SARS-Cov-2 Restructures the Host Chromatin Architecture 2

bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

1 SARS-CoV-2 Restructures the Host Chromatin Architecture 2

3 Authors: Ruoyu Wang 1,2,†, Joo-Hyung Lee 1,†, Feng Xiong 1, Jieun Kim 3,4, Lana Al Hasani 1,2, Xiaoyi 4 Yuan 3,4, Pooja Shivshankar 1,3,4, Joanna Krakowiak 1, Chuangye Qi 1, Yanyu Wang 3,4, Holger K. 5 Eltzschig 2,3,4, Wenbo Li 1,2,*

7 Affiliations:

8 1 Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas 9 Health Science Center, Houston, 77030, TX, USA

10 2 Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center and 11 UTHealth, Houston, 77030, TX, USA

12 3 Department of Anesthesiology, McGovern Medical School, University of Texas Health Science Center, 13 Houston, 77030, TX, USA

14 4 Center for Perioperative Medicine, McGovern Medical School, University of Texas Health Science 15 Center, Houston, 77030, TX, USA

17 † These authors contributed equally

18 * Correspondence: Wenbo Li, Ph.D. ([email protected]). MSB 6.161, Fannin Street, Houston, 19 Texas, 77030, USA; Tel: 713-500-6103.

1 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

20 ABSTRACT:

21 SARS-CoV-2 has made >190-million infections worldwide, thus it is pivotal to understand the viral 22 impacts on host cells. Many viruses can significantly alter host chromatin1, but such roles of SARS-CoV- 23 2 are largely unknown. Here, we characterized the three-dimensional (3D) genome architecture and 24 epigenome landscapes in human cells after SARS-CoV-2 infection, revealing remarkable restructuring of 25 host chromatin architecture. High-resolution Hi-C 3.0 uncovered widespread A compartmental weakening 26 and A-B mixing, together with a global reduction of intra-TAD chromatin contacts. The cohesin complex, 27 a central organizer of the 3D genome, was significantly depleted from intra-TAD regions, supporting that 28 SARS-CoV-2 disrupts cohesin loop extrusion. Calibrated ChIP-Seq verified chromatin restructuring by 29 SARS-CoV-2 that is particularly manifested by a pervasive reduction of euchromatin modifications. Built 30 on the rewired 3D genome/epigenome maps, a modified activity-by-contact model2 highlights the 31 transcriptional weakening of antiviral interferon response genes or virus sensors (e.g., DDX58) incurred 32 by SARS-CoV-2. In contrast, pro-inflammatory genes (e.g. IL-6) high in severe infections were uniquely 33 regulated by augmented H3K4me3 at their promoters. These findings illustrate how SARS-CoV-2 rewires 34 host chromatin architecture to confer immunological gene deregulation, laying a foundation to 35 characterize the long-term epigenomic impacts of this virus. 36

2 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

37 MAIN:

38 The COVID-19 pandemic poses an unprecedented challenge to humankind. Mechanistic understanding of 39 how SARS-CoV-2 impacts cellular functions is critical to solving this challenge. Control of host 40 chromatin has been used by other viruses to antagonize host defense or to exert long-term influences 1,3, 41 which, however, is largely unknown for SARS-CoV-2.

43 The folding of mammalian chromatin in three-dimension (3D) is an outstanding but incompletely 44 understood process in biology4. Several layers of architectures are now observed that together influence 45 critical nuclear processes such as gene transcription, replication, recombination, or DNA damage repair4– 46 6. These architectures include A/B compartments, Topological Associating Domains (TADs), and 47 chromatin loops5,7 (Extended Data Fig.1a,b). A/B compartments are suggested to be formed, at least in 48 part, via homotypic attractions between chromatin regions of similar epigenetic features7,8. For TADs and 49 loops, CCCTC-binding factor (CTCF) and the cohesin complex are two main regulators6. Growing 50 evidence supports that cohesin acts via a “loop extrusion” process by which it actively translocates on 51 chromatin to create DNA contacts inside TADs, until it encounters specific CTCF sites and was blocked 52 from further extrusion6 (Extended Data Fig.1a,b). These two mechanisms can crosstalk - the formation 53 of TADs by loop extrusion appears to antagonize compartmentalization6,7,9,10. How 3D chromatin 54 architectures are rewired in pathological conditions remains poorly understood.

56 Widespread restructuring of the host 3D genome 57 To explore how SARS-CoV-2 may impact the host 3D genome, we conducted viral infection in A549 58 cells (of human alveolar epithelial cell origin) expressing Angiotensin-converting enzyme 2 (ACE2) 59 receptor (A549-ACE2) (Fig.1a, Extended Data Fig.1c), and first generated ribo-depleted total RNA-Seq 60 at two time-points post infection (see data summary in Extended Data Table 1). A 24-hour infection 61 (multiplicity of infection or MOI: 0.1) resulted in ~90% of RNA-Seq reads aligned to the viral genome, 62 indicating a high level of infection (Extended Data Fig.1d), in agreement with previous observations11. 63 Shorter term infection for 6-hr resulted in ~20% RNA-Seq reads attributed to the virus genome, 64 suggesting insufficient viral infection/replication (Extended Data Fig.1d)11. We therefore focused on 65 cells 24hr-post-infection (24hpi) to characterize the viral impacts on the host chromatin. Consistent with 66 RNA-Seq, immunofluorescence of the spike protein verified a high ratio of viral infection at 24hpi 67 (Extended Data Fig.1e).

3 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

69 We employed Hi-C 3.012, a recent version of in situ Hi-C that revised cross-linking procedure and 70 included two restriction enzymes, which can detect high-resolution 3D chromatin architecture at both 71 long and short distances. We generated biological replicates in A549-ACE2 cells at 24hpi or in mock- 72 infected cells (Mock), and deeply sequenced the Hi-C 3.0 libraries to ~2.6 billion read pairs for mock and 73 infected conditions (Extended Data Table 1), generating ~630-770 millions unique contact pairs for each 74 condition. There is high concordance between Hi-C 3.0 replicates as demonstrated by the stratum- 75 adjusted correlation coefficients (SCC)13 (Extended Data Fig. 2a,b), and an example region is shown in 76 Extended Data Fig. 2c. We thus combined the replicates for the analysis below, and still refer to these 77 data as Hi-C.

