R5D4) that were

206 infected with either the recombinant clone HIV-1 NL43 (Figure 2C) or with HIV-1 WEAU, a primary R5-tropic

207 patient isolate (Figure 2D) [50, 51], where PMA induced 10-fold or 3-fold more reactivation, respectively, than

208 CD3/CD28 stimulation. As described for primary T cells, in all experiments only a fraction of latent HIV-1

209 infection events that could be reactivated by PMA stimulation would respond to CD3/CD28 stimulation. These

210 data suggest that HIV-1 infection induced CD3-inertness is a fundamental phenomenon that is shared

211 between primary T cells and T cell lines.

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213 Generation of CD3-responsive and CD3-inert latently HIV-1 infected T cell clones. While the percentage

214 of latently HIV-1 infected T cells in long-term HIV-1 infection cultures based on Jurkat T cells (Figures 2B-D)

215 is higher than in in vitro models of latent HIV-1 infection in primary T cells (Figure 2A), these T cell populations

216 are a mixture of cells that are not infected, infected with defective viruses and 5-10% latently HIV-1 infected

217 T cells, likely representing a spectrum of CD3 response phenotypes. The signaling noise resulting from the


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218 cellular diversity would render these T cell populations suboptimal to perform detailed systems biology-based

219 analysis of the mechanisms governing CD3-inertness. We thus generated two latently HIV-1 infected T cell

220 clones that would represent CD3-responsive (JWEAU-A10) and CD3-inert (JWEAU-C6) T cell phenotypes

221 (Figure 3A), despite comparable level of CD3 or CD28 expression (Figure 3B). PMA triggered similar HIV-1

222 reactivation response levels in both T cell clones (Figure 3A), but even saturating amounts of CD3/CD28

223 mAbs only triggered reactivation in JWEAU-A10 T cells, but not in JWEAU-C6 cells. (Figure 3B). CD3-

224 inertness of JWEAU-C6 T cells was not a function of the utilized antibody clone (OKT3, UCHT1, HIT3A)

225 (Figure 3E). Using a TransAM assay to quantify relative NF-B activity over time we could show that TCR/CD3

226 stimulation of JWEAU-A10 T cells produced a strong NF-B response that became apparent as early as 15

227 minutes after stimulation, peaked after one hour and was maintained at an elevated level for >4 hours.

228 Throughout the same observation period, TCR/CD3 stimulation would not trigger an efficient NF-B signal in

229 JWEAU-C6 cells (Figure 3F). This is likely the result of a block in the upstream signaling cascade and not the

230 result of a dysfunctional canonical NF-B pathway, as not only PMA, but also TNF-, which triggers the NF-

231 B activation through a different upstream pathway would induce efficient HIV-1 reactivation in JWEAU-A10

232 and JWEAU-C6 T cells (Supplemental Figure 3). Of note, TLR5 signaling which triggers NF-B activation

233 following stimulation with bacterial flagellin was also partially compromised in JWEAU-C6 T cells when

234 compared to JWEAU-A10 T cells (Figure 3G). This was also observed at the population level, where flagellin

235 stimulation only triggered HIV-1 reactivation in a fraction of the latently HIV-1 infected T cell population (Figure

236 3H). Changes to the host cell signaling network that were induced during HIV-1 infection thus may not be

237 limited to the TCR/CD3 signaling pathway and extend to other activating receptors/pathways that are currently

238 investigated as targets of latency reversing agents (LRA) [52-56].

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240 Gene expression patterns associated with CD3-inert latently HIV-1 infected T cells. At the time, studies

241 that have described the phenotype of latently HV-1 infected T cells have focused on changes to the

242 transcriptome [16, 17]. To possibly align our data with these data sets, we first generated RNA-seq data

243 describing the gene expression signatures of uninfected T cells, the CD3 activation-inert JWEAU-C6 T cells

244 and the CD3-responsive JWEAU-A10 T cells.


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245 A total of 7,151 genes (of 20,830 genes with any reads) were differentially expressed (likelihood ratio

246 test, adjusted P-value < 0.01) across the three cell types. More stringent pairwise comparison between the

247 parental Jurkat T cells and each of the latently HIV-1 infected T cell clones was performed with a padj < 0.01,

248 a fold-change 2; and the signal for at least one the cell types had to be >250 reads. Relative to Jurkat T cells

249 481 genes were upregulated and 627 were downregulated in JWEAU-C6 T cells (total of 1108 genes); 537

250 genes were upregulated and 484 genes were downregulated in JWEAU-A10 T cells (total of 1021 genes)

251 (Figure 4A). 354 of the downregulated and 304 of the upregulated genes were shared between the two latently

252 HIV-1 infected T cells clones. This would mean that of the altered genes, only 45% are shared by the two

253 latently HIV-1 infected T cell clones. In a direct comparison of the two latently HIV-1 infected T cell clones,

254 relative to JWEAU-A10 T cells, 348 genes were downregulated and 125 were upregulated in JWEAU-C6 T

255 cells (Figure 4B). These findings are consistent with recent reports suggesting vast heterogeneity between

256 individual latently HIV-1 infected T cells and emphasize that HIV-1 latency can be maintained in extensively

257 different cellular environments [16, 17]. A direct interaction network generated using the list of genes found

258 to be differentially regulated between JWEAU-C6 and JWEAU-A10 T cells only integrated 53% of the seed

259 nodes, and of these, 50% were connected with only a single link (Supplemental Figure 4). In average each

260 node of the network had only three interactions. Both the failure to efficiently link all signals into the network

261 and the low level of interactions per node suggest that a large portion of the observed gene expression

262 changes are not functionally related and represent transcriptomic noise. In line with transcriptomic noise being

263 the major component of the observed signal changes, motifs suggested by network enrichment analysis had

264 very low statistical significance and, most importantly, completely failed to recognize the defined experimental

265 phenotype, TCR/CD3 inertness (Supplemental Table 1).

266 If transcriptomic noise were to mask functional motifs in the data set, it stands to reason that

267 hierarchical clustering of the gene expression data could improve the likelihood of discovering motifs that

268 control latency, assuming the phenomenon is driven primarily by changes to gene expression (Figure 4C).

