HSV-1 Single Cell Analysis Reveals Anti-Viral and Developmental Programs 2 Activation in Distinct Sub-Populations

bioRxiv preprint doi: https://doi.org/10.1101/566489; this version posted March 5, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

1 HSV-1 single cell analysis reveals anti-viral and developmental programs 2 activation in distinct sub-populations

3 4 Nir Drayman1,*, Parthiv Patel1, Luke Vistain1, Savaş Tay1,* 5 1 Institute of Molecular Engineering and Institute of Systems Biology, The University of Chicago, Chicago, 6 IL, 60637, USA. 7 Correspondence: [email protected], [email protected] 8 9 ABSTRACT 10 Viral infection is usually studied at the population level by averaging over millions of cells. However, 11 infection at the single-cell level is highly heterogeneous. Here, we combine live-cell imaging and single- 12 cell RNA sequencing to characterize viral and host transcriptional heterogeneity during HSV-1 infection of 13 primary human cells. We find extreme variability in the level of viral gene expression among individually 14 infected cells and show that they cluster into transcriptionally distinct sub-populations. We find that anti- 15 viral signaling is initiated in a rare group of abortively infected cells, while highly infected cells undergo 16 cellular reprogramming to an embryonic-like transcriptional state. This reprogramming involves the 17 recruitment of beta-catenin to the host nucleus and viral replication compartments and is required for late 18 viral gene expression and progeny production. These findings uncover the transcriptional differences in 19 cells with variable infection outcomes and shed new light on the manipulation of host pathways by HSV- 20 1.

22 Keywords: single-cell; RNA-sequencing; virus; HSV-1; Herpes Simplex; anti-viral; development; beta- 23 catenin; WNT; heterogeneity 24

25 INTRODUCTION

27 Viruses are obligatory intracellular parasites that rely on the biochemical functions of their hosts to carry 28 out infection. While usually studied at the level of cell populations, viral infection is inherently a single- 29 cell problem, where the outcome of infection can dramatically differ between genetically identical cells. 30 For example, early studies in the 1940s investigated the burst size of individually infected bacteria and 31 concluded that it both spans three orders of magnitude and cannot be solely attributed to differences in 32 bacteria size (Delbrück, 1945). A later study measured the burst size from individual HeLa cells infected 33 with Herpes Simplex virus 1 (HSV-1) and found that many of the infected cells did not release any progeny, 34 that the variability between individual cells was high and that it did not correlate with the multiplicity of 35 infection (MOI) used (Wildy et al., 1959). More recently, technological improvements have allowed the 36 quantification of burst sizes and infection kinetics of different mammalian viruses, pointing to a high degree 37 of cell-to-cell variability in infection (Zhu et al., 2009; Timm and Yin, 2012; Schulte and Andino, 2014; 38 Combe et al., 2015; Heldt et al., 2015; Cohen and Kobiler, 2016; Guo et al., 2017; Drayman et al., 2017). 39 One well-known source of this variability is the random distribution of the number of viruses that individual 40 cells encounter (Parker, 1938; Smith, 1968; Cohen and Kobiler, 2016). Another source is genetic variability 41 in the virus population, with some virus particles being unable or less fit to establish infection (Huang and 42 Baltimore, 1970; Lauring et al., 2013; Stern et al., 2014).

43 It is becoming clear that a third source of this variability is the host cell state at the time of infection (Snijder 44 et al., 2009, 2012; Drayman et al., 2017). Variability in the host cell state can arise from both deterministic 45 processes such as the cell-cycle and stochastic processes such as mRNA transcription and protein 46 translation (Elowitz et al., 2002; Cohen et al., 2008; Tay et al., 2010; Loewer and Lahav, 2011; Kellogg 47 and Tay, 2015). Recently, the advent of single-cell RNA-sequencing (scRNA-seq) has allowed researchers 48 to examine virus-host interactions in multiple systems, mainly those of RNA viruses (Steuerman et al., 49 2018; Russell et al., 2018; Xin et al., 2018; Zanini et al., 2018; Shnayder et al., 2018). While scRNA-seq is 50 providing a wealth of new information on viral infection, it is currently limited to the characterization of 51 highly abundant transcripts. Thus, it is clear that a better understanding of viral infection requires studies at 52 the single-cell level.

53 HSV-1 is a common human pathogen that belongs to the herpesviridae family and serves as the prototypic 54 virus for studying alpha herpesviruses infection. HSV-1 de novo infection has both lytic and latent phases. 55 In the lytic phase, the virus infects epithelial cells at the site of contact, where it replicates, destroys the host 56 cell and releases viral progeny. The latent phase is restricted to neurons, in which the virus remains silent

57 throughout the host life with occasional reactivation. Here, we focus on the lytic part of the virus life cycle. 58 While lytic infection is usually asymptomatic, in some cases - particularly in immune-compromised 59 individuals and infants - it can results in life threatening conditions such as meningitis and encephalitis.

60 To initiate infection, HSV-1 must bind to its receptors, enter the cytoplasm, travel to the nuclear pore and 61 inject its linear double-stranded DNA into the host nucleus (Kobiler et al., 2012). Once in the nucleus, viral 62 gene expression proceeds in a temporal cascade of three classes of viral genes: immediate-early, early and 63 late (Honess and Roizman, 1974, 1975; Harkness et al., 2014). DNA replication occurs in sub-nuclear 64 structures, called replication compartments (RCs), that aggregate the seven essential replication proteins as 65 well as other viral and host proteins (de Bruyn Kops and Knipe, 1988; Liptak et al., 1996; Weller and Coen, 66 2012; Dembowski and DeLuca, 2015; Dembowski et al., 2017; Reyes et al., 2017; Dembowski and DeLuca, 67 2018). ICP4 is the major viral trans-activator and is required for viral infection to progress beyond the point 68 of immediate-early gene expression. Upon viral DNA replication, ICP4 is predominantly localized in the 69 RCs, with some diffuse nuclear and cytoplasmic localization (Knipe et al., 1987; Zhu and Schaffer, 1995).

70 Several studies applied high-throughput technologies to analyze the cellular response to HSV-1 infection 71 at the population level. RNA sequencing revealed a widespread deregulation of host transcription, including 72 the disruption of transcription termination (Rutkowski et al., 2015; Hennig et al., 2018), activation of anti- 73 sense transcription (Wyler et al., 2017), depletion of RNA-polymerase II from the majority of host genes 74 (Abrisch et al., 2015; Birkenheuer et al., 2018) and changes in splicing and polyadenylation (Hu et al., 75 2016). While most cellular genes are down-regulated by infection, some genes were reported to be up- 76 regulated, including host transcription factors and anti-viral genes (Pasieka et al., 2006; Taddeo et al., 2002; 77 Hu et al., 2016). Proteomics studies have defined the different stages and protein complexes during HSV- 78 1 replication (Dembowski and DeLuca, 2015; Suk and Knipe, 2015; Dembowski et al., 2017; Reyes et al., 79 2017; Dembowski and DeLuca, 2018) as well as the cellular protein response to infection (Kulej et al., 80 2017; Lum et al., 2018).