79 Hi-C revealed a strikingly widespread alteration of the 3D genome in SARS-CoV-2 infected cells. 80 Evidently, the near-diagonal value of the contact matrix that denotes short range chromatin interactions 81 was weakened globally, as exemplified by a 30Mb region on chromosome 9 (Fig.1b, black arrowheads). 82 Whereas the contacts far away from the diagonal, which are long-distance chromatin interactions, were 83 often deregulated (increased or decreased for different regions, green box) (Fig.1b). Pearson correlation 84 map of Hi-C interactions consistently revealed these changes (Fig.1c), which also suggests alteration of 85 chromatin compartmentalization (see below). Zoom-in view of an example region of ~0.7Mb showed that 86 chromatin domains (“rectangles” with high-frequency cis interactions, Extended Data Fig.1a,b) are often 87 weakened (Fig.1d), whereas chromatin loops (“dot” structures off the diagonal, Extended Data Fig.1a,b) 88 are deregulated (Fig.1d). A P(s) curve shows the frequency (P) of intra-chromosomal interaction ranked 89 by genomic distance (s) (Fig.1e), demonstrating that SARS-CoV-2 elicited a global reduction of short 90 distance chromatin contact (< 560kb), a moderate increase of mid-to-long distance interactions (~560k- 91 8.9Mb), and enhanced interactions for far-separated regions (>28Mb). Intriguingly, at the inter- 92 chromosomal levels, trans-chromosomal contacts were generally increased by viral infection, as shown by 93 the fold changes of pairwise interactions between any two chromosomes, or by calculating the ratios of 94 trans-versus-cis chromosomal contacts in Hi-C data (Extended Data Fig. 2d,e). The enhancement of both 95 inter-chromosomal interactions and the extremely long-distance (>28Mb) intra-chromosomal interactions 96 (Fig.1e) suggest changes in chromatin compartmentalization (see below). At such large scales, inter- and 97 intra-chromosomal interactions can display some shared properties9,14.

99 Defective compartmentalization, A weakening, and A-B mixing 100 Principal component analysis (PCA) of Hi-C maps can divide 3D genomes into A/B compartments, 101 which largely overlap the euchromatin and heterochromatin, respectively7.15. Regions in each

4 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

102 compartment preferentially interact within the same compartment7,15(Extended Data Fig.1a,b). 103 Analyzing a 100kb-binned Hi-C contact matrix, we found significant defects of chromatin 104 compartmentalization in SARS-CoV-2 infected cells (Fig.2a). Overall, compartmental scores (E1) 105 exhibited a general reduction in virus infected cells (i.e., moving below the diagonal, Fig.2a), suggesting 106 a pervasive weakening of A compartment and/or A-to-B switching. By using a E1 score change of 0.2 as 107 cutoff (see Methods16), we found that ~30% of genomic regions exhibited compartmental weakening or 108 switching after viral infection (Extended Data Fig. 3a). Consistent with the general trend of E1 109 reduction, compartmental changes commonly display features of weakened A (e.g. A to weaker A, or A 110 to B) or strengthening of B compartment (B to stronger B). Among these, A-to-weaker-A change is the 111 most common (~18% of all genomic bins, Fig.2a and Extended Data Fig. 3a). Example regions are 112 shown in Fig.2b.

113

114 We evaluated the epigenetic features of the regions prone to compartmental changes incurred by the virus. 115 By ranking each genomic bin based on its E1 score change, we sorted them into six categories (Fig.2c). 116 For those showing E1 increase, we dubbed them “A-ing” bins and those showing decrease “B-ing”. We 117 then examined the enrichment of both active and repressive histone marks on these six categories of bins. 118 Genomic regions originally harbored higher active chromatin marks known to feature A compartment 119 (such as H3K27ac) were found to become more B-like (“B-ing” bins) after viral infection; whereas the 120 genomic regions that were originally higher in repressive histone marks known to feature B compartment 121 (particularly H3K27me3) become more A-like (“A-ing” bins) by SARS-CoV-2 (Fig. 2d). RNA 122 polymerase II (Pol2) exhibits similar enrichment as that of H3K27ac mark (Fig. 2d). These results 123 suggest that both the originally A or B compartments were losing their identity, indicating defective 124 chromatin compartmentalization.

125

126 Indeed, defects of chromatin compartmentalization can be clearly detected in the example region shown 127 in Fig.1c, in which the originally strong separation between A and B compartmental regions become 128 “fuzzier”. Inter-compartmental interactions formed between regions of A and B increased, whereas those 129 formed homotypically within A or B reduced (Fig.1c). A saddle plot demonstrated that such changes are 130 global after virus infection (Fig.2e, see Methods8,10). There is a strong reduction of A-A homotypic 131 interactions accompanied by gain of A-B mixing, while B-B interactions were not obviously changed 132 (Fig.2e). Similarly, by calculating another compartmentalization metric, the sliding correlation scores (SC 133 score9, Extended Data Fig. 3b, see Methods), we found that SC scores were not affected at boundaries 134 where A/B compartments transition to the other, but were globally decreased for regions inside the same

5 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

135 compartments. This result reinforces the conclusion that SARS-CoV-2 weakened host chromatin 136 compartmentalization. Increased A-B mixing (and hence weakened compartmentalization) can also be 137 seen at very large scales in between two chromosomes, e.g. chromosomes 17 and 18, where originally 138 well-separated A-A/B-B homotypic interactions were significantly compromised but A-B interactions 139 globally enhanced (Extended Data Fig. 3c).

140 141 Viral reprogramming of host epigenome and H3K27ac reduction 142 A-compartment in general enriches active histone modifications, while B-compartment repressive and 143 heterochromatin marks15. To mechanistically understand compartmentalization changes, we 144 systematically profiled epigenomic landscapes in Mock or SARS-CoV-2 infected cells by ChIP-Seq of 145 active and repressive histone modifications (H3K4me3, H3K27ac, H3K9me3, and H3K27me3) (Fig. 1a). 146 Western blots showed that most of these modifications remain unaltered, but surprisingly, active histone 147 mark H3K27ac displayed a consistent and significant reduction after SARS-CoV-2 infection (Fig. 2f). 148 We therefore conducted spike-in calibrated ChIP-Seq to precisely quantify epigenomic changes (see 149 Methods17, Extended Data Fig.4a). By calculating the ratio of ChIP-Seq reads aligned to the human 150 versus to the mouse genome (i.e., spike-in), we consistently observed a reduction of H3K27ac on the host 151 chromatin by ~40-45% (Extended Data Fig.4b,d), which agrees with the changes in western blots 152 (Fig.2f). There is no overall change of another active mark H3K4me3 (Extended Data Fig.4c,e), but 153 there are moderate gains of repressive histone marks, particularly the H3K9me3, after infection 154 (Extended Data Fig.4c,f,g). 155 156 The epigenome reprogramming after SARS-CoV-2 infection resonates with the pervasive decrease of A- 157 A compartmental interactions and weakening of A compartment in Hi-C data (Fig.2a,e). We examined 158 histone mark changes on the six categories of genomic regions that bear differential compartmental 159 changes (Fig.2c), finding that the most weakened A compartment (“B-ing”) regions are associated with 160 stronger reduction of H3K27ac (Fig.2g), whereas heterochromatin mark H3K9me3 displays the opposite 161 trend (Fig.2h). An example is shown in Extended Data Fig.4h, where H3K27ac reduced and H3K9me3 162 increased, correlating with compartmental changes: weakened A-A contacts and increased A-B inter- 163 compartmental mixing. Because attractions between homotypic chromatin regions were suggested to be 164 an important basis for compartmentalization7,9,10, our results support the notion that SARS-CoV-2 165 disrupted host chromatin compartmentalization, at least in part, via reprogramming chromatin 166 modifications. 167 168 Pervasive weakening of intra-TAD interactions