269 This was indeed the case. Regulation of genes in two of the sub-clusters was largely shared between the two

270 latently HIV-1 infected T cell lines, but different from the parental T cells. Genes in cluster #2 (960 genes) and

271 cluster #6 (2353 genes) were upregulated or downregulated, respectively, in both latently infected T cell

272 clones when compared to uninfected T cells. This would suggest that these changes were likely common


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273 features of latency control. Gene Ontology (GO) enrichment (Figure 4C; Supplemental Figure 5) of cluster #6

274 genes (downregulated in both latently HIV-1 infected T cell clones) suggested changes to RNA metabolism,

275 adaptive immune system function, the antiviral response, regulation of the mitotic cell cycle, and T cell

276 activation. Upregulated genes (cluster #2) were found to be reported as critical for lipid metabolism, regulation

277 of cellular component size, and actin filament-based processes. These findings are in agreement with

278 previous descriptions of gene regulation in latently HIV-1 infected cells, suggesting that our model system

279 recapitulates established transcriptional hallmarks of latently infected host cell changes [42, 57-91].

280 In the remaining four clusters, gene regulation differed between the two latently HIV-1 infected T cells,

281 but one or the other had gene regulation patterns similar to the parental T cells (cluster #1 (1449 genes), #3

282 (1155 genes), #4 (805 genes), #5 (429 genes)), emphasizing that latently HIV-1 infected T cells not only differ

283 in their gene expression signature from uninfected T cells, but also greatly differ from each other. As

284 uninfected Jurkat T cells are known to be responsive to TCR/CD3 stimulation, we would speculate that cluster

285 #1 and #4, in which Jurkat T cells and the responsive JWEAU-A10 T cells share expression patterns, but

286 JWEAU-C6 gene expression patterns differ, should hold the dysregulated genes that are responsible for the

287 TCR/CD3-inertness of JWEAU-C6 T cells. Surprisingly, none of the enriched GO terms for these genes

288 suggested critical relevance to CD3-responsiveness. Enrichment for Gene Ontology (GO) terms using the

289 genes in clusters #1 and #4 failed to identify GO terms associated with TCR signaling (Figure 4C and

290 Supplemental Figure 5). We thus refocused on the genes in cluster #6 that would be descriptive of the T cell

291 activation motif (GO term 0042110), however, detailed analysis of these data emphasized that the TCR/CD3

292 signaling pathway is generally impaired in latently HIV-1 infected T cells, but did not reveal relevant

293 differences between the two latently HIV-1 infected T cell clones that could immediately explain the completely

294 CD3-inert phenotype of JWEAU-C6 T cells (Supplemental Figure 6).

295 The data confirm that host T cells of latent HIV-1 infection events greatly differ from uninfected T cells

296 and demonstrate that heterogeneity between latently HIV-1 infected primary T cells is also a characteristic of

297 latently infected T cell lines that were generated under identical experimental conditions. The data suggest

298 that impairment of the TCR signaling pathway is a fundamental characteristic that is shared between latently

299 infected T cell clones, but little information on potential targets that could influence the stability of latent HIV-

300 1 infection could be directly extracted from the data set. Lastly, given the sheer magnitude of the gene


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301 expression differences that are detected in the latently HIV-1 infected T cells, and the likely resulting

302 misalignment of many cellular functionalities, it is tempting to speculate that transcriptomic noise could induce

303 an unspecific signaling threshold to TCR/CD3 signaling.

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305 Kinomic description of CD3-activation inert latently HIV-1 infected T cells. One possible explanation for

306 the inability of the RNA-level analysis to directly identify the underlying functional impairment (CD3-inertness)

307 that stabilizes latent HIV-1 infection is that major regulation effects occur at the level of post-transcriptional

308 and post-translational modifications (e.g. protein phosphorylation). Changes to the proteome at the level of

309 protein expression and phosphorylation would be the most direct indicators of functional modifications of the

310 host cell signaling network that cause CD3-inertness and contribute to the stability of latent HIV-1 infection.

311 To identify protein regulation effects that differ between CD3-responsive and CD3-inert T cells we again used

312 antibody arrays. Between JWEAU-A10 and JWEAU-C6 T cells the array identified a total of 123 significant

313 differences. Of these signals, 74% were phospho-site specific and 26% were pan-specific. This was

314 consistent with the general distribution of pan-specific or phospho-site specific antibodies on these arrays and

315 did not indicate any bias, however, it provides an estimate of the number of changes occurring at the level of

316 protein phosphorylation. The MetaCore direct interaction algorithm efficiently linked these differentially

317 regulated signals. 86% of the seed nodes were linked into a direct protein-protein interaction network, which

318 had an average degree of 14 interactions per node, a sign that likely more than one pathway within the overall

319 network was affected, and no single master switch controlled the recorded changes (Supplemental Figure 7).

320 The three highest linked nodes in the network were p53 (55 interactions), STAT3 (47 interactions), and c-Src

321 (46 interactions) (Table 3), which paralleled the findings in primary T cells from HIV/ART patients (Table 1).

322 Also, consistent with the functional phenotype, pathway enrichment analysis identified Immune

323 Response/TCR signaling (p=6.78E-14) as a highly ranked altered motif (Table 4). There was additional

324 overlap with other motifs that had been identified as altered in primary T cell material from HIV/ART patients,

325 suggesting that HIV-1 infection induced changes that are shared between primary T cells and T cell lines are

326 not limited to the immediate TCR/CD3 signaling pathway (e.g. G1-S Growth Factor regulation (p=3.97E-16),

327 NOTCH signaling(p=1.87E-16), regulation of initiation (p= 2.2E-15)).

328


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329 Validating the role of transcriptomic noise as reactivation threshold for TCR/CD3 signaling. The gene

330 expression and protein level analysis suggested two potential biomolecular mechanisms that contribute to

331 TCR/CD3 activation inertness in a large fraction of the latently HIV-1 infected T cell reservoir: (i) the presence

332 of transcriptomic noise that could interfere with accurate signal transduction and (ii) stable changes of the

333 activity or responsiveness of individual network nodes detected mostly at the protein level, that could act as

334 central switches for the transmission of the TCR/CD3 signal. To test the first hypothesis, we took advantage

335 of findings by others describing that histone deacetylase (HDAC) inhibitors, BET protein inhibitors or cell

336 differentiating agents, through a variety of specific and off-target effects trigger genome-wide changes to gene

337 expression patterns, and as such create transcriptomic noise [92-102]. Some have actually argued that HDAC

338 inhibitors would trigger HIV-1 reactivation by inducing an increase in genetic noise [103] and the varying

339 potential of different histone deacetylase inhibitors to trigger HIV-1 reactivation has been linked to the

340 induction of differential host cell responses [104].