81 While incredibly informative, population-level analyses suffer in that they average over all the cells in the 82 population. In the case of virus-infected cells, the population is far from homogenous and could in fact 83 contain opposite phenotypes such as highly-infected and abortively-infected cells, leading to contradictory 84 results. One such example is the seemingly complex relation between HSV-1 infection and type I interferon 85 (IFN) signaling. The picture that emerges from population-level measurements is oxymoronic, with wild- 86 type HSV-1 infection both clearly activating (Gianni et al., 2013; Hu et al., 2016; Liu et al., 2016; Reinert 87 et al., 2016) and clearly repressing (Lin et al., 2004; Johnson et al., 2008; Kew et al., 2013; Johnson and 88 Knipe, 2010; Su et al., 2016; Christensen et al., 2016; Manivanh et al., 2017; Yuan et al., 2018; Chiang et

89 al., 2018) the type I IFN pathway. Such discrepancies might be resolved with the use of single-cell 90 measurements.

91 Here, we apply a combination of live-cell time-lapse fluorescent imaging, scRNA-seq and sequencing of 92 sorted cell populations to explore HSV-1 infection at the single-cell level. We find that single cells infected 93 by the virus show variability in all aspects of infection, starting from the initial phenotype (abortive 94 infection vs. successful initiation of viral gene expression), through the timing and rate of viral gene 95 expression and ending with the host cellular response. This study resolves the apparent discrepancy in the 96 literature regarding type I IFN induction and shows that it is restricted to a rare sub-population of abortively- 97 infected cells. Surprisingly, we find that the main transcriptional response in highly-infected cells is the 98 reprogramming of the cell to an embryonic-like state. We focus on the viral activation of the WNT/β- 99 catenin pathway and find that β-catenin is recruited to cell nucleus and the viral RCs and is required for 100 viral gene expression and progeny production. In addition to increasing our understanding of the basic 101 nature of viral infection, these findings could potentially have clinical implications for the use of oncolytic 102 HSV-1 based viruses for cancer treatment.

103 104 RESULTS

105

106 Viral infection dynamics varies among individual cells

107 We began by studying the temporal variability in viral gene expression initiation. To do so, we employed 108 a wild-type HSV-1 (strain 17) that was genetically modified to express ICP4-YFP (Everett et al., 2003). 109 Primary human fibroblasts (HDFn) were infected at a multiplicity of infection (MOI) of 2 and monitored 110 by time-lapse fluorescent microscopy (Fig. 1A, supplementary Movie 1). An MOI of 2 was chosen as it 111 resulted in ~50% of the cells becoming ICP4-positive during primary infection. Note that we determined 112 the genome:PFU ratio for our viral stock and found it to be 36±4, suggesting that all the cells in the culture 113 have likely encountered numerous virus particles.

114 Initiation of ICP4 expression was observed to mostly occur between 1 and 4 hours post-infection (Fig. 1B). 115 Almost no new infections were observed between 4-6 hours, but two infection peaks were later seen at 8 116 and 11 hours. These peaks are likely the result of secondary infections, since new viral progeny can be 117 detected in infected cells starting at 6 hours post-infection (Pomeranz and Blaho, 2000; Ikeda et al., 2011; 118 Drayman et al., 2017). Given that the majority of infected cells have initiated viral gene expression by 5 119 hours, we chose this time point for further analyses. At 5 hours, two cellular populations can be clearly

120 distinguished: cells which successfully initiated viral gene expression (ICP4+) and cells in which infection 121 was aborted (ICP4-). Of 1,814 cells infected with HSV-1, 996 cells (55%) were ICP4+ and 818 (45%) were 122 ICP4-.

123 Among the ICP4+ cells, nuclear levels of ICP4 varied by ~100-fold, ranging from 7x104 to 9x106 AU (Fig. 124 1C). Infected cells showed three distinct phenotypes in regards to ICP4 localization (Fig. 1D). Upon its 125 expression, ICP4 is initially diffuse throughout the cell nucleus. As its level increases, ICP4 forms discrete 126 foci in the nucleus. These are the viral RCs, where viral DNA replication takes place. Later, the levels of 127 cytoplasmic ICP4 increases and interspersed foci can be seen in the cytoplasm. These phenotypes are 128 temporally linked and delineate the progression through infection. As evident by time-lapse microscopy 129 (supplementary Movie 1), individual cells show a high degree of variability not only in the timing of initial 130 gene expression, but also in the rate of infection progression.

131 Taken together, we find that not all infected cells successfully initiate viral gene expression under these 132 experimental conditions. Those that do initiate viral gene expression show variation in the timing of initial 133 gene expression, the rate of infection progression and the level and localization of the immediate-early 134 protein ICP4. These results prompted us to explore cellular heterogeneity on a larger scale by applying 135 single-cell RNA-sequencing (scRNA-seq) to infected cells.

136 Viral gene expression is extremely variable among individual cells

137 HDFn were mock-infected or infected with wild-type or a ICP0 HSV-1 mutant and harvested for scRNA- 138 seq at 5 hours post-infection. We chose to include the ICP0 mutant as it results in a relatively high number 139 of abortive infections and a robust activation of anti-viral responses. For scRNA-seq we applied the Drop- 140 seq protocol (see Methods and (Macosko et al., 2015). Briefly, a microfluidic device was used to 141 encapsulate individual cells in a water-in-oil droplet in which cell lysis, mRNA-capture and barcoding took 142 place. The barcoded mRNA was then recovered from the droplets, reverse-transcribed, amplified and 143 sequenced. Since each cDNA was barcoded with a cell and transcript ID, the sequencing data allows reliable 144 quantification of the number of transcripts in individual cells.

145 Only 0.4% of mock-infected cells had any reads aligned to the HSV-1 genome, with a maximal expression 146 of 2 viral gene counts (0.05% of transcripts). Cells infected with either wt or ICP0 HSV-1 showed extreme 147 cell-to-cell variability in the amount of viral transcripts they express, ranging from 0-36% (Fig. 1E). The 148 viral gene expression distribution was highly skewed, with most cells expressing low levels of viral 149 transcripts and some cells expressing much higher levels (Fig. 1E). The Gini coefficient, a measurement of 150 population inequality ranging from zero (complete equality) to one (complete inequality), was used to 151 evaluate the distribution of viral gene expression among individual cells. The Gini coefficients were 0.8 for

152 wt infection and 0.77 for ICP0, higher than that reported for viral gene expression by Influenza virus 153 (0.64, (Russell et al., 2018)). When wt viral gene expression is visualized in two-dimension (using the tSNE 154 dimensionality reduction technique (Maaten and Hinton, 2008)), two clusters of cells can be seen, 155 distinguished by the amount of viral gene expression (less or more than ~1%, Fig. 1F). A similar distribution 156 was seen for ICP0 infected cells, although there were significantly less cells in the “highly-infected” 157 cluster (Supplementary Fig. 1).

158 To further explore cell-to-cell variability in viral gene expression, we analyzed the relative expression of 159 the four groups of viral transcripts, corresponding to their temporal order of expression: immediate-early 160 (IE), early (E) and late (subdivided into early-late (1) and true-late (2)). We focused on the group of 161 highly infected cells, since the lowly infected cells had too few viral gene counts for accurate analysis. Fig. 162 1G shows the relative expression of the viral gene classes in single cells, ordered from low to high viral 163 gene expression. The fraction of late genes increases as total viral gene expression increases, at the expense 164 of IE and E genes. The correlations between viral gene expression and the four classes of viral transcripts 165 are shown in Fig. 1H-K. Similar observations were made for ICP0 infected cells (Supplementary Fig. 1).