6 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

169 We then examined chromatin architectures at finer scales (10kb~1Mb), namely TADs and chromatin 170 loops5–7. A very pronounced phenomenon as shown by example regions in Fig.1d and Fig. 3a is that cis- 171 interactions within TADs were significantly reduced by virus infection, whereas the contacts beyond 172 TADs (out of the rectangle) are unchanged or increased. Examining this phenomenon genome-widely, we 173 calculated the insulation scores (IS)18 on our Hi-C maps and identified 4,094 TADs (see Methods). 174 Aggregation Domain Analyses (ADA) of these TADs verified the significant weakening of intra-TAD 175 cis-interactions by SARS-CoV-2, which accompanies unchanged or increased cis-interactions outside of 176 TADs (Fig. 3b). Quantification of all intra-TAD contacts showed dramatic reduction (Fig.3c). 177 Interestingly, weakened intra-TAD contacts were not accompanied by severe loss of TAD identities, i.e. 178 the boundaries of TADs were largely unchanged (Fig. 3a,b middle panel). Indeed, insulation scores on 179 TAD boundaries were only mildly affected by SARS-CoV-2 (Fig.3d).

180

181 Cohesin depletion from intra-TAD regions 182 To understand the drastic diminishing of intra-TAD interactions, we examined the chromatin binding of 183 CTCF and cohesin, the main organizers of TADs5–7 by calibrated ChIP-Seq. These factors were not 184 affected by 24hpi infection at protein levels (Extended Data Fig.5a). In accord, spike-in calibration of 185 ChIP-seq showed no significant global change of their chromatin binding (Extended Data Figs.4a,5b). 186 Peak calling for each factor results in ~40,000~60,000 peaks (Extended Data Fig.5c,e,g). Importantly, 187 for two cohesin subunits, 40.4% (27,152/67,140) of RAD21 sites and 31.8% (20,837/65,379) of SMC3 188 sites were significantly reduced after SARS-CoV-2 infection (Extended Data Fig.5c,d,e,f see Methods), 189 indicating a dramatic depletion of cohesin from chromatin. In contrast, only a small percentage of their 190 binding sizes were gained, i.e., 2.2% (1,510/67,140) for RAD21 sites and 3.1% (2,034/65,379) for SMC3 191 (Extended Data Fig.5c,e). Moderate changes of CTCF binding were observed, with 10.6% 192 (4,853/45,530) sites lost and 10.0% (4,555/45,530) sites gained due to SARS-CoV-2 (Extended Data 193 Fig.5g,h). These changes can be seen in an example TAD region (Fig.3e) whose Hi-C map was shown in 194 Fig.3a. We divided all TADs into six categories based on their sensitivity to SARS-CoV-2 infection (i.e., 195 quantitative reduction of intra-TAD contacts, Extended Data Fig.6a), and examined the chromatin 196 features of these categories. Notably, the more virus-sensitive the TADs are (i.e., more weakening of 197 intra-TAD contacts), the higher enrichment of cohesin and NIPBL they bear (Fig. 3f), suggesting that 198 intra-TAD weakening is due to cohesin depletion in these TADs. Indeed, as compared to the least 199 weakened TADs, the top 10% most virus-weakened TADs are associated with a more dramatic loss of 200 cohesin from the intra-TADs regions (Fig.3g). In addition, cohesin loss was more dramatic at the intra- 201 TAD regions, although was detectable at the TAD boundaries (Fig.3e,g). As comparisons, CTCF was

7 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

202 minimally impacted either at TAD boundaries or intra-TADs (Fig.3e,g), indicating that SARS-CoV-2 203 preferentially disrupts cohesin action inside TADs, but largely leaves intact the TAD structures. 204 205 Besides cohesin, virus-sensitive TADs were originally more enriched in active chromatin mark H3K27ac 206 and RNA Pol2 (RPB1), but possess comparable heterochromatin marks as compared to other TADs 207 (Fig.3f). We thus examined the epigenetic changes of these TADs after infection, finding that for 208 H3K27ac, whereas its level was globally reduced (Fig.2f), the reduction was consistent among all 209 categories of TADs (Extended Data Fig. 6b). In contrast, H3K9me3, but not another heterochromatin 210 mark H3K27me3, was gained significantly more in the most weakened TADs (Extended Data Fig. 211 6c,d). This unique correlation suggests that H3K9me3 increase may play a role in cohesin depletion and 212 intra-TAD weakening caused by SARS-CoV-2. 213

214 SARS-CoV-2 mildly impacted dot-shaped chromatin loops

215 Dot-shaped chromatin loop is a prominent feature of Hi-C data that often forms between convergent 216 CTCF sites7,15. The definition and functions of chromatin loops can be debatable7, for example, enhancer- 217 promoter contacts may be defined as loops by other work or methods but often do not appear as dots in 218 Hi-C5,7. Some work defined dot-shaped loops in Hi-C largely as structural loops that may not be 219 regulatory for gene transcription19. In this study, we refer to dot-shaped structures as chromatin loops, and 220 define enhancer-promoter contacts using Hi-C interaction strength (see Methods). Deep Hi-C 3.0 data 221 permitted us to detect 11,926 loops in Mock or SARS-CoV-2 infected cells at 5~10 kb resolution by 222 HICCUPS, which outnumbers loops often detectable by conventional in situ Hi-C9,12. Some example 223 regions are shown below in Fig. 4b and Extended Data Fig. 8d,e. At a global scale, aggregation peak 224 analyses (APA) showed that chromatin loops were not overtly affected by viral infection (Extended Data 225 Fig. 7a). By quantitative changes (Hi-C contacts change FDR<0.1, see Methods), we found that 2.96% 226 (353/11,926) loops were weakened by 24hpi, whereas 4.70% (560/11,926) loops gained strength 227 (Extended Data Fig. 7b,c). The weakened loops are mostly short-range loops (median size 150kb), while 228 interestingly the gained ones are much longer (median size 417.5kb, Extended Data Fig. 7d). This is 229 consistent with the P(s) curve (Fig. 1e) that long distance chromatin interactions were enhanced by virus 230 infection. As compared to virus-strengthened loops, the anchors of weakened short loops showed more 231 dramatic cohesin depletion (Extended Data Fig. 7e), and are more preferentially located intra-TADs than 232 on boundaries (Extended Data Fig. 7f,g). These results are consistent with the pervasive reduction of 233 intra-TAD interactions (Fig.3b,c), suggesting that the virus-weakened short-range loops are a 234 consequence of defective cohesin loop extrusion inside TADs. That the majority of loops are unaffected is