341 We reasoned that if transcriptomic noise contributes to a TCR/CD3 activation threshold in latently HIV-

342 1 infected T cells, then treatment of the CD3-responsive JWEAU-A10 T cells with HDAC inhibitors, BET

343 inhibitors or cell differentiating agents, would increase the level of transcriptomic noise and reduce the ability

344 of TCR/CD3 activation to trigger HIV-1 reactivation. Following a 24 hours pretreatment period to allow each

345 inhibitor to exert its full effect, non-toxic concentrations of the histone deacetylase inhibitor

346 suberanilohydroxamic acid (SAHA; vorinostat) had no direct effect on HIV-1 reactivation in either, JWEAU-

347 A10 and JWEAU-C6 T cells (Figure 5A). As predicted, SAHA then decreased the TCR/CD3 stimulation-

348 induced HIV-1 reactivation response of JWEAU-A10 T cells, while not restoring a TCR/CD3 response in

349 JWEAU-C6 T cells. The cell differentiating compound hexamethylene bisacetamide (HMBA), which has been

350 reported to trigger HIV-1 reactivation in some experimental systems [105, 106], did not directly trigger HIV-1

351 reactivation (Figure 5B). However, in line with its ability to induce transcriptomic noise HMBA suppressed the

352 TCR/CD3 stimulation-induced HIV-1 reactivation response of JWEAU-A10 T cells. HMBA had no effect on

353 latent HIV-1 infection in JWEAU-C6 T cells, neither by itself nor following TCR/CD3 stimulation. Lastly, we

354 tested the effect of JQ1, a Brd4 inhibitor that has been reported to not only affect global cellular gene

355 expression, but to trigger low level HIV-1 reactivation [107-109]. At the utilized concentration JQ1 triggered

356 modest levels of HIV-1 reactivation by itself in JWEAU-A10 T cells, but again suppressed the ability of


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357 TCR/CD3 stimulation to trigger HIV-1 reactivation (Figure 5C). These data provide evidence that compounds

358 that are known to generate transcriptomic noise impair the ability of TCR/CD3 signaling to trigger HIV-1

359 reactivation in otherwise CD3-responsive T cells, and thereby indirectly validate a role for transcriptomic noise

360 as a HIV-1 reactivation threshold component.

361

362 Probing the role of individual network nodes in CD3 inertness and HIV-1 latency control. The second

363 possibility would be that individual, highly linked network nodes act as molecular switches that control latent

364 HIV-1 infection, or are key to CD3-inertness, and if targeted alter the stability of latent HIV-1 infection events

365 (Supplemental Figure 7). We would not expect that interference with these key network nodes would directly

366 trigger HIV-1 reactivation but anticipated that pharmacological targeting of the selected network nodes would

367 either boost or abrogate TCR/CD3 activation-induced HIV-1 reactivation. Ideally, interference would restore

368 the ability of TCR/CD3 activation to trigger HIV-1 reactivation in the otherwise inert JWEAU-C6 T cells.

369 We chose three targets for which selective, clinically relevant inhibitors were available and that were

370 (i) all highly ranked nodes in in the protein-protein interaction network describing the difference between

371 primary T cells from healthy individuals and HIV/ART patients (Table 1) and (ii) central to the network

372 describing the kinomic differences between CD3-responsive and CD3-inert latently HIV-1 infected T cell

373 clones (Table 3). By these means, we would prioritize targets that occur in a cell type independent manner

374 and are less likely to be relevant only in immortalized latently HIV-1 infected T cell clones. Based on these

375 criteria, we chose to target (i) Src (dasatinib), (ii) Raf (sorafenib) and (iii) STAT3 (S31-201). Dasatinib is

376 approved for the treatment of chronic myelogenous leukemia and acute lymphoblastic leukemia. Sorafenib

377 is approved for the treatment of certain kidney and liver cancers and certain forms of Acute Myeloid Leukemia.

378 The selective STAT3 inhibitor S31-201 had been successfully used in several mouse cancer models [110,

379 111]. We pretreated JWEAU-A10 T cells or long-term HIV-1 infected T cell populations that we enriched for

380 latently HIV-1 infected T cells (see methods section) with increasing concentrations of the three drugs, and,

381 after 24 hours, stimulated with a suboptimal concentration of anti-CD3/CD28 mAbs. Initial use of suboptimal

382 CD3/CD28 stimulation would facilitate the discovery of inhibitory or additive/synergistic effects of the drug on

383 TCR/CD3 signaling. Consistent with the reported central role of Src proteins in TCR/CD3 signaling, inhibition

384 of Src by sub-cytotoxic concentrations of dasatinib effectively abrogated TCR/CD3 triggered HIV-1


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385 reactivation, in both JWEAU-A10 T cells (Figure 6A) and an enriched latently HIV-1 infected T cell population

386 (Figure 6D) [112-115]. It is noted that dasatinib targets several Src family members, which certainly could

387 explain the observed potent effect. While this finding is not surprising, and likely therapeutically irrelevant, it

388 shows the ability of network software to correctly identify and prioritize functionally important protein targets.

389 In contrast to the inhibitory effect of dasatinib, the Raf inhibitor sorafenib boosted the ability of suboptimal

390 CD3/CD28 mAb combinations to trigger HIV-1 reactivation. Sorafenib concentrations as low as 80nM

391 increased the ability of a suboptimal CD3/CD28 stimulation signal to trigger HIV-1 reactivation in JWEAU-A10

392 T cells from 10% to >20%. A boosting effect could already be observed at lower sorafenib concentrations

393 (Figure 6B). A less potent but significant reactivation-boosting effect was also observed in the latently HIV-1

394 infected T cell population (Figure 6E). The limited effect of sorafenib on the cell population is another indicator

395 of the heterogeneity between latently HIV-1 infected T cells. Finally, the STAT3 inhibitor S31-201 showed

396 robust enhancement of CD3/CD28 triggered HIV-1 reactivation in JWEAU-A10 T cells (Figure 6C), and

397 importantly, had a strong reactivation-boosting effect in the latently HIV-1 infected T cell populations (Figure

398 6F).