166 Our scRNA-seq data indicates a wide and uneven distribution of viral gene expression during HSV-1 167 infection, with most cells expressing none or low levels of viral gene transcripts and a smaller group 168 expressing much higher levels, in agreement with the ICP4 expression levels presented above. We note that 169 significant cell-to-cell differences are seen even within the group of highly infected cells, with viral gene 170 expression ranging from 1% to >30%, and that this “viral expression load” is correlated with late gene 171 expression.

172 The cell-cycle affects HSV-1 gene expression

173 The effect of the cell cycle on HSV-1 infection was evaluated by calculating a cell-cycle score for each cell 174 in our dataset (Tirosh et al., 2016) and measuring the correlation between HSV-1 gene expression and the 175 cell-cycle score (Supplementary Fig. 2). We found that viral gene expression is negatively correlated with 176 the cell-cycle score, with cells in the later parts of the cell cycle expressing ~10-fold less viral genes than 177 those in the early part of the cycle. This finding is in agreement with previous results, showing that cells in 178 the G2 phase of the cell-cycle are less likely to initiate viral gene expression (Drayman et al., 2017).

179 As the cell-cycle is both a major source of cell-to-cell variability and negatively correlated with viral gene 180 expression, it was crucial to regress out the cell-cycle effect before analyzing the host response 181 (Supplementary Fig. 2). We could now turn to analyze the host genes that are differentially expressed 182 among HSV-1 infected cells, starting with the anti-viral response.

183 The anti-viral program is only detected in a rare sub-population of abortively infected cells

184 As previous population-level studies reported the activation of anti-viral genes during wild-type HSV-1 185 infection, we hypothesized that highly infected cells (Fig 2A,B, cluster 1) should be enriched for anti-viral 186 genes. To our surprise, differential gene expression analysis of the two clusters did not indicate up- 187 regulation of the anti-viral response in cluster 1 (Sup. Table 1). In fact, canonical anti-viral genes such as 188 IFIT2 and IFIT3 were only detected in 2-3% of the cells from both clusters 1 and 2 (Fig. 2C).

189 One possible explanation is that anti-viral genes are indeed expressed in highly infected cells but were not 190 detected by scRNA-seq due to technical limitations. To investigate this, infected cells were FACS-sorted 191 into two populations based on ICP4-YFP expression (ICP4+ and ICP4-) and each population was sequenced. 192 In agreement with the scRNA-seq data, expression of canonical anti-viral genes was not significantly 193 different between mock-infected and ICP4+ cells. Rather, our analysis indicated that a small group of genes, 194 including the anti-viral genes IFIT1 and IFIT2, were specifically up-regulated in the ICP4- population (Fig. 195 2D, Sup. Table 2). The Gene Ontology (GO) biological processes associated with these up-regulated genes 196 included terms such as “response to type I interferon” and “immune response” (Sup. Table 3). QPCR 197 validation of selected transcripts is shown in Fig. 2E.

198 To pinpoint the origin of the anti-viral response cells were stained for IRF3, as IRF3 translocation from the 199 cytoplasm to the nucleus is one of the first steps in type I interferon response. In ICP4+ cells, IRF3 was 200 blocked from entering the nucleus and concentrated in the nuclear periphery (Fig. 2F), while a rare subset 201 of ICP4- cells (<1%) showed nuclear localization of IRF3.

202 We next evaluated the anti-viral response in cells infected by ICP0 HSV-1. ICP0 is a multifunctional viral 203 protein, which blocks IRF3 signaling (Lin et al., 2004). Cells infected with ICP0 clustered into four groups 204 (Fig. 3A). Cluster 1 consists of abortively-infected cells with very few viral transcripts. Clusters 2 and 3 205 have slightly higher viral gene expression and the small cluster 4 consists of highly-infected cells (Fig. 206 3B,D). While the magnitude of the anti-viral response in ICP0-infected cells was much greater than that 207 of wt-infected cells (Fig. 2), it was still only observed in a small population of cells, with ~8% of the cells 208 expressing IFIT1 and MX2 (compared to none of the mock-infected cells). These cells had low viral gene 209 expression levels and belonged to clusters 1-3 (Fig. 3C,D). Anti-viral signaling was not seen in highly- 210 infected cells of cluster 4 (Fig. 3C,D).

211 RNA-sequencing of sorted cells that were infected with ICP0 identified ~80 genes as significantly up- 212 regulated in ICP4- cells compared to mock and ICP4+ cells (Fig. 3E, Supplementary Table 4). These genes 213 were enriched for functional annotations of anti-viral signaling (Fig. 3F, Supplementary Table 5) and 214 binding sites of the transcription factors IRF1, IRF7 and STAT5 (Fig. 3G, Supplementary Table 6). An

215 important difference from wt infection is that, while enriched in the ICP4- population, these anti-viral genes 216 are also activated in the ICP4+ population, albeit to a lesser extent (Fig. 3H). We confirmed this observation 217 through immunofluorescent staining of IRF3 (Fig. 3I). The staining showed a higher proportion of cells 218 with nuclear IRF3 localization compared to wt-infected cells. The majority of cells with nuclear IRF3 were 219 ICP4- but some ICP4+ cells also showed nuclear IRF3 staining. These ICP4+ cells showed diffuse nuclear 220 localization of ICP4, which indicates that infection was aborted prior to the generation of replication 221 compartments (Fig. 1D). The few cells that were able to proceed to the later stages of infection (as indicated 222 by the appearance of replication compartments and cytoplasmic ICP4 foci) showed the same peri-nuclear 223 aggregation of IRF3 as wt-infected cells (Fig. 3I and Fig. 2F).

224 Altogether, sequencing of both single cells and sorted cell populations, as well as immuno-fluorescence 225 staining of IRF3, suggests that the anti-viral program is initiated in a small subset of abortively-infected 226 cells but is blocked in highly infected cells, even in the absence of ICP0. This behavior explains the apparent 227 discrepancy between previous population-level measurements that showed both activation and inhibition 228 of type I interferon signaling during HSV-1 infection.

229 HSV-1 infection results in transcriptional reprogramming of the host to an embryonic-like state

230 We next focused on genes that are up-regulated during HSV-1 infection, in either the scRNA-seq or sorted 231 cell population experiments. 977 genes were significantly up-regulated in wt ICP4+ cells as compared to 232 both ICP4- and mock-infected cells (Fig. 4A, Sup. Table 7). 87 genes were significantly up-regulated in 233 highly infected single cells (Fig. 4B, Sup. Table 1). Remarkably, we found that a major portion of these up- 234 regulated genes are associated with GO terms that concern regulation of RNA transcription and 235 developmental processes (Fig. 4C,D and Sup. Tables 8-9). Similar results were observed in cells infected 236 with ICP0 (Supplementary Fig. 3 and Supplementary Tables 10-15).

237 The promoters of these genes are enriched for binding sites of several transcription factors, including Sp1, 238 MAZ, LEF1 and TCF3 (Fig. 4E,F and Sup. Tables 16-17). 23% of the promoters of the up-regulated genes 239 in ICP4+ cells contained a binding site for the TCF/LEF transcription factors, which are activated by the 240 WNT/-catenin pathway (Sokol, 2011) (Fig. 4G). Note that LEF1 itself is a WNT target gene and is up- 241 regulated during HSV-1 infection (Fig. 5A).