8 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

235 consistent with that TAD boundaries were largely spared (Fig.3b,d), where cohesin binding was mildly 236 affected (Fig.3e,g). 237 238 Restructured chromatin architecture explains immuno-pathological changes

239 COVID-19 patients with severe symptoms often show two key immuno-pathological features: a 240 delayed/weakened innate immune antiviral response (i.e., type-I interferon response) together with an 241 exacerbated production of pro-inflammatory cytokines (e.g., IL-6)20. Our maps of 3D genome and 242 epigenome provided an opportunity to mechanistically understand the deregulation of these genes 243 incurred by the virus. Our RNA Pol2 (RPB1) ChIP-Seq and RNA-Seq in 24hpi SARS-CoV-2 infected 244 cells recapitulated these two immuno-pathological features, indicating that immunological gene alteration 245 occurs transcriptionally (Pol2 in Fig.4a, RNA-Seq in Extended Data Fig. 8a). As compared to 246 interferon-beta (IFN-b) stimulus, SARS-CoV-2 infection elicited very limited activation of interferon 247 response genes (hereafter IFN genes) but strong increases of proinflammatory genes (hereafter PIF 248 genes)(Fig.4a). Key examples include IFIT1/2/3/5, DDX58 (a.k.a. RIG-I) or IFIH1 (a.k.a. MDA5) for IFN 249 genes, and IL-6 or CXCL8 for the PIF genes (Extended Data Fig. 8b,c). Inspection of IFN gene loci 250 revealed remarkable changes of chromatin architecture by viral infection: 1), the dot-shaped loops were 251 often diminished and the chromatin contacts (which include most enhancer-promoter contacts) throughout 252 the hosting TAD were reduced; 2), cohesin occupancy within the hosting TADs was decreased; 3), the 253 active mark H3K27ac displayed significant reductions at many putative enhancers near IFN genes (Fig. 254 4b, Extended Data Fig. 8d,e). Both enhancer-promoter contacts and enhancer activities may contribute 255 to gene transcriptional outputs2 (Extended Data Fig. 1a,b), we thus modified and applied the activity-by- 256 contact (ABC)2 algorithm to model how chromatin architecture and epigenome changes underlie virus 257 rewiring of host transcription (Fig.4c, also see Methods). Combining alteration of enhancer activity and 258 enhancer-promoter contact strength correctly modeled the weakened transcriptional outputs of IFN genes 259 (Pearson’s correlation coefficient R=0.61, Fig.4d). In contrast, poor prediction was achieved if only one 260 of the two features was considered (contact only: R=0.13 or enhancer activity only: 0.33; Extended Data 261 Fig. 8f,g). We experimentally tested the functions of these enhancers by CRISPRi (dCas9-KRAB-MeCP2 262 and gRNAs) in two IFN gene loci where they were weakened by SARS-CoV-2, finding that IFN genes 263 were indeed transcriptionally inhibited from responding to poly(I:C), a synthetic dsRNA simulates virus 264 infection (Extended Data Fig. 8h,i). These results demonstrated that weakened 3D chromatin contacts 265 and enhancer activity together shaped the transcriptional inhibition of IFN genes after SARS-CoV-2 266 infection.

267

9 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

268 However, for PIF genes, while ABC scores correlated with their changes, their true transcriptional levels 269 after SARS-CoV-2 infection were often several-fold higher than ABC scores (Extended Data Fig. 9a, 270 examples in Extended Data Fig. 9b). Indeed, as shown for IL-6 and CXCL8 (IL-8) loci, coding for key 271 clinically detrimental cytokines, enhancer activities (H3K27ac) and intra-TAD contacts were reduced for 272 these genes (Fig. 4e, Extended Data Fig. 9c), albeit they were strongly up-regulated by virus infection 273 (Extended Data Fig. 9b). We therefore re-examined our epigenome datasets at the PIF gene loci, finding 274 a unique and dramatic gain of H3K4me3 mark at their promoters (Fig. 4e, Extended Data Fig. 9c), 275 which did not take place on IFN genes (Fig. 4b, Extended Data Fig. 8d,e). At a genome-wide scale, 276 H3K4me3 exhibited relatively limited changes by SARS-CoV-2: 5.8% (1,843/31,761) sites showed 277 increases and 3.5% (1,104/31,761) sites decreases (Extended Data Fig. 9d). Interestingly, the genes 278 close to gained H3K4me3 sites are specifically enriched for TNF-alpha pathway, TGF-beta signaling, and 279 inflammatory response (Extended Data Fig. 9e). Thus, we revised the ABC algorithm by including 280 H3K4me3 changes at PIF gene promoters (Fig.4c), and this revised ABC-P2 score better modeled their 281 strong transcriptional induction (Extended Data Fig. 9b, Fig. 4f, see Methods). Intriguingly, virus- 282 augmented promoters (with H3K4me3 increases) display motif enrichment of specific transcriptional 283 factors, such as IRF1/2 or Jun/AP1 (Extended Data Fig. 9f). These results together suggest that 284 inflammatory genes are induced by SARS-CoV-2 through a unique mechanism to augment their promoter 285 strength (H3K4me3), even when their enhancer and enhancer-promoter contacts were weakened.

286 287 DISCUSSION:

288 Here, we mapped high-resolution landscapes of the 3D genome and epigenome in cells of human lung 289 epithelial origin after acute SARS-CoV-2 infection, and our results revealed dramatic viral restructuring 290 of the host chromatin. Hi-C 3.0 data uncovered significant defects of chromatin compartmentalization and 291 TAD structures (Fig.4g). These are manifested at the compartment levels as wide-spread A weakening 292 and A-B mixing, and at the TAD levels as global reduction of intra-TAD chromatin contacts. These 3D 293 genome changes, to our knowledge, represent a unique and previously unappreciated rewiring of the 294 mammalian 3D genome in pathological settings.