399 Having demonstrated that c-Raf and STAT3 inhibition boosted the effect of suboptimal CD3/CD28

400 stimulation, we next explored whether either c-raf inhibition or STAT3 inhibition would actually reprogram

401 latently HIV-1 infected T cells that are TCR/CD3-inert to become CD3-responsive. This would mean that

402 following either sorafenib or S31-201 treatment, optimal CD3/CD28 stimulation would trigger HIV-1

403 reactivation in the sub-population of latently HIV-1 infected T cells that are otherwise only responsive to PMA

404 treatment (CD3 inert). As seen in Figure 7A, sorafenib treatment resulted in a small, statistically not significant

405 increase in the percentage of HIV-1 expressing T cells following optimal CD3/CD28 activation, but the majority

406 of the CD3-inert T cell fraction that was revealed by PMA stimulation could still not be addressed. The STAT3

407 inhibitor S31-201 exhibited a much stronger boosting effect. Pretreatment with S31-201 followed by

408 CD3/CD28 stimulation produced HIV-1 reactivation levels that were similar to the reactivation levels produced

409 by PMA (Figure 7B), indicating that STAT3 inhibition would restore the ability of a large fraction of the initially

410 CD3-inert T cells to generate an effective TCR/CD3 pathway signal. Of note, neither sorafenib nor S31-201

411 had any effect on the ability of CD3/CD28 mAb combinations to trigger HIV-1 reactivation in the inert JWEAU-

412 C6 T cells, suggesting that extreme CD3-inertness likely cannot be reversed (Supplemental Figure 8).


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413 Finally we tested whether these findings were transferable to latently HIV-1 infected primary T cell

414 models, a possibility that was suggested by the similarity of signaling network changes identified in primary T

415 cells from HIV/ART patients (Table 1) and latently HIV-1 infected T cell lines (Table 3). As for the experiments

416 shown in Figure 2A, we generated latently HIV-1 infected primary T cell cultures using CD4+ T cells from 5

417 different individuals and tested whether c-raf or STAT3 inhibition here would also boost the ability of

418 CD3/CD28 stimulation to trigger HIV-1 reactivation (Figure 7C). Similar to what was observed in latently HIV-1

419 infected T cell lines, neither sorafenib nor S31-201 by themselves had any HIV-1 reactivating effect, but both

420 drugs exhibited the ability to boost CD3/CD28 mAb-induced HIV-1 reactivation in a donor-dependent manner.

421 For two of the donors, treatment with both drugs increased CD3/CD28-induced reactivation, whereas for the

422 remaining three donors only one of the drugs provided boosting activity. The observed donor variation is

423 common to models of latent HIV-1 infection that utilize replication competent non-attenuated virus, but it also

424 points out that heterogeneity caused by the immunological history of primary T cells likely adds to the

425 complexity of HIV-1 latency control in primary T cells. The ability of systems biology data that were generated

426 in T cell line models to effectively predict drug targets in latently HIV- 1 infected primary T cells provides

427 additional evidence that TCR/CD3 inertness is a fundamental mechanism that may consistently affect T cells

428 that survive the infection.

429


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430 DISCUSSION

431

432 At the time it is assumed that latent HIV-1 infection events are established as activated T cells become

433 HIV-1 infected before transitioning to a long-lived resting memory phenotype [1, 116-120]. The resting status

434 of memory T cells is thought to deplete HIV-1 of essential transcription factors and thereby restrict its ability

435 to express its genes, forcing the virus into a latent state [121-123]. Based on this concept, it is assumed that

436 T cell activation through antibody-mediated stimulation of the TCR/CD3 complex, the experimental equivalent

437 of cognate antigen recognition, would trigger HIV-1 reactivation. It has by now been conclusively

438 demonstrated that this is correct only for a part of the latent HIV-1 reservoir, whereas a large portion of the

439 latent reservoir remains recalcitrant to TCR/CD3 activation [15, 36]. We thus hypothesized that a part of the

440 HIV-1 reservoir is established in T cells that are either TCR/CD3 activation inert prior to infection or are

441 rendered TCR/CD3 activation resistant by the cellular response to the actual infection event.

442 In this study we provide formal experimental evidence that TCR/CD3 inertness (i) is generated during

443 HIV-1 infection (Figures 1, 2), (ii) is a fundamental mechanism that is conserved between primary T cells and

444 T cell lines (Figure 2) and (iii) affects a substantial portion of the T cells serving as host cells for the latent

445 HIV-1 reservoir (Figures 2, 7). The existence of such an activation-inert reservoir T cell population would

446 conclusively explain why cognate antigen recognition does not deplete the latent HIV-1 reservoir over time.

447 We further demonstrate that host cells of latent HIV-1 infection events are characterized by extensive stable

448 changes to their signaling networks (Tables 1 and 3). TCR/CD3 inertness was caused by (iv) the occurrence

449 of high-level transcriptomic noise or could be induced by the generation of transcriptomic noise (Figure 5),

450 and (v) was associated with selective changes to the TCR/CD3 signaling pathway, which could be targeted

451 with pharmacological inhibitors to largely reverse TCR/CD3 inertness (Figures 6,7).

452 The observed presence of transcriptomic noise explains the findings by others that described a high

453 degree of heterogeneity between individual latently HIV-1 infected T cells [16, 17]. Our research extends on

454 these studies in primary T cells and demonstrates that this diversity is not necessarily the result of a pre-

455 existing heterogeneity caused by the different immunological history or differentiation status of T cells that

456 later on become latently HIV-1 infected. While such T cell diversity may add to the observed heterogeneity in

457 primary T cells (Figure 7C), we show that heterogeneity is generated by a fundamental mechanism that must


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458 be part of the cellular response to the actual infection event. Heterogeneity between latently HIV-1 infected T

459 cells was observed at the protein and gene expression level (Figure 4, and Tables 1-4). Given that gene

460 expression analysis is now the basis of most studies, it was somewhat surprising that RNA-seq analysis would

461 indicate extensive changes to the gene expression landscape, but network analysis software could not

462 efficiently integrate these data into interaction networks or for that matter assign functional motifs to the data

463 (Supplemental Table 2). Even hierarchical clustering followed by subtree cluster analysis would not directly

464 identify potential molecular switches that were to affect TCR/CD3 mediated HIV-1 reactivation. This was even

465 more surprising as the phenotype, TCR/CD3 inertness, was already predetermined by functional assays

466 (Figures 1-3). In contrast, kinome analysis that directly determined systemwide changes to protein expression

467 or phosphorylation patterns, immediately identified the underlying molecular phenotype (Table 4). The data

468 emphasize the need to investigate HIV-1 latency control mechanisms at the level of both, RNA regulation and

469 protein functionality.