242 Examples of up-regulated genes are shown in Fig. 5A and include canonical WNT target genes such as 243 AXIN2, key developmental genes belonging to the SOX, HOX and HES families, stem-cell associated 244 transcripts such as LGR5 and a multitude of extra-cellular ligands of various developmental pathways, 245 including the WNT, Notch, Hedgehog and TGFsignaling pathways. In agreement with the less efficient 246 infection by ICP0, most of these transcripts are also up-regulated in ICP0-infected ICP4+ cells, but to a

247 lesser degree than in wt-infected cells. Concomitant with the establishment of this embryonic-like 248 transcriptional program, we observed a reduction in the levels of key fibroblast marker-genes, such as 249 1(III) collagen and fibronectin (Fig. 5B).

250 We conclude that cells highly-infected by HSV-1 undergo de-repression of embryonic and developmental 251 genetic programs, including the WNT/-catenin pathway.

252 -catenin translocates to the nucleus and concentrates in the viral replication compartments

253 Since many of the up-regulated genes are known WNT target genes and/or contain LEF/TCF binding sites 254 in their promoters, we investigated the state of -catenin in infected cells. Infected cells were fixed and 255 stained for -catenin at 5 hours post-infection (Fig. 6A). As expected, -catenin was mainly 256 cytoplasmic/membrane-bound in mock-infected cells. In HSV-1 infected cells, -catenin showed three 257 distinct localization patterns: un-perturbed (cytoplasmic), diffuse nuclear or aggregated in nuclear foci (Fig. 258 6A,B). Similar results were obtained for cells infected by ICP0 (Supplementary Fig. 4).

259 At 5 hours post-infection, 37% of the cells were ICP4 negative, 14% were at the earliest stage of infection 260 (diffuse nuclear ICP4), 31% have assembled viral replication compartments and 18% progressed to show 261 cytoplasmic foci of ICP4 (Fig. 5C). ICP4 levels increase from one group to the next, in accordance with 262 the temporal progression of infection (Fig. 6D).

263 -catenin localization was linked to the temporal progression of infection. -catenin remained cytoplasmic 264 in both ICP4-negative cells and cells with diffused nuclear ICP4, translocated to the nucleus upon the 265 generation of the replication compartments and subsequently co-localized with the RCs, but only in cells 266 showing cytoplasmic foci of ICP4 (Fig. 6B,E). A similar phenotype was seen in two epithelial cell-lines: 267 A549, a lung cancer cell-line, and Mel624, a patient-derived melanoma cell-line (Fig. 6F).

268 This analysis indicates that -catenin is indeed co-opted by HSV-1. Cell-to-cell variability in the 269 progression of infection results in heterogeneity of -catenin localization, with recruitment of -catenin to 270 the viral replication compartments occurring at the later stages of infection.

271 -catenin activation is necessary for late viral gene expression and progeny production

272 Since -catenin target genes are activated by the virus, and since -catenin is recruited to the viral 273 replication centers, we hypothesized that -catenin activity is required for the completion of the viral life 274 cycle. We infected cells treated with an inhibitor of -catenin activity, iCRT14 (Gonsalves et al., 2011), 275 and measured viral gene expression at 5 hours post-infection (Fig. 7A-D). Our results indicate that -catenin 276 inhibition had no or minimal impact on immediate-early gene expression (Fig. 7A) but significantly

277 inhibited early and late gene expression (Fig. 7B-D). These observations are in agreement with the late 278 recruitment of -catenin to the viral RCs described above.

279 To measure the impact of -catenin inhibition on viral progeny formation, we treated the cells with iCRT14, 280 infected the cells for 24 hours and harvested and titrated the resulting viral progeny by plaque assay (Fig. 281 7E). In accordance with its impact on late viral gene expression, -catenin inhibition significantly reduced 282 viral progeny formation (Fig. 7F). Similar results were obtained when -catenin was silenced using siRNA 283 (Fig. 7G,H).

284 Taken together, our data show that HSV-1 reprograms the cell to an embryonic-like state, in part through 285 the co-option of -catenin, which is needed for late viral gene expression and progeny production.

286 287 DISCUSSION

288

289 In this study we applied a combination of time-lapse fluorescent imaging, scRNA-seq and sequencing of 290 sorted cell populations to understand HSV-1 infection at the single cell level. We find that single cells 291 infected by the virus show variability in all aspects of infection, starting from the initial phenotype (abortive 292 infection vs. successful initiation of viral gene expression), through the timing and rate of viral gene 293 expression and ending with the host cellular response. Such heterogeneity in the population of infected cells 294 is detrimental when performing population-averaged measurements but can be untangled through single- 295 cell analyses to gain new insights into biological processes.

296 With regard to IFN response, we find that two opposite phenotype exist in the population of infected cells, 297 explaining the discrepancy in the literature. Surprisingly, we find the IFN indication is limited to a small 298 group of abortively infected cells, even in cells infected by the ICP0 mutant (Fig. 2,3). Why only a subset 299 of cell activate the anti-viral program is an intriguing question and several hypotheses come to mind. For 300 example, these cells could be poised for IFN induction due to stochastic variability in expression of the 301 signaling pathway components (Zhao et al., 2012; Patil et al., 2015) or it might be linked to the number of 302 viral particles a cell encounters. We plan to pursue and further characterize these rare cells in future studies.

303 We further found that highly-infected cells undergo transcriptional reprogramming and activate multiple 304 developmental pathways. Focusing on -catenin, we found it localization correspond to distinct stages in 305 HSV-1 infection (Fig. 6): it is first recruited to the cell nucleus and then later to the viral replication 306 compartments. These findings augment a growing body of literature that shows a link between viral

307 infection and the -catenin pathway (reviewed in (van Zuylen et al., 2016)), such as during infections with 308 MCMV (Juranic Lisnic et al., 2013), Influenza (More et al., 2018), HBV (Daud et al., 2017) and Rift Valley 309 Fever virus (Harmon et al., 2016).

310 How HSV-1 infection causes this massive reprogramming of the host cell state is currently unknown, 311 although we can rule out a direct involvement of ICP0, since this reprogramming is also occurring during 312 infection with ICP0, albeit to a lesser extent. While -catenin activation is certainly one part of this, it is 313 likely that the expression of epigenetic regulators is also important. Indeed, a proteomics study of the host 314 chromatin during HSV-1 infection has identified widespread changes in the epigenome landscape of 315 infected cells (Kulej et al., 2017). While we describe here a positive role for -catenin activation during 316 HSV-1 infection, a recent report described an inhibitory role for the germline transcription factor DUX4 317 during HSV-1 infection (Full et al., 2018). Thus, future studies will need to tease apart the differential 318 contributions and effects of different developmental pathways activated during infection.