295

296 Notably, the cohesin complex was depleted, in a pervasive but also selective manner, from intra-TAD 297 regions by SARS-CoV-2. Such changes of cohesin not only provide a molecular explanation to the 298 weakening of intra-TAD chromatin contacts, but also support a notion that cohesin extrusion defects 299 inside TADs released these chromatin regions to engage in long-distance chromatin associations (Fig.4g). 300 Indeed, virus infected chromatin displayed a higher frequency of extremely long distance intra-

10 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

301 chromosomal as well as inter-chromosomal interactions (Fig.1e and Extended Data Fig.2d). Gains of 302 long distance chromatin interactions have been observed after genetic depletion of cohesin subunits from 303 human or mouse cells, supporting that active loop extrusion prevents chromatin mixing due to homotypic 304 attraction6,9,10. Two critical features highlight that SARS-CoV-2 infection disrupts both the epigenome 305 and cohesin loop extrusion, resulting in 3D genome changes in a manner more complex than genetic 306 depletion of cohesin: 1), the augmented compartmental interactions after SARS-CoV-2 infection are 307 mostly formed inter-compartmentally (i.e., between A-B), whereas those after cohesin depletion were 308 mostly formed intra-compartmentally (e.g., between A-A); 2), SARS-CoV-2 infection significantly 309 ablates cohesin from intra-TAD regions, but affects them mildly at TAD boundaries. The first feature can 310 be explained by a global weakening of euchromatin modification after virus infection. Indeed, virus- 311 elicited decreases of active mark H3K27ac and increases of heterochromatin mark H3K9me3 correlate 312 with the weakening of A compartment. In contrast, histone modifications were largely unchanged by 313 genetic depletion of cohesin9. We propose that weakened euchromatin after SARS-CoV-2 infection on 314 one hand reduced A compartment strength and A-A interactions, and on the other blurred the distinction 315 between A and B compartments, contributing to wide-spread A-B mixing. This is consistent with the 316 notion that homotypic chromatin attractions, once disrupted, can compromise compartmentalization7,8. 317 For the second feature, the mechanisms underlying preferential cohesin depletion from intra-TAD regions 318 remain to be determined. We found that TADs most sensitive to SARS-CoV-2 are originally of higher 319 enrichment of cohesin loader NIPBL, suggesting a plausible scenario that this virus utilizes specific 320 mechanisms to perturb cohesin loading in these TADs. Alternatively, the cohesin release process may 321 also be perturbed, which affects the loading-release equilibrium of cohesin to allow its preferential loss 322 inside TADs6.

323

324 An important insight from our work is that architectural restructuring of host chromatin underlies the 325 dichotomic transcriptional changes of immunological genes seen in COVID-19 pathology: weakened IFN 326 responses accompanying increased pro-inflammatory gene expression20. Weakening of enhancer- 327 promoter contacts together with reduced enhancer activity correlates well with inhibited IFN gene 328 transcription, and was validated by CRISPRi experiments. These changes take place at critical disease 329 loci, including genes coding for key RNA virus sensors, DDX58 (coding for RIG-I), whose inhibition is 330 required for successful infection21. Unexpectedly, we found that SARS-CoV-2 directly upregulates the 331 promoter activities of pro-inflammatory genes, suggesting unappreciated regulators on these promoters to 332 confer inflammatory phenotypes in COVID-19 patients20. Further studies are warranted to understand 333 such promoter-centered PIF transcriptional activation, which may offer therapeutic targets to relieve 334 lethal inflammation in patients.

11 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

335

336 Epigenetic alteration is known to exert long term effects in affecting gene expression or cellular 337 phenotypes22,23. Understanding the viral alteration of host chromatin not only provides new knowledge 338 and strategies to fight SARS-CoV-2, but also paves way for further studies of the epigenetic disturbance 339 in patients suffering long COVID24.

340

12 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

341 Contribution: 342 W.L. conceived the project. Wet lab: J.-H.L., and R.W. did most of the experiments, with help from F.X., 343 L.A.H., and J.K. J.Kim, P.S, X.Y., J.Y.W. and H.K.E. generated SARS-CoV-2 virus and conducted 344 human cell infections. Dry lab: R.W. Manuscript: W.L., R.W. and J.-H.L. wrote the manuscript, and 345 received inputs from all authors. 346 347 Acknowledgements: 348 W.L. is a Cancer Prevention and Research Institute of Texas (CPRIT) Scholar. This work is supported by 349 funding from the University of Texas McGovern Medical School, NIH ‘‘4D Nucleome’’ program 350 (U01HL156059), NCI (K22CA204468), NIGMS (R21GM132778, R01GM136922), CPRIT (RR160083, 351 RP180734), Welch foundation (AU-2000-20190330) and the John S. Dunn foundation to W.L, as well as 352 from NIH (R01HL154720, R01DK122796, R01DK109574, R01HL133900) and Department of Defense 353 (W81XWH2110032) to H.K.E.. J.-H.L. is a recipient of a UTHealth Innovation for Cancer Prevention 354 Research Training Program Post-doctoral Fellowship (RP160015). R.W. is a recipient of a John and 355 Rebekah Harper Fellowship in Biomedical Sciences (to MD Anderson and UTHealth Graduate School). 356 Most of our next generation sequencing work was conducted with UTHealth Cancer Genomics Core, 357 which received funding from CPRIT (RP180734). 358 359 Disclosure 360 The content is solely the responsibility of the authors and does not necessarily represent the official views 361 of the Cancer Prevention and Research Institute of Texas. 362 363 Conflict of Interests: N.A.

364 Contact for Reagent and Resource Sharing

365 All requests for information, reagents and resources should be directed to the Lead Contact: Wenbo Li, 366 Ph.D., ([email protected]).