470 The formal demonstration that heterogeneity at the gene expression and protein

471 expression/phosphorylation level extends to functional diversity has implications for therapeutic strategies

472 that seek to trigger HIV-1 reactivation in patients. The data imply that single drug interventions will be

473 insufficient to accomplish complete viral reactivation and subsequent elimination. TCR/CD3 inertness was not

474 the only functional consequence of the molecular changes that can occur in host cells of latent HIV-1 infected

475 T cells. While we do not detail this phenomenon, we found that TLR pathway signaling can also be impaired

476 (Figure 3), which would have implications for efforts to identify HIV-1 reactivating agents that address the

477 TLR/Myd88 pathway [52-56]. Given that our data detail the dysfunctional nature of latently HIV-1 infected T

478 cells, and the seemingly random nature of observed changes, it needs to be assumed that the further

479 upstream a NF-B activating pathway is targeted, the less likely it is that an efficient, HIV-1 reactivating NF-

480 B signal is being triggered. The results thus support the idea that optimal HIV-1 reactivation agents should

481 induce NF-B activation by targeting proteins as immediate upstream of NF-B as possible (e.g. directly

482 interfere with the NF-B/IB complex), which reduces the likelihood that changes to the host cells affect drug

483 efficacy. Pathways that activate NF-B, but are essential to the long-term survival of the infected T cell could

484 be most attractive. Here functionality needs to be assumed, as the cells would otherwise be eliminated shortly

485 after the infection event reduces functionality of such an essential pathway. This may make the CD3 or TLR


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486 pathways suboptimal targets for LRAs, unless possibly used in a combination therapy. By the same token,

487 the extensive heterogeneity would favor the development of drugs like SMAC mimetics such as AZD5582

488 that also activate the non-canonical NF-B pathway. SMAC mimetics, including AZD5582 have already been

489 demonstrated to trigger HIV or SIV reactivation [124-126]. A possible advantage of SMAC mimetics as LRAs

490 could be that they also target an important survival pathway for cells. SMAC mimetics imitate the activity of a

491 protein call Second Mitochondrial-derived Activator of Caspases, a pro-apoptotic mitochondrial protein that is

492 an endogenous inhibitor of a family of cellular proteins called the Inhibitor of Apoptosis Proteins (IAPs), which

493 in turn are important regulators of cell death and survival. The importance of such a pathway for long-term

494 survival of the cells should make it less likely that this pathway is impaired in host cells of latent HIV-1 infection

495 events. Functional conservation of this pathway would increase the likelihood of SMAC mimetics to broadly

496 trigger HIV-1 reactivation within the population of latently HIV-1 infected T cells, despite the heterogeneity

497 and pre-existing inertness in other pathways.

498 Conceptually, the provision of experimental evidence that many host cells of latent HIV-1 infection

499 events are extensively altered in their signaling networks and gene expression patterns and in extension, are

500 altered in many of their functionalities, raises the possibility to affect the stability of latent HIV-1 infection

501 events in these cells through an alternative approach. Therapeutic interventions that would restore host cell

502 functionality may enable TCR/CD3 activation-inert latently HIV-1 infected T cells to properly respond to

503 cognate antigen recognition, and the ensuing cellular increase in NF-B activation would be sufficient to

504 trigger HIV1 reactivation. Such a scenario would focus on the development of cellular reprograming strategies

505 and remove the requirement for a potent, systemwide, stimulatory therapeutic intervention, which would here

506 be provided by a natural process, specific antigen recognition. Given the general impairment of T cell

507 functionality in PBMCs from HIV/ART patients that we observed (Figure 1), a T cell status that seems to have

508 many signaling network similarities with latently HIV-1 infected T cells, such a strategy may also improve the

509 overall immune status of HIV-1 patients.

510

511


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512 MATERIAL AND METHODS

513

514 Cell culture and reagents. All T cell lines were maintained in RPMI 1640 supplemented with 2 mM L-

515 glutamine, 100 U/ml penicillin, 100 µg/ml streptomycin and 10% heat inactivated fetal bovine serum. Fetal

516 bovine serum (FBS) was obtained from HyClone (Logan, Utah) and was tested on a panel of latently infected

517 cells to assure that the utilized FBS batch did not spontaneously trigger HIV-1 reactivation [48, 127]. The

518 phorbol esters, Phorbol 12-myristate 13-acetate (PMA), prostratin, along with HMBA, Flagellin, and

519 Trichostatin A (TSA) were purchased from Sigma. Recombinant human TNF-α a well established trigger of

520 HIV-1 reactivation was purchased from Gibco. The PKC activator Bryostatin and Sodium Butyrate (NaBu)

521 were purchased from EMD Millipore. Vorinostat (SAHA) was purchased from Selleck chemicals. Specific

522 Inhibitors or recombinant proteins such as the STAT3 inhibitor S31-201 (NSC 74859), the Src inhibitor

523 dasatinib, or the c-Raf inhibitor sorafenib were all purchased from Fisher Scientific. Anti-CD3/CD28 antibody-

524 coated beads (ImmunoCult) were purchased from STEMCELL technologies (Vancouver, CA).

525

526 Human Sample disclosure and Information. Healthy, HIV-seronegative adults and HIV-seropositive

527 subjects were recruited from the 1917 Clinic cohort at the University of Alabama at Birmingham to donate

528 peripheral blood. All HIV-1 seropositive subjects were on ART and with undetectable viral loads (VL <50

529 copies/mL) for a median of 10 months (6.5-17.3 months). In accordance with the specific protocol for this

530 project that was approved by the UAB Committee on Human Research (IRB-160715008), all subjects

531 provided written informed consent for all biologic specimens and clinical data used in this study.

532

533 Model of HIV-1 latency in primary T cells. CD4+ T-cells were isolated from PBMCs from healthy donors

534 using a negative CD4+ T cell isolation kit (Stemcell), and were then rested overnight in RPMI (10% FBS, 1%

535 Pen/Strep, 1% L-Glutamine, and IL-2 (10 IU/ml). The T cells were stimulated with Immunocult (Stemcell) at a

536 concentration of ~2 beads/cell and incubated at 37° for 3 days. On day three post stimulation the T cells were

537 placed in fresh RPMI medium at a density of 5 million cells/well in a 6 well plate and infected with a HIV-GFP

538 reporter virus (pBR43IeG-NA7nef) [74]. After infection cells were pelleted by centrifugation and placed in fresh

539 RPMI medium. After 48 hours GFP levels were determined as surrogate marker of HIV-1 infection using flow


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540 cytometry to confirm successful infection. To prevent further viral replication 3TC and EFV and were added

541 and the cells were cultured for 30 days. Medium was replenished twice a week. After 30 days the T cell

542 cultures were sorted using a BD Aria cell sorter to remove all remaining GFP+, and therefore actively infected

543 T cells to reduce the signal background. Cells were then plated at a density of 5x105 to 1x106/ml into the

544 described experimental conditions. 3 days post reactivation the cell cultures were analyzed for the frequency

545 of GFP+ T cells using a BD LSRII flow cytometer acquiring at least 100,000 events per experimental condition.