319 -catenin and other developmental pathways are often found to be mutated or dysregulated during 320 tumorigenesis (Reya and Clevers, 2005; Krausova and Korinek, 2014; Duchartre et al., 2016) and are 321 considered as promising targets for cancer treatment (Takebe et al., 2015). In this context, it is interesting 322 to speculate as to the possible impact of -catenin activation by HSV-1 on its use as an oncolytic agent 323 (Sanchala et al., 2017; Watanabe and Goshima, 2018). Currently, the first-line treatment for late-stage 324 melanoma is immune checkpoint inhibitors (Tracey and Vij, 2019). While these inhibitors revolutionized 325 melanoma treatment, not all patients respond to them. This was shown to be associated with -catenin 326 activity in the tumor, where high -catenin levels negatively correlate with treatment success (Spranger and 327 Gajewski, 2015; Spranger et al., 2015). Given that an HSV-1 based oncolytic therapy has been FDA 328 approved for late-stage melanoma (Pol et al., 2015), it is tempting to speculate that the high level of - 329 catenin in melanomas that are resistant to checkpoint inhibitors would serve to augment oncolytic HSV-1 330 replication and anti-tumor effects - although this of course would have to be carefully assessed in separate 331 studies.

A bioRxiv preprint doi: https://doi.org/10.1101/566489B ; this version posted March 5, 2019.C The copyright holder for this preprintD (which was not HSV-1 certified by peer review) is the author/funder. All rights reserved. No reuse allowed withoutn= 996 permission. (ICP4-YFP) n= 970 cells Uninfected 1 hr

Diffused nuclear 2 hrs Primary human fibroblasts

Replication centers 3 hrs Single cell Live cell n= 1648 RNAseq imaging RC + Cytoplasmic 6 hrs

G E E F % HSV-1 n= 807 n= 1613 Gini= 0.80 Late (g2)

10 Late (g1)

Early 0.1 Immediate -Early n= 4500 0

r= -0.25 r= -0.34 r= 0.40 r= 0.43 p= 3X10-4 p= 8X10-7 p= 4X10-9 p= 2X10-10

Figure 1. Cell-to-cell variability in infection dynamics and viral gene expression. A. HDFn cells were infected with HSV-1 expressing ICP4- YFP and analyzed by time-lapse fluorescent imaging and scRNA-seq. B. Distribution of the initial time of ICP4 expression. The black line denotes the average and the gray shadowing denotes the standard error of the time of ICP4 expression by single cells. n=970 cells from 3 fields of view. C. Violin plots showing the distribution of ICP4 nuclear intensity at 5 hours post infection of mock (n=1648) or HSV-1 infected (n=996) cells. D. Schematic diagram of the different localization phenotypes of ICP4 (left) and a representative single cell showing these phenotypes at different time points following infection. E. Violin plots showing the distribution of the % of viral transcripts (out of the total transcripts) for individual cells which were mock-infected (n=4500), infected with wt (n=807) or DICP0 (n=1613) HSV-1. Y-axis is logarithmic. F. tSNE plot based on viral gene expression in wt HSV-1 infection. Each dot represents a single cell and is colored according to the % of viral transcripts from blue (low) to red (high). Color bar is logarithmic. Gini is the Gini coefficient G. The relative abundance of the four viral gene classes: immediate-early (blue), early (orange), late g1 (yellow) and late g2 (purple) in highly-infected single cells (highlighted by a gray oval in panel F). Single cells are ordered by their % viral transcripts from low (left) to high (right) which is denoted by the black line. H. Scatter plots of single cells showing the % viral transcripts on the x-axis and the relative abundance of each viral gene class on the Y-axis. r and p are the Pearson correlation coefficients and p-values, respectively. bioRxiv preprint doi: https://doi.org/10.1101/566489; this version posted March 5, 2019. The copyright holder for this preprint (which was not ABCcertified by peer review) is the author/funder. All rights reserved. No reuse allowed withoutD permission.mock HSV-1 YFP- HSV-1 % HSV-1 IFIT2 expressed IFIT3 expressed transcripts IFIT2 not expressed IFIT3 not expressed 1 2 1 2 1 2 gene expression gene expression (z-score) 10

CXCL6

1 IFIT1 IFIT2

0.1

EF IRF3 ICP4 (YFP) Merged + DAPI p=0.002 p=0.02 -1 HSV

p=0.003 p=0.02 mock

Figure 2. Anti-viral program is initiated in abortively infected cells. A. tSNE plot based on viral and host gene expression. Cells are colored according to their clustering. Cluster 1 is colored green and cluster 2 is colored purple. B. tSNE plot as in A, cells are colored according to their % of viral transcripts expression. C. tSNE plot as in A, cells are colored according to expression (red) or no expression (gray) of IFIT2 (left) or IFIT3 (right). D. Heat-map of genes which are significantly up-regulated in ICP4- cells, as compared to both mock and ICP4+ cells. RNA-sequencing was performed in duplicates denoted by the numbers 1 and 2 on the top row. Each row shows the normalized (z-score) expression of a single gene, colored from low (blue) to high (red). E. QPCR validation of selected genes. Bar plots showing the expression level of the viral genes ICP4 and gB (top) and the anti-viral genes FIT1 and IFIT2 (bottom). Value are mean±s.e of three independent biological repeats. Individual measurements are shown as circles. p-values were calculated using a one-tailed t-test F. Immunoflorescent staining of IRF3 in mock-infected (bottom) or HSV-1 infected (top) cells at 5 hours post infection. The arrowhead points to an ICP4 negative cell that shown nuclear IRF3 staining (nucleus border denoted by a dashed white line). The arrows point to aggregation of IRF3 outside the nucleus in ICP4 positive cells. in in to to E. C. by the the their in (left), of of up- or the top to -2 ICP0. (z-score) D -2 on both mock 1X10 p=0.03 IFIT1 2 1X10 -3 by to of identified viral and host up-regulated 9X10 and on with cells colored cells infected -3 genes Scatter plots showing A, -3 2X10 in mock-infected (bottom) mock-infected cells (first compared D. in 8X10 -3 in in Transcription factor binding cells infected as 1X10 as G. IRF3 of viral transcripts expression. (right). -sequencing was performed expression (gray) cells are colored according of the numbers 1 cells, p=0.001 of - A, tSNE plot based HSV-1 transcripts (top left) by which are significantly D RNA Transcription factors binding sites binding factors Transcription the nuclear periphery. or no OASL terms enriched for in of in tSNE plot a single gene, colored from low (blue) or anti-viral genes the % GO as cells. each cluster of B. of to F. of genes + or G three independent biological repeats. Individual expression. Cells are colored according (middle) ICP4 of aggregated OASL expressed OASL not expressed OASL 2 -9 is ICP0 mutant. A. ICP0-infected ICP4 s.e 2X10 gene Figure 3. antiD viral signaling according clustering. Heat-map MX the expression expression column) D expression (red) tSNE plots and duplicates denoted high (red). row. Eachexpression row shows the normalized p=0.02 The copyright holder for this preprint (which was not was (which preprint this for holder copyright The Immunoflorescent staining -9 selected genes. Bar plots showing the expression level I. -6 2X10 of -5 1X10 which IRF3 3X10 DAPI * ICP4 negative cells with nuclear IRF3. Asterisks denote ICP4 positive in to -8 -6 terms associated with anti-viral signaling. MX2 expressed MX2 not expressed Value are mean± -4 2X10 the FDR-corrected p-value for its enrichment. Bar length shows the % 3X10 GO is infection, of bar QPCR validation p=3X10 GO biological processes GO biological Merged Merged + IFIT2. H. * each Regulation of cell proliferation Regulationof cell Response to cytokineResponse Immune responseImmune Defenseresponse and Type-I interferon Type-I Defenseto virus yellow are to this version posted March 5, 2019. 2019. 5, March posted version this ; in FIT1 IFIT1 expressed IFIT1 not expressed F panel E. H the late stages * in OAS1 OAS2 OASL in CXCL6 CXCL3 MX2 IFIT3 1 1 0.1 10 0 a cell % HSV- transcripts identified to ICP4 (YFP) and the value next term. Highlighted ICP0 D bar * GO - the anti-viral genes of genes the 5 hours post infection. The arrowheads point the https://doi.org/10.1101/566489 circles. p-values were calculated using a one-tailed t-test at on certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. permission. without allowed No reuse reserved. rights All author/funder. is the review) by peer certified to and doi: doi: as ICP0 YFP ICP0 D gB * written is and the promoters IRF3 mock in 1 2 1 1 2 1 2 1 2 ICP4 bioRxiv preprint preprint bioRxiv