367 368 Data availability statement 369 Data generated in this study have been deposited to NCBI GEO (GSE179184, 370 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE179184). 371 372 Code availability statement 373 Analyses of Hi-C, ChIP-Seq and RNA-Seq in this study were performed by using published 374 softwares or codes as described in the Methods part. 375

13 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

376 References 377 1. Liu, X., Hong, T., Parameswaran, S., Ernst, K., Marazzi, I., Weirauch, M. T. & Bass, J. I. F. Human 378 Virus Transcriptional Regulators. Cell 182, 24–37 (2020). 379 2. Fulco, C. P., Nasser, J., Jones, T. R., Munson, G., Bergman, D. T., Subramanian, V., Grossman, S. R., 380 Anyoha, R., Doughty, B. R., Patwardhan, T. A., Nguyen, T. H., Kane, M., Perez, E. M., Durand, N. C., 381 Lareau, C. A., Stamenova, E. K., Aiden, E. L., Lander, E. S. & Engreitz, J. M. Activity-by-contact model 382 of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat Genet 51, 1664–1669 383 (2019). 384 3. Heinz, S., Texari, L., Hayes, M. G. B., Urbanowski, M., Chang, M. W., Givarkes, N., Rialdi, A., 385 White, K. M., Albrecht, R. A., Pache, L., Marazzi, I., García-Sastre, A., Shaw, M. L. & Benner, C. 386 Transcription Elongation Can Affect Genome 3D Structure. Cell 174, 1522-1536.e22 (2018). 387 4. Dekker, J., Marti-Renom, M. A. & Mirny, L. A. Exploring the three-dimensional organization of 388 genomes: interpreting chromatin interaction data. Nat Rev Genet 14, 390–403 (2013). 389 5. Kempfer, R. & Pombo, A. Methods for mapping 3D chromosome architecture. Nat Rev Genet 21, 207– 390 226 (2020). 391 6. Davidson, I. F. & Peters, J.-M. Genome folding through loop extrusion by SMC complexes. Nat Rev 392 Mol Cell Bio 1–20 (2021). doi:10.1038/s41580-021-00349-7 393 7. Mirny, L. A., Imakaev, M. & Abdennur, N. Two major mechanisms of chromosome organization. Curr 394 Opin Cell Biol 58, 142–152 (2019). 395 8. Belaghzal, H., Borrman, T., Stephens, A. D., Lafontaine, D. L., Venev, S. V., Weng, Z., Marko, J. F. & 396 Dekker, J. Liquid chromatin Hi-C characterizes compartment-dependent chromatin interaction dynamics. 397 Nat Genet 1–12 (2021). doi:10.1038/s41588-021-00784-4 398 9. Rao, S. S. P., Huang, S.-C., Hilaire, B. G. S., Engreitz, J. M., Perez, E. M., Kieffer-Kwon, K.-R., 399 Sanborn, A. L., Johnstone, S. E., Bascom, G. D., Bochkov, I. D., Huang, X., Shamim, M. S., Shin, J., 400 Turner, D., Ye, Z., Omer, A. D., Robinson, J. T., Schlick, T., Bernstein, B. E., Casellas, R., Lander, E. S. 401 & Aiden, E. L. Cohesin Loss Eliminates All Loop Domains. Cell 171, 305-320.e24 (2017). 402 10. Schwarzer, W., Abdennur, N., Goloborodko, A., Pekowska, A., Fudenberg, G., Loe-Mie, Y., Fonseca, 403 N. A., Huber, W., Haering, C. H., Mirny, L. & Spitz, F. Two independent modes of chromatin 404 organization revealed by cohesin removal. Nature 551, 51 (2017). 405 11. Blanco-Melo, D., Nilsson-Payant, B. E., Liu, W.-C., Uhl, S., Hoagland, D., Møller, R., Jordan, T. X., 406 Oishi, K., Panis, M., Sachs, D., Wang, T. T., Schwartz, R. E., Lim, J. K., Albrecht, R. A. & tenOever, B. 407 R. Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19. Cell 181, 1036- 408 1045.e9 (2020). 409 12. Oksuz, B. A., Yang, L., Abraham, S., Venev, S. V., Krietenstein, N., Parsi, K. M., Ozadam, H., 410 Oomen, M. E., Nand, A., Mao, H., Genga, R. M. J., Maehr, R., Rando, O., Mirny, L., Gibcus, J. H. & 411 Dekker, J. Systematic evaluation of chromosome conformation capture assays. BioRxiv. 412 doi:10.1101/2020.12.26.424448 413 13. Yang, T., Zhang, F., Yardımcı, G. G., Song, F., Hardison, R. C., Noble, W. S., Yue, F. & Li, Q. 414 HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. 415 Genome Res 27, 1939–1949 (2017). 416 14. Su, J.-H., Zheng, P., Kinrot, S. S., Bintu, B. & Zhuang, X. Genome-Scale Imaging of the 3D 417 Organization and Transcriptional Activity of Chromatin. Cell (2020). doi:10.1016/j.cell.2020.07.032 418 15. Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., 419 Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S. & Aiden, E. L. A 3D Map of the Human Genome 420 at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell 159, 1665–1680 (2014).