546

547 Generation of latently HIV-1 infected T cell populations and T cell clones. The latently HIV-1 infected T

548 cell populations were generated by infecting a Jurkat T cell-based reporter cell population (JR5D4 cells) [50]

549 with the primary HIV-1 patient isolate WEAU [51] or the classic laboratory strain HIV-1 NL43. Starting two

550 days post infection a reverse transcriptase inhibitor (3T3) was continuously added to prevent viral replication

551 and the establishment of pre-integration latency. On day 4 post infection we selected cultures with infection

552 levels around 40% and maintained these until day ~60 when active infection levels had mostly declined to

553 levels between 1-5%. In the HIV-1 WEAU infected T cell population, addition of PMA to this T cell population

554 revealed the presence of a reactivatable HIV-1 reservoir that varied in size between 4-10%. The HIV-1 WEAU

555 infected bulk cell populations were plated in a limiting dilution and tested for CD3 inertness and PMA

556 responsiveness, to generate the differentially responsive cell clones JWEAU-A10 (responsive) and JWEAU-

557 C6 (inert). To generate T cell populations that were enriched for latently HIV-1 infected T cells, we exploited

558 the reported ability of host cells of latent HIV-1 infection events to repeatedly shut down the virus into a latent

559 state. HIV-1 reactivation was triggered using PMA and on day 2 post activation cell sorting was performed for

560 GFP+ T cells. Over a period of 3-4 weeks, active HIV-1 infection in these cells was again shut down into a

561 latent state, resulting in an enriched population in which ~80% of the cells would hold reactivatable, latent

562 HIV-1 infection events.

563

564 CFSE Proliferation assays. PBMCs were resuspended in PBS at roughly 107 cells/ml and labeled at room

565 temperature with 5,6-carboxyflourescein diacetate succinimidyl ester (CFSE; Molecular Probes, Eugene, OR)

566 at 1.25M for 4 mins. The cells were washed twice in PBS (10% FBS). Cells were then resuspended in

567 complete media (RPMI + 10% AB sera, 50 U/ml of penicillin/streptomycin, 2mM L-glutamine). Cells were


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568 stimulated with -CD3/CD28 mAbs for 4 days, after which proliferation was measured by loss of CFSE

569 fluorescence using a BD LSRII flow cytometer with >100,000 lymphocyte events collected per sample.

570 Unstimulated cells were used for negative controls.

571

572 Kinexus™ Antibody Microarray-based analysis. Kinome analysis to study protein expression and

573 phosphorylation levels was done using Kinex™ microarray analysis. 50 μg of cell lysate protein (~5x106 cells)

574 from each sample or experimental condition were covalently labeled with a proprietary fluorescent dye

575 according to the manufacturer’s instructions (Kinexus, Canada). After the completion of the labeling reaction,

576 any free dye was removed by gel filtration. After blocking non-specific binding sites on the array, an incubation

577 chamber was mounted onto the microarray to permit the loading of two side by side samples on the same

578 chip. Following sample incubation, unbound proteins were washed away. KAM-850 arrays detect 189 protein

579 kinases, 31 protein phosphatases and 142 regulatory subunits of these enzymes and other cell signaling

580 proteins. This array provided information on the phosphorylation state of 128 unique sites in protein kinases,

581 4 sites in protein phosphatases and 155 sites in other cell signaling proteins. KAM-900 chips are spotted in

582 duplicates with over 870 antibodies. 265 pan-specific antibodies and 613 phosphosite specific antibodies.

583 Each array produced a pair of 16-bit images, which are captured with a Perkin-Elmer ScanArray Reader laser

584 array scanner (Waltham, MA). Signal quantification was performed with ImaGene 8.0 from BioDiscovery (El

585 Segundo, CA) with predetermined settings for spot segmentation and background correction. The

586 background-corrected raw intensity data were logarithmically transformed with base 2. Since Z normalization

587 in general displays greater stability as a result of examining where each signal falls in the overall distribution

588 of values within a given sample, as opposed to adjusting all of the signals in a sample by a single common

589 value, Z scores are calculated by subtracting the overall average intensity of all spots within a sample from

590 the raw intensity for each spot, and dividing it by the standard deviations (SD) of all of the measured intensities

591 within each sample [128]. Z’ ratios were further calculated by taking the difference between the averages of

592 the observed protein Z scores and dividing by the SD of all of the differences for that particular comparison.

593 Calculated Z’ ratios have the advantage that they can be used in multiple comparisons without further

594 reference to the individual conditional standard deviations by which they were derived.

595


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596 RNA-seq analysis. Total RNA was extracted using the Qiagen RNeasy mini kit. Genewiz (Plainfield, NJ)

597 prepared cDNA libraries and performed sequencing. For RNA-seq data processing and analysis, raw paired

598 reads were first adapter-trimmed from fastq files using TrimGalore! (http://www.bioinformatics.babraham.

599 ac.uk/projects/trim_galore/). Reads were aligned to Hg19 using STAR [129], and count matrices were

600 generated using HTSeq-count [130]. DESeq2 was used to normalize read counts (CPM) and analyze

601 differential expression [131]. For the analysis of global transcriptional regulation, genes were considered

602 differentially expressed if they had an adjusted P-value <0.01 by the likelihood ratio test. For pairwise

603 comparisons, genes were considered differentially expressed if they had at least a 1.5-fold change, adjusted

604 P-value < 0.01, and at least one signal > 250 CPM, as qPCR validation repeatedly failed to confirm lower

605 signals. Pheatmap (*) was used to row-normalize, cluster (via hclust, using the complete linkage method),

606 and visualize global transcriptional changes. Gene Ontology analysis was conducted using Metascape [132].

607

608 Network Analysis. MetaCore software (Thomson Reuter) was used to generate shortest pathway interaction

609 networks that are presented. Pathway specific GO filters or tissue specific filters were used to prioritize nodes

610 and edges. MetaCore was further used to identify pathway associations of the identified input dataset, using

611 network enrichment analysis.

612

613 Statistical Analysis. Statistical significance of differences between multiple experimental conditions was

614 determined by ANOVA with multiple comparisons (Tukey’s correction) (*p<0.05, **p<0.01, ***p<0.001,

615 ****p<0.0001).