E. The term *

D ICP0 mock

DICP0 infected (top) cells

panel E

I regulated genes annotated

measurements are shown sites enriched

viral genes or

gene expression (z-score) cells with nuclear IRF3. The arrow points ABC A bioRxiv preprint doi: https://doi.org/10.1101/566489; this versionB posted March 5, 2019. The copyright holder for this preprint (which was not mock HSV-1certified YFP by- peerHSV- review)1 is the author/funder. All rights reserved.Cluster No reuse 2 allowed without permission.Cluster 1 gene 1 2 1 2 1 2 (HSV-1 low) (HSV-1 high) z-score 3 SOX11 JAG1

2 HES4

HES6 1

LEF1

MSX1 DLL1 0 WNT11 LGR5 NEFM BMP8A Axin2 -1

BRD2 NEUOROD2 NEUROG1 -2 SOX1 SHH HOXD1 -3 single cells CDGO biological processes GO biological processes (genes up-regulated in cells) (genes up-regulated in cluster 1) G

Regulation of RNA pol II transcription 3X10-39 RNA processing 2X10-11

Positive regulation of gene expression 2X10-39 Reproduction 3X10-5

Tissue development 4X10-42 Cellular response to stress 1X10-3

Positive regulation of biosynthetic process 6X10-35 Establishment of localization in cell 2X10-3

Cell development 2X10-39 Positive regulation of gene expression 2X10-3

Regulation of multicellular organismal development 3X10-32 Regulation of RNA pol II transcription 2X10-3

E Transcription factors binding sites F Transcription factors binding sites (genes up-regulated in cells) (genes up-regulated in cluster 1)

3X10-43 9X10-9

3X10-22 3X10-4

4X10-28 1X10-3

2X10-17 5X10-4

-3 2X10-21 2X10

-3 1X10-15 3X10

Figure 4. HSV-1 infection up-regulates developmental pathways. A. Heat-map of genes which are significantly up-regulated in ICP4+ cells, as compared to both mock and ICP4- cells. RNA-sequencing was performed in duplicates denoted by the numbers 1 and 2 on the top row. Each row shows the normalized (z-score) expression of a single gene, colored from low (blue) to high (red). B. Heat-map of genes which are significantly up-regulated in cluster 1 (highly-infected) compared to cluster 2 (lowly-infected) single cell. Each row shows the normalized (z-score) expression of a single gene, colored from low (blue) to high (red) in 80 single cells (40 from cluster 1 and 40 from cluster 2). Color bar is shared for both panels A and B. C,D. Go terms enriched for genes identified in panels A and B, respectively. The GO term is written on the bar and the value next to each bar is the FDR-corrected p-value for its enrichment. Bar length shows the % of up-regulated genes annotated to the GO term. Highlighted in yellow are GO terms associated with development and gene regulation. E,F. Transcription factors binding sites enriched in the promoters of genes identified in panels A and B, respectively. The value next to each bar is the FDR-corrected p-value for its enrichment. Bar length shows the % of up-regulated genes containing binding sites for each transcription factor. Highlighted in yellow are the LEF1, TCF3 and MYC transcription factors, which are part of the WNT/b-catenin pathway. G. A simplified diagram of the WNT/b-catenin signaling pathway that controls LEF/TCF transcriptional activity. bioRxiv preprint doi: https://doi.org/10.1101/566489; this version posted March 5, 2019. The copyright holder for this preprint (which was not A certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. B WNT target genes Development Stemness Ligands Transcription factors Fibroblast markers

Figure 5. Cellular reprogramming during HSV-1 infection A. Bar plots showing the expression level (TPM - transcripts per million) of selected examples of genes that participate in developmental pathways and are up-regulated in HSV-1 infected cells. Black bars denote mock-infected and ICP4- cells. Green bars denote ICP4+ cells. Value are mean±s.e of the two sequenced biological replicates (Fig. 4A and Supplementary Fig. 3A). B. Bar plots showing the expression level (TPM - transcripts per million) of selected examples of fibroblast marker genes. bioRxiv preprint doi: https://doi.org/10.1101/566489; this version posted March 5, 2019. The copyright holder for this preprint (which was not A certified by peer review) is the author/funder. All rights reserved. NoB reuse allowed without permission. b-catenin ICP4 (YFP) Merged + DAPI b-catenin ICP4 Merged

1 Uninfected mock

2 Diffused nuclear

1 3 Replication -1 2 centers

HSV 3 4 4 RC + Cytoplasmic

-20 C D p=4.3X10-40 E p=2X10 RC + cytoplasmic Replication centers p=0.002 p=2.3X10-37 18%

31% 37% p>0.5

14% p=3.6X10-33

Uninfected Diffused nuclear F G b-catenin ICP4 (YFP) Merged + DAPI b-catenin ICP4 Merged

1 1 A549

2 2

3 3 4 Mel624 4

Figure 6. b-catenin translocates to the nucleus and concentrates in the viral replication compartments. A. Immunoflorescent staining of b-catenin in mock-infected (top) or HSV-1 infected (bottom) HDFn cells at 5 hours post infection. B. magnified imaged of the four cells denoted by dashed white boxes in panel A, showing representative images of cells with different ICP4 and b-catenin localizations. C. quantification of the relative abundances of the different ICP4 localizations (n=204 cells). D,E. Violin plots showing the distributions of ICP4 (D) and b-catenin (E) nuclear levels in cells showing the four ICP4 localization phenotypes. p-values were calculated by a two-tailed two-sample t-test and corrected for multiple comparisons by the Bonferroni correction. F,G. Immunofluorsence as in panels A and B for A549 and Mel624 cells. bioRxiv preprint doi: https://doi.org/10.1101/566489; this version posted March 5, 2019. The copyright holder for this preprint (which was not ABCDcertified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

p=0.051

p=0.001 p=0.008 p=0.006

E F GH p=0.03

p=0.001 DMSO

p=8X10-4 iCRT14

Figure 7. b-catenin is required for late viral gene expression and progeny production. A-D. HDFn treated with the b-catenin inhibitor iCRT14 or with vehicle alone (DMSO) were infected with HSV-1 and analyzed for viral gene expression by RT-PCR at 5 hours post infection. Results show the mean±standard error from three biological repeats, as compared to the DMSO treatment. The genes analyzed were ICP4 (A), ICP8 (B), gB (C) and UL36 (D). Circles are the results of individual experiments. p-values were calculated using a two-tailed one-sample t-test. E. Representative image of a plaque assay, titrating virus generated from a 24 hour infection of HDFn treated with iCRT14 or DMSO. F. Titers were calculated for viral progeny produced from iCRT14 or DMSO treated as in E. bars show the mean±standard error from five biological repeats, as compared to the DMSO treatment. Circles are the results of individual experiments. p-value was calculated using a two-tailed one-sample t-test. G-H. HDFn were treated with control siRNA (siCt) or siRNA targeting b-catenin (sib-cat) for 72 hrs. Cells were assessed for b-catenin expression (G) or infected for 24 hours and viruses harvested and titrated as in panel E (H). bars show the mean±standard error from three biological repeats. p- value was calculated using a two-tailed one-sample t-test. bioRxiv preprint doi: https://doi.org/10.1101/566489; this version posted March 5, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