14 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

421 16. Yusufova, N., Kloetgen, A., Teater, M., Osunsade, A., Camarillo, J. M., Chin, C. R., Doane, A. S., 422 Venters, B. J., Portillo-Ledesma, S., Conway, J., Phillip, J. M., Elemento, O., Scott, D. W., Béguelin, W., 423 Licht, J. D., Kelleher, N. L., Staudt, L. M., Skoultchi, A. I., Keogh, M.-C., Apostolou, E., Mason, C. E., 424 Imielinski, M., Schlick, T., David, Y., Tsirigos, A., Allis, C. D., Soshnev, A. A., Cesarman, E. & 425 Melnick, A. M. Histone H1 loss drives lymphoma by disrupting 3D chromatin architecture. Nature 1–7 426 (2020). doi:10.1038/s41586-020-3017-y 427 17. Liu, N. Q., Maresca, M., Brand, T. van den, Braccioli, L., Schijns, M. M. G. A., Teunissen, H., 428 Bruneau, B. G., Nora, E. P. & Wit, E. de. WAPL maintains a cohesin loading cycle to preserve cell-type- 429 specific distal gene regulation. Nat Genet 1–10 (2020). doi:10.1038/s41588-020-00744-4 430 18. Crane, E., Bian, Q., McCord, R. P., Lajoie, B. R., Wheeler, B. S., Ralston, E. J., Uzawa, S., Dekker, J. 431 & Meyer, B. J. Condensin-driven remodelling of X chromosome topology during dosage compensation. 432 Nature 523, 240 (2015). 433 19. Zhang, H., Emerson, D. J., Gilgenast, T. G., Titus, K. R., Lan, Y., Huang, P., Zhang, D., Wang, H., 434 Keller, C. A., Giardine, B., Hardison, R. C., Phillips-Cremins, J. E. & Blobel, G. A. Chromatin structure 435 dynamics during the mitosis-to-G1 phase transition. Nature 576, 158–162 (2019). 436 20. Carvalho, T., Krammer, F. & Iwasaki, A. The first 12 months of COVID-19: a timeline of 437 immunological insights. Nat Rev Immunol 21, 245–256 (2021). 438 21. Yamada, T., Sato, S., Sotoyama, Y., Orba, Y., Sawa, H., Yamauchi, H., Sasaki, M. & Takaoka, A. 439 RIG-I triggers a signaling-abortive anti-SARS-CoV-2 defense in human lung cells. Nat Immunol 1–9 440 (2021). doi:10.1038/s41590-021-00942-0 441 22. Cavalli, G. & Heard, E. Advances in epigenetics link genetics to the environment and disease. Nature 442 571, 489 (2019). 443 23. Mantovani, A. & Netea, M. G. Trained Innate Immunity, Epigenetics, and Covid-19. New Engl J Med 444 383, 1078–1080 (2020). 445 24. Nalbandian, A., Sehgal, K., Gupta, A., Madhavan, M. V., McGroder, C., Stevens, J. S., Cook, J. R., 446 Nordvig, A. S., Shalev, D., Sehrawat, T. S., Ahluwalia, N., Bikdeli, B., Dietz, D., Der-Nigoghossian, C., 447 Liyanage-Don, N., Rosner, G. F., Bernstein, E. J., Mohan, S., Beckley, A. A., Seres, D. S., Choueiri, T. 448 K., Uriel, N., Ausiello, J. C., Accili, D., Freedberg, D. E., Baldwin, M., Schwartz, A., Brodie, D., Garcia, 449 C. K., Elkind, M. S. V., Connors, J. M., Bilezikian, J. P., Landry, D. W. & Wan, E. Y. Post-acute 450 COVID-19 syndrome. Nat Med 27, 601–615 (2021). 451 25. Ahn, J. H., Davis, E. S., Daugird, T. A., Zhao, S., Quiroga, I. Y., Uryu, H., Li, J., Storey, A. J., Tsai, 452 Y.-H., Keeley, D. P., Mackintosh, S. G., Edmondson, R. D., Byrum, S. D., Cai, L., Tackett, A. J., Zheng, 453 D., Legant, W. R., Phanstiel, D. H. & Wang, G. G. Phase separation drives aberrant chromatin looping 454 and cancer development. Nature 1–5 (2021). doi:10.1038/s41586-021-03662-5 455 26. Lee, J.-H., Wang, R., Xiong, F., Krakowiak, J., Liao, Z., Nguyen, P. T., Moroz-Omori, E. V., Shao, J., 456 Zhu, X., Bolt, M. J., Wu, H., Singh, P. K., Bi, M., Shi, C. J., Jamal, N., Li, G., Mistry, R., Jung, S. Y., 457 Tsai, K.-L., Ferreron, J. C., Stossi, F., Caflisch, A., Liu, Z., Mancini, M. A. & Li, W. Enhancer RNA 458 m6A Methylation Facilitates Transcriptional Condensate Formation and Gene Activation. Mol Cell, in 459 press. 460 27. Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M. & 461 Gingeras, T. R. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). 462 28. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., 463 Durbin, R. & Subgroup, 1000 Genome Project Data Processing. The Sequence Alignment/Map format 464 and SAMtools. Bioinformatics 25, 2078–2079 (2009). 465 29. Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for 466 exploring deep-sequencing data. Nucleic Acids Res 42, W187–W191 (2014).

15 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

467 30. Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., 468 Myers, R. M., Brown, M., Li, W. & Liu, X. S. Model-based Analysis of ChIP-Seq (MACS). Genome Biol 469 9, R137 (2008). 470 31. Servant, N., Varoquaux, N., Lajoie, B. R., Viara, E., Chen, C.-J., Vert, J.-P., Heard, E., Dekker, J. & 471 Barillot, E. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol 16, 259 472 (2015). 473 32. Durand, N. C., Shamim, M. S., Machol, I., Rao, S. S. P., Huntley, M. H., Lander, E. S. & Aiden, E. L. 474 Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments. Cell Syst 3, 95– 475 98 (2016). 476 33. Abdennur, N. & Mirny, L. A. Cooler: scalable storage for Hi-C data and other genomically labeled 477 arrays. Bioinformatics (2019). doi:10.1093/bioinformatics/btz540 478 34. Flyamer, I. M., Illingworth, R. S. & Bickmore, W. A. Coolpup.py: versatile pile-up analysis of Hi-C 479 data. Bioinformatics 36, 2980–2985 (2020). 480 481 482

16 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Figure 1 560kb 8.9 Mb 28Mb

−2 a e 10

−3 0.1 MOI 24hpi 10

−4 10

3D Genome / Hi-C 3.0 −5

SARS-CoV-2 Contact Frequency 10 Mock Me Ac Ac −6 SARS-CoV-2 Me Me 10 Mock 0.5 Epigenome/calibrated ChIP-Seq 0.0 A549-ACE2

log2 FC −0.5

5 7 10 10 Distance (s,bp)

b Mock chr9 (100-130Mb) SARS-CoV-2 SARS-CoV-2/Mock

−3 −3 0 10 10

−2 log2 FC Hi-C contact map

0.2 0.5 0.5

0.0 0.0 0.0

−0.2

Pearson correlation map −0.5 −0.5 SARS-CoV-2 - Mock

100 Mb 105 Mb 110 Mb 115 Mb 120 Mb 125 Mb 130 Mb 100 Mb 105 Mb 110 Mb 115 Mb 120 Mb 125 Mb 130 Mb 100 Mb 105 Mb 110 Mb 115 Mb 120 Mb 125 Mb 130 Mb 1 1 0 0 E1 −1 E1 −1

d zoom-in chr9 chr9 95.7 Mb 95.7 Mb 95.7 Mb 2 95.8 Mb 95.8 Mb 95.8 Mb

−2 −2 1 95.9 Mb 10 95.9 Mb 10 95.9 Mb 96 Mb 96 Mb 96 Mb 0 96.1 Mb 96.1 Mb 96.1 Mb

96.2 Mb −3 96.2 Mb −3 96.2 Mb log2 FC 10 10 −1 96.3 Mb 96.3 Mb 96.3 Mb

96.4 Mb 96.4 Mb 96.4 Mb −2

96 Mb 96 Mb 96 Mb 95.7 Mb95.8 Mb95.9 Mb 96.1 Mb96.2 Mb96.3 Mb96.4 Mb 95.7 Mb95.8 Mb95.9 Mb 96.1 Mb96.2 Mb96.3 Mb96.4 Mb 95.7 Mb95.8 Mb95.9 Mb 96.1 Mb96.2 Mb96.3 Mb96.4 Mb bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