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616 FIGURE LEGENDS

617

618 Figure 1: T cells from HIV-1 patients on ART exhibit altered activation response phenotypes. (A) T

619 cells from healthy individuals and HIV/ART patients were stained with CFSE and stimulated with a CD3/CD28

620 mAb combination. The proliferative response of the T cells, measured as decrease in CFSE fluorescence

621 intensity, was determined on day 4 post stimulation using flow cytometric analysis. The coordinated CFSE

622 profile shown is representative for a total 8 of 11 healthy controls, whereas an asynchronous CFSE profile

623 was found for 8 of 11 HIV/ART patients. (B) Kinome array analysis was performed using T cells from 9 healthy

624 individuals and 8 HIV/ART patients. Samples were produced either at baseline or following 24h of IL-2

625 incubation. The samples for each experimental conditions were pooled to minimize individual variations and

626 loaded onto Kinexus antibody KAM-850 arrays and expression levels and phosphorylation states of a total of

627 510 kinases and phosphatases were determined. Signals that were altered in samples from HIV/ART patients

628 relative to healthy controls at baseline or following IL-2 stimulation were plotted. (C) Focused analysis of

629 kinase and transcription factor signals from the kinome array involved in the TCR/CD3 signaling pathway

630 represented as a heat map (expression or phosphorylation) for each of the four experimental conditions.

631

632 Figure 2: HIV-1 infection induced TCR/CD3 activation inertness is conserved between primary T cells

633 and T cell lines. (A) Primary CD4+ T-cells from three donors were activated, infected with a HIV-GFP reporter

634 virus and 4 weeks post infection, residual GFP-positive, and therefore actively infected T cells were removed

635 by cell sorting. The T cell cultures were then left unstimulated (control), stimulated with anti-CD3/CD28 mAb

636 coated beads or stimulated with PMA/ionomycin. Using GFP expression as a surrogate marker, levels of

637 active/reactivated HIV-1 expression were determined 72 hours post stimulation using flow cytometric analysis

638 for GFP expression. (B) Jurkat T cells were infected with a HIV-1 NL4-3 based GFP reporter virus as

639 previously described [49, 50]. The T cell populations were then cultured for a total of 60 days in the presence

640 of RT inhibitors to prevent de novo infection. At this time, samples of the infection cultures were either left

641 unstimulated, treated with anti-CD3/CD28 mAb coated beads or stimulated with PMA. Baseline HIV-1

642 expression levels in the untreated cells and HIV-1 reactivation in the stimulated cultures were determined

643 using flow cytometric analysis for GFP expression. Similar experiments were performed using a reporter T


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644 cell line that expresses GFP when actively HIV-1 infected (J-R5D4 cells). J-R5D4 T cells were infected with

645 (C) HIV-1 NL4-3 or (D) HIV-1 WEAU, a primary HIV-1 patient isolate. Following six weeks of culture in the

646 presence of RT inhibitors reactivation experiments were performed as in (B) and reactivation levels were

647 determined by flow cytometric analysis for GFP expression. Data plotted as mean ± standard deviation and

648 the significance of the response differences were determined by one-way ANOVA with multiple comparisons.

649

650 Figure 3: Generation of TCR/CD3-inert and -responsive latently HIV-1 infected T cell clones. (A)

651 Representative flow cytometric dot plot analysis of GFP expression as a surrogate marker of HIV-1 expression

652 in the TCR/CD3-responsive JWEAU-A10 T cells and the TCR/CD3-inert JWEAU-C6 T cells at baseline

653 (control) and following activation by -CD3/CD28 mAb or PMA. (B) Expression of CD3 and CD28 on JWEAU-

654 A10 and JWEAU-C6 T cells as determined by flow cytometric analysis. (C) HIV-1 reactivation levels in

655 JWEAU-A10 and JWEAU-C6 T cells following stimulation with increasing concentrations of the PKC activating

656 phorbolester PMA as determined by flow cytometric analysis for GFP expression. (D) HIV-1 reactivation levels

657 in JWEAU-A10 and JWEAU-C6 T cells following stimulation with increasing concentrations of -CD3/CD28

658 mAb as determined by flow cytometric analysis for GFP expression. (E) HIV-1 reactivation levels in JWEAU-

659 A10 and JWEAU-C6 T cells following stimulation with increasing concentrations of different -CD3 mAbs

660 (OKT3, UCHT1, HIT3A) as determined by flow cytometric analysis for GFP expression. (F) Kinetic NF-B p65

661 response in JWEAU-A10 and JWEAU-C6 T cells following stimulation with -CD3/CD28 mAb. (G) HIV-1

662 reactivation levels in JWEAU-A10 and JWEAU-C6 T cells following stimulation with increasing concentrations

663 of the TLR5 agonist flagellin. (H) HIV-1 reactivation levels in JWEAU-A10 and JWEAU-C6 T cells following

664 stimulation with increasing concentrations of flagellin. Where indicated, data represent the mean ± standard

665 deviation of at least three independent experiments. The significance of differences between experimental

666 conditions was determined by one-way ANOVA with multiple comparisons.

667

668 Figure 4: Transcriptomic analysis reveals extensive differences between uninfected and latently HIV-1

669 infected T cells. (A) Pairwise comparison of altered gene expression patterns between the parental Jurkat

670 T cells and each of the latently HIV-1 infected T cell clones was performed with a padj < 0.01, a fold-change

671 2; and the signal for at least one the cell types had to be >250 reads. (B) Pairwise comparison of differentially


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672 expressed genes between JWEAU-A10 T cells and JWEAU-C6 T cells. Gene expression changes are plotted

673 as log2 fold change in JWEAU-C6 T cells relative to gene expression levels in JWEAU-A10 T cells. (C) RNA-

674 seq analysis was used to describe the genome-wide RNA expression signature of uninfected Jurkat T cells,

675 the TCR/CD3 responsive JWEAU-A10 and the TCR/CD3-inert JWEAU-C6 T cells. For each T cell line, RNA-

676 seq data were obtained for low and high cell density conditions. 7,151 genes with differential expression

677 (likelihood ratio test, adjusted P-value < 0.01) across the three cell types were subjected to hierarchical

678 clustering. Subtrees representing genes that were lowly or highly expressed throughout all three cells were

679 automatically omitted through the selection criteria.