A B % HSV-1

Gini= 0.77 Late (g2)

10 Late (g1)

Early 0.1

Immediate 0 -Early

r= -0.14 r= -0.15 r= 0.23 r= 0.24 p= 0.056 p= 0.03 p= 9X10-4 p= 6X10-4

Supplementary Figure 1. Cell-to-cell variability in viral gene expression upon DICP0 infection. A tSNE plot based on viral gene expression. Each dot represents a single cell and is colored according to the % of viral transcripts from blue (low) to red (high). Color bar is logarithmic. Gini is the Gini coefficient B. The relative abundance of the four viral gene classes: immediate-early (blue), early (orange), late g1 (yellow) and late g2 (purple) in highly-infected single cells (highlighted by a gray oval in panel A). Single cells are ordered by their % viral transcripts from low (left) to high (right) which is denoted by the black line. C. Scatter plots of single cells showing the % viral transcripts on the x-axis and the relative abundance of each viral gene class on the Y-axis. r and p are the Pearson correlation coefficients and p-values, respectively. bioRxiv preprint doi: https://doi.org/10.1101/566489; this version posted March 5, 2019. The copyright holder for this preprint (which was not A certified by peerBefore review) cell-cycle is the author/funder. regression All rights reserved. No reuse allowed without permission. % HSV-1 G2/M Cluster 1 transcripts score

0.1

After cell-cycle regression B % HSV-1 G2/M Cluster 1 transcripts score

0.1

Cluster 2 + Cluster 3 0

C D wt DICP0

r= -0.09 , p= 7X10-3 r= -0.12 , p= 2X10-7

Supplementary Figure 2. Cell-cycle is anti-correlated with viral gene expression and is a major source of transcriptional variability. A. tSNE plots based on viral and host gene expression (wt infection). Left - Cells are colored according to their clustering. Cluster 1 is colored blue, cluster 2 is colored green and cluster 3 is colored brown. Middle - Cells are colored based on the % of viral transcripts they express from blue (low) to red (high). Color bar is logarithmic. Right - Cells are colored based on cell-cycle score, from blue (low) to red (high). . B. tSNE plots as in panel A, showing cell clustering (left), viral gene expression (middle) and cell-cycle score (right) after regressing out the cell-cycle effect on gene expression. C,D. Cell-cycle score is anti-correlated with wt (C) and DICP0 (D) viral transcription. r and p are the Pearson correlation coefficient and associated p-value, respectively A bioRxiv preprint doi: https://doi.org/10.1101/566489; this versionB posted March 5, 2019. The copyright holder for this preprint (which was not mock DcertifiedICP0 YFP by- peerD review)ICP0 is the author/funder. All rights reserved.Cluster No 1reuse allowed withoutCluster permission. 4 1 2 1 2 1 2 (DICP0 low) (DICP0 high)

single cells

C GO biological processes D GO biological processes (genes up-regulated in cells) (genes up-regulated in Cluster 4)

-16 Positive regulation of gene expression 5X10 Negative regulation of nitrogen metabolism 3X10-12

-15 Positive regulation of biosynthetic process 7X10 Response to stress 5X10-12

-13 Regulation of RNA pol II transcription 8X10 Regulation of RNA pol II transcription 3X10-10

Cell development 2X10-14 Negative regulation of gene expression 9X10-11

Tissue development 2X10-13 Positive regulation of gene expression 1X10-9

Response to external stimulus 8X10-11 Positive regulation of biosynthetic process 2X10-9

E F Transcription factors binding sites Transcription factors binding sites (genes up-regulated in cells) (genes up-regulated in Cluster 4)

5X10-18 4X10-15

3X10-12 7X10-16

-13 9X10-16 1X10

9X10-7 1X10-13

3X10-7 5X10-9

2X10-12 2X10-11

Supplementary Figure 3. DICP0 infection up-regulates developmental pathways. A. Heat-map of genes which are significantly up-regulated in ICP4+ cells, as compared to both mock and ICP4- cells. RNA-sequencing was performed in duplicates denoted by the numbers 1 and 2 on the top row. Each row shows the normalized (z-score) expression of a single gene, colored from low (blue) to high (red). B. Heat-map of genes which are significantly up- regulated in cluster 4 (highly-infected) compared to cluster 1 (lowly-infected) single cell. Each row shows the normalized (z-score) expression of a single gene, colored from low (blue) to high (red) in 80 single cells (40 from cluster 4 and 40 from cluster 1). C,D. Go terms enriched for genes identified in panels A and B, respectively. The GO term is written on the bar and the value next to each bar is the FDR-corrected p-value for its enrichment. Bar length shows the % of up-regulated genes annotated to the GO term. Highlighted in yellow are GO terms associated with development and gene regulation. E,F. Transcription factors binding sites enriched in the promoters of genes identified in panels A and B, respectively. The value next to each bar is the FDR- corrected p-value for its enrichment. Bar length shows the % of up-regulated genes containing binding sites for each transcription factor. Highlighted in yellow are the LEF1 and TCF3 transcription factors, which are part of the WNT/b-catenin pathway. bioRxiv preprint doi: https://doi.org/10.1101/566489; this version posted March 5, 2019. The copyright holder for this preprint (which was not A certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. b-catenin ICP4 Merged

Cell 1 Cell 2

Cell 3

Cell 4

B b-catenin ICP4 Merged b-catenin ICP4 Merged Cell 1 Cell 3 Cell 2 Cell 4

Supplementary Figure 4. b-catenin translocates to the nucleus and concentrates in the viral replication compartments upon DICP0 infection. A. Immunofluorescent staining of b-catenin in DICP0 infected HDFn cells at 5 hours post infection. B. magnified imaged of the four cells denoted by dashed white boxes in panel A, showing representative images of cells with different ICP4 and b-catenin localizations. Note that DICP0 infection results in more abortive infections than wt infection, manifesting in many ICP4+ cells which show diffused nuclear localization, unable to form replication compartments. bioRxiv preprint doi: https://doi.org/10.1101/566489; this version posted March 5, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

340 MATERIALS AND METHODS

341 Cells, viruses and inhibitors

342 Primary neonatal human dermal fibroblast (HDFn) were purchased from Cascade Biologics (cat #C0045C), 343 grown and maintained in medium 106 (Cascade Biologics, cat #M106500) supplemental with Low Serum 344 Growth Supplement (Cascade Biologics, cat #S00310). Cells were maintained for up to eight passages, and 345 experiments were performed on cells between passages 4-7. A549 cells were purchased from Sigma- 346 Aldrich and maintained in DMEM supplemented with 10% fetal bovine serum. Mel624, a patient-derived 347 melanoma cell-line, was obtained from the lab of Professor Thomas Gajewski at the Univeristy of Chicago 348 and maintained in RPMI supplemented with HEPES, NEAA, Pen/Strep and 10% fetal bovine serum. Vero 349 and U2OS cells (obtained from the lab of Matthew D. Weitzman, University of Pennsylvania) grown in 350 DMEM supplemented with 10% fetal bovine serum were used for viral propagation and titration. wt and 351 ICP0 HSV-1 (strain 17) expressing ICP4-YFP were generated by Roger Everett (Everett et al., 2003) and 352 were a kind gift from Matthew D. Weitzman. Viral stock was prepared by infecting Vero cells (for wt virus) 353 or U2OS cells (for ICP0) at an MOI of 0.01 and harvesting viral progeny 2-3 days later using 3 cycles of 354 freezing and thawing. Viral stock was titrated by plaque assay, aliquoted and stored at -80C. iCRT14, a - 355 catenin inhibitor was purchased from Sigma-Aldrich (cat #SML0203) and dissolved in DMSO.

356 Time lapse fluorescent imaging

357 HDFn cells were seeded on 6-well plates and allowed to attach and grow for one day. On the day of the 358 experiment, cells were counted and infected with HSV-1 at an MOI of 2. Cells were washed once with 106 359 medium without supplements and virus was added in the same media at a final volume of 300l per well. 360 Virus was allowed to adsorb to cells for one hour at 37C with occasional agitation to avoid cell drying. The 361 inoculum was aspirated and 2ml of full growth media was added and this point was considered as “time 362 zero.” Cells were imaged in a Nikon Ti-Eclipse, which was equipped with a humidity and temperature 363 control chamber. Images were acquired every 15 minutes for 24 hours from multiple fields of view. Image 364 analysis was performed with ImageJ and MATLAB.

365 Single-cell RNA-sequencing

366 HDFn infected with HSV-1 at an MOI of 2 were harvested at 5 hours post-infection and washed three times 367 in PBS containing 0.01% BSA. Cells were counted and processed according to the Drop-seq protocol 368 (Macosko et al., 2015) in the Genomics facility core at the University of Chicago. Sequencing was 369 performed on the Illumina NextSeq500 platform. Preliminary data analysis (quality control, trimming of 370 adaptor sequences, UMI and cell barcode extraction) was performed on a Linux platform using the Drop-

371 seq Tools (Version 1.13) and Drop-seq Alignment Cookbook (Version 1.2), available at 372 https://github.com/broadinstitute/Drop-seq/releases. Alignment of reads was performed using the STAR 373 aligner (Version 2.5.4b, (Dobin et al., 2013) to a concatenated version of the human (GRCh38 primary 374 assembly, Gencode release 27) and HSV-1 genomes (Genbank accession: JN555585). The HSV-1 genome 375 annotation file was kindly provided by Moriah Szpara (Penn State University). Following the generation of 376 the DGE (digital gene expression) file, further analyses were performed in MATLAB and included quality 377 control, cell clustering, correlation and differential gene expression analyses and data visualization. All the 378 scripts used for data analysis and visualization are available upon request.

379 RNA-sequencing of sorted cells

380 HDFn cells were mock or HSV-1 infected at an MOI of 2, trypsinized, washed and re-suspended in full 381 growth media. Cells were filtered through a 100m mesh into FACS sorting tubes and kept on ice. HSV-1 382 infected cells were sorted into two populations based on ICP4-YFP expression. 0.5 million cells were 383 collected from each population. Mock-infected cells were similarly sorted. ICP4 negative cells had the same 384 level of YFP fluorescence at mock-infected cells. For ICP4 positive cells, we collected cells that were in 385 the top 30% of YFP expression. The two populations were clearly separated from each other. Sorting was 386 performed on an AriaFusion FACS machine (BD) at the University of Chicago flow-cytometry core facility. 387 Total RNA was extracted from cells using the RNeasy Plus Mini Kit (QIAGEN) and submitted to The 388 University of Chicago Genomics core for library preparation and sequencing on a HiSeq4000 platform 389 (Illumina). Reads were mapped to a concatenated version of the human and HSV-1 genomes with STAR 390 aligner (see single-cell RNA-sequencing above for details). Reads were counted using the featureCounts 391 command, which is a part of the Subread package (Liao et al., 2013). Further analyses were performed in 392 MATLAB and included differential gene expression analyses and data visualization. All the scripts used 393 for data analysis and visualization are available upon request.

394 Sequencing data availability

395 All sequencing data has been deposited in the Gene Expression Omnibus (GEO) under accession number 396 GSE126042.

397 Immunofluorescence staining

398 HDFn were seeded in 24-well plates and allowed to attach and grow for one day. Cells were infected as 399 above and fixed using a 4% paraformaldehyde solution at 5 hours post-infection. Cells were fixed for 15 400 minutes at room temperature and washed, blocked and permeabilized with a 10% BSA, 0.5% Triton-X 401 solution in PBS for one hour. Cells were then incubated with primary antibodies in a staining solution (2%

402 BSA, 0.1% Triton-X in PBS) overnight at 4C. Cells were washed three times with PBS, incubated with 403 secondary antibodies in staining solution for 1 hour at room temperature, washed three times with PBS and 404 covered with 1 ml PBS containing a 1:10,000 dilution of Hoechst 33342 (Invitrogen, cat #H3570). Cells 405 were imaged on a Nikon Ti-Eclipse inverted epi-fluorescent microscope. Primary antibodies were mouse 406 monoclonal anti--catenin (R&D systems, cat #MAB13291, used at 1:200 dilution) and rabbit monoclonal 407 anti-IRF3 (Cell Signaling Technologies, Cat #11904S, used at 1:400 dilution). Secondary antibodies were 408 AlexaFluor 555 conjugated anti-mouse and anti-rabbit F(ab’)2 fragments (Cell Signaling Technologies, cat 409 #4409S, #4413S, used at 1:1,000 dilution).

410 siRNA nucleofection

411 5X105 HDFn were washed once in PBS and nucleofected with 1µM siRNA against β-catenin 412 (Dharmacon, siGENOME Human CTNNB1, cat #M-003482-00-0005) or a scarmbeld siRNA control 413 (Dharmacon siGENOME Non-Targeting siRNA Pool #1, cat #D-001206-13-05) using the Human 414 Dermal Fibroblast NucleofectorTM Kit (Lonza, cat #VPD-1001). β-catenin expression was assayed 3 days 415 later by Q-PCR.

416

417 ACKNOWLEDGMENTS

418 We wish to thank Matthew D Weitzman for sharing with us the ICP4-YFP expressing HSV-1 and Moriah 419 Szpara for the genome annotation of the HSV-1 strain 17. Sequencing and library preparations were 420 performed at The University of Chicago Genomics core facility and cell sorting at the Flow Cytometry 421 core. We also with to thank Oren Kobiler for support and advice throughout the project. ND wishes to thank 422 EMBO and HFSPO for their support through post-doctoral fellowships at different stages of the project.

423

424 AUTHOR CONTRIBUTIONS

425 ND and ST designed the experiments, ND, PP and LV performed experiments, ND preformed data analysis, 426 ND and ST wrote the manuscript.

427

428 DECLARATION OF INTERESTS

429 No conflicts of interests exist

430

431 REFERENCES

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