680

681 Figure 5: Induction of genetic noise suppresses TCR/CD3 induced HIV-1 reactivation. To determine if

682 transcriptomic noise contributes to the stability of latent HIV-1 infection we pretreated the TCR/CD3

683 responsive JWEAU-A10 and the TCD-CD3 inert JWEAU-C6 T cells with compounds known to induce

684 transcriptomic noise by promoting unspecific transcriptional elongation and then stimulated the cells -

685 CD3/CD28 mAbs. Cells were treated overnight with (A) the HDAC inhibitor SAHA (300 nM), (B) the cell

686 differentiating agent HMBA (2mM), and (C) the BET inhibitor JQ1 (10µM) and then treated with increasing

687 concentrations of -CD3/CD28 mAbs. HIV-1 reactivation levels were determined by flow cytometry for GFP

688 expression 24 hours post CD3/CD28 activation. Data represent the mean ± standard deviation of at least

689 three independent experiments. The significance of differences between experimental conditions was

690 determined by one-way ANOVA with multiple comparisons.

691

692 Figure 6: Pharmacological targeting of network hubs alters TCR/CD3 responsiveness of latently HIV-

693 1 infected T cells. To validate that network analysis correctly predicted and prioritized drug targets that affect

694 the ability of TCR/CD3 stimulation to trigger HIV-1 reactivation we tested the effect of inhibitors against Src,

695 Raf and STAT3 on the ability of a-CD3/CD28 mAbs to trigger HIV-1 reactivation. JWEAU-A10 T cells (A-C)

696 or populations enriched for latently HIV-1 infected T cells (D-F) were incubated overnight with different

697 concentrations of the Src inhibitor dasatinib (A, D), the c-Raf inhibitor sorafenib (B, E), or the STAT3 inhibitor

698 S31-201 (C, F) before stimulation with a sub-optimal concentration of α-CD3/CD28 mAbs. Data represent


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28

699 the mean ± standard deviation of at least three independent experiments. The significance of differences

700 between experimental conditions was determined by one-way ANOVA with multiple comparisons.

701

702 Figure 7: Restoring CD3 responsiveness in latently HIV-1 infected T cells. To test whether

703 pharmacological interventions that were prioritized by network analysis would specifically restore the ability

704 of the CD3 signaling pathway to trigger latent HIV-1 infection in CD3-inert T cells, we stimulated latently HIV-1

705 infected T cell populations with -CD3/CD28 mAbs without any pretreatment or following overnight

706 pretreatment with optimal concentrations of (A) the c-Raf inhibitor sorafenib or (B) the STAT3 inhibitor S31-

707 201 (50 µM). PMA stimulation was used as a positive control to determine the maximum achievable

708 reactivation level in the T cell population. The difference between the reactivation levels accomplishable by

709 -CD3/CD28 mAb activation and PMA stimulation constitutes the TCR/CD3-inert T cell reservoir (CD3 inert).

710 Any increase of HIV-1 reactivation levels above the ability of -CD3/CD28 mAbs to trigger HIV-1 reactivation

711 means that the therapeutic intervention allows antibody stimulation to target previously activation inert latently

712 HIV-1 infected T cells. (C) Using a HIV-GFP reporter virus latently HIV-1 infected T cell populations were

713 established in primary CD4+ T cell populations obtained from five different donors. The T cells were left

714 unstimulated (control) or treated overnight with optimal concentrations of the STAT3 inhibitor S31-201 or the

715 Raf inhibitor sorafenib. Each of these cultures was then split and left either untreated or stimulated with -

716 CD3/CD28 mAbs. HIV-1 reactivation was determined by flow cytometry 72 hours post -CD3/CD28 mAbs

717 addition using GFP expression as a surrogate marker of active HIV-1 expression. The significance of

718 differences between experimental conditions was determined by one-way ANOVA with multiple comparisons.

719

720 Supplemental Figure 1: CD4+ T cells from HIV/ART patients exhibit an altered proliferation response

721 following CD3/TCR stimulation. Proliferation of primary CD4+ T cells from (A) HIV/ART patients or (B)

722 healthy donors measured using CFSE staining reveals a distinct pattern of cellular division representative

723 plots shown (n=11).

724

725 Supplemental Figure 2: Network analysis of kinome data obtained from T cells from HIV/ART patients.

726 Protein interaction networks were generated using T cells from HIV/ART patients in comparison to T cells


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29

727 from healthy donors. The direct protein-protein interaction network was generated using MetaCore software

728 and describes the signal transduction pathways that are altered in HIV/ART patients.

729

730 Supplemental Figure 3: TNF-α triggered HIV-1 reactivation in the latently HIV-1 infected T cell clones.

731 JWEAU-A10 or JWEAU-C6 T cells were stimulated with increasing amounts of TNF-a. HIV-1 reactivation

732 levels were determined by flow cytometric analysis, using GFP expression as a surrogate marker of active

733 HIV-1 expression. Data represent the mean ± standard deviation of at least three independent experiments.

734

735 Supplemental Figure 4: Transcriptomic analysis shows global changes to transcriptional regulation

736 in latently infected cells. Significantly altered RNA signals were used to generate protein-protein

737 interaction networks using the MetaCore direct interaction algorithm. The network visualizes the large

738 amount of unconnected seed nodes, that were likely regulated by bystander effects.

739

740 Supplemental Figure 5: Gene Ontology analysis of distinct clusters within RNA-seq data. Gene

741 ontology analysis of the individual gene clusters in Figure 4C.

742

743 Supplemental Figure 6: Focused analysis of genes involved in TCR/CD3 mediated T cell activation.

744 Expression levels of genes involved in T cell activation were compared between Jurkat T cells and the two

745 latently HIV-1 infected T cell lines, JWEAU-A10 and JWEAU-C6. The gene expression heat map visualizes

746 a general down regulation of genes involved in T cell activation in latently HIV-1 infected T cell clones, but

747 no relevant differences between the TCR/CD3 activation responsive JWEAU-A10 T cells and the activation

748 inert JWEAU-C6 T cells.

749

750 Supplemental Figure 7: Kinome analysis of differentially responsive cell lines generates highly

751 connected protein interaction networks. Protein Interaction networks were generated from significantly

752 altered protein signals between JWEAU-A10 and JWEAU-C6s on the Kinexus kinome chip as previously

753 described.

754


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30

755 Supplemental Figure 8: Protein targets fail to rescue completely CD3 inert latently HIV-1 infected

756 cells: To determine if the inhibitors against Src, Raf and STAT3 were capable of rescuing completely CD3

757 inert latently infected T-cells compounds were titrated on JWEAU-C6 cells prior to overnight stimulation with

758 αCD3/CD28 mABs. Data represent the mean ± standard deviation of at least three independent

759 experiments.


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31

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Extensive proteomic and transcriptomic changes quench the … · 2020-06-29 · 28 population...

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