483 Figure legends 484 485 Fig. 1. SARS-CoV-2 significantly restructured the host 3D genome. 486 a. A diagram illustrating the experimental design of this work. 487 b. Snapshots of normalized contact matrices of Hi-C 3.0 at an example region (chr9:100-130Mb, hg19) in 488 Mock or SARS-CoV-2 infected conditions. Log2 fold change of Hi-C contact is shown to the right. Black 489 arrowheads point to reduced short-distance interactions along the diagonal. Green boxes show altered 490 compartmentalization. 491 c. Snapshots of Pearson correlation matrices of Hi-C 3.0 in the same region as panel b. Changes of the 492 correlation matrices after infection are shown to the right. Arrowheads point to regions with altered 493 Pearson correlation matrices and are changed A-B (black) or A-A (red) compartmental interactions. 494 d. Zoom-in Hi-C snapshots of a 700kb region in panel b,c (chr9:95.7-96.4Mb, hg19). Pink and green 495 arrowheads show changed or unchanged domains and dot-shaped chromatin loops. 496 e. (Top) P(s) curve showing the relationship between contact frequency (P) and distance (s) of both mock 497 (grey) and SARS-CoV-2 (red) Hi-C datasets. (Bottom) Log2 fold changes of contact frequency ranked by 498 distances, with dotted lines marking the crossing points of the two curves. 499

17 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Figure 2 b a compartment change A->B chr9 q31.2 q31.3 q32 109.0M 110.0M 111.0M 112.0M 113.0M 114.0M 115.0M 116.0M 117.0M 2 A-> weaker A 1.5 B-> stronger B Mock rep1 B->A -1.5 1 1.5 B->weaker B Mock rep2 A-> stronger A 0 -1.5 1.5 SARS-CoV-2 −1 rep1 -1.5 1.5 SARS-CoV-2 −2 rep2 -1.5 SARS-CoV-2 compartment score

−2 −1 0 1 2 Mock compartment score (E1) Epigenetic Features of comparments c d affected by SARS-CoV-2 PCA E1 score changes (SARS-CoV-2 / Mock) H3K27ac 1 H3K4me3 E1 RPB1 0 E1 “A-ing” “B-ing” (Log2 ) H3K9me3 −1 H3K27me3

Top 5% 50~90% Fold enrichment of features 10%~50% Top 5~10% Bottom 5% Bottom 5~10% “A-ing” “B-ing” Top 5% 50~90% 10%~50% Top 5~10% Bottom 5% Bottom 5~10%

e Mock SARS-CoV-2 SARS-CoV-2 / Mock

0 0 B-B 2 × 10 B-B 2 × 10 B-B 0.75

0.50 PCA E1 score 0.25

0 0 10 10 0.00

−0.25 Log2 FC O/E Hi-C contact O/E Hi-C contact

−1 −1 −0.50 6 × 10 6 × 10 −0.5 A-B A-A A-B A-A A-B A-A PCA E1 score

H3K27ac changes H3K9me3 changes f Rep #1 Rep #2 g h 3 1.5 2 1.0 1

0 0.5 Mock SARS-CoV-2 Mock SARS-CoV-2 Total −1 Histone H3 0.0 H3K27Ac −2

SARS-CoV-2 / Mock −0.5 SARS-CoV-2 / Mock log2 FC of calibrated ChIP-Seq

− log2 FC of calibrated ChIP-Seq H3K9me3

H3K4me3

Top 5% 50~90% Top 5% 50~90% 10%~50% 10%~50% H3K27me3 Top 5~10% Bottom 5% Top 5~10% Bottom 5% “A-ing” Bottom “B-ing”5~10% “A-ing” Bottom 5~10%“B-ing” bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

500 Fig. 2. Compartmental A weakening, A-B mixing and epigenome reprogramming by SARS-CoV-2. 501 a. A scatter plot showing the compartmental scores (E1 scores) of genome-wide 100kb genomic bins in 502 mock or SARS-CoV-2 infected cells. Six categories of changes based on E1 changes were colored-coded. 503 b. A snapshot of compartmental score (E1) tracks at an example region (chr9: 108Mb-118Mb). 504 Rep1/Rep2 are replicates. Orange or blue color indicates A compartment (E1 score >0) or B compartment 505 (E1 score < 0), respectively. Arrows and arrowheads exemplify compartmental changes in panel a (with 506 matched colors). 507 c. A diagram shows the categories of genomic bins with changes of A/B compartmental scores, and were 508 referred to as “A-ing” (E1 scores increase) or “B-ing” (E1 scores reduce) after virus infection. 509 d. Heatmaps showing the enrichment of active or repressive histone marks or RNA Pol2 (RPB1) on the 510 six categories of genomic bins defined in panel c. Scale indicates log2-transformed enrichment of these 511 features (see Methods). 512 e. Saddle plots showing the compartmentalization between genomic regions ranked by their PCA E1 513 scores (all genomic regions were divided into 50 bins in total, see Methods). Therefore, for example, A-A 514 homotypic interactions are in the right lower part of the plot; A-B interactions are in the right upper and 515 left lower parts. Differential compartmental interactions are shown on the right as log2 fold changes 516 (SARS-CoV-2/Mock). 517 f. Western blots showing the abundances of total histone H3 or several modifications in Mock and SARS- 518 CoV-2 infected (24hpi) cells. 519 g. Boxplots showing the log2 fold changes of calibrated ChIP-Seq signals of H3K27ac and H3K9me3 for 520 the six categories of genomic bins with varying compartmental changes (as in panel c). The boxplot 521 centre lines represent medians; box limits indicate the 25th and 75th percentiles; and whiskers extend 1.5 522 times the interquartile range (IQR) from the 25th and 75th percentiles. 523

18 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Figure 3 available under aCC-BY-NC-ND 4.0 International license.

a Mock SARS-CoV-2 SARS-CoV-2 / Mock

−3 0 10 10−3

−2 log2 FC chr14: 71,400,000 - 72,600,600 Genes

71.4 Mb 71.6 Mb 71.8 Mb 72 Mb 72.2 Mb 72.4 Mb 72.6 Mb 71.4 Mb 71.6 Mb 71.8 Mb 72 Mb 72.2 Mb 72.4 Mb 72.6 Mb 71.4 Mb 71.6 Mb 71.8 Mb 72 Mb 72.2 Mb 72.4 Mb 72.6 Mb 0 0 IS score −1 −1 TAD Intra-TAD interactions b Mock SARS-CoV-2 SARS-CoV-2 / Mock c 0.025 P< 1e-100

2.00 2.00 2.0 0.020 1.50 1.50 1.0 0.015 1.00 1.00 0.0

0.50 0.50 −1.0 Log2 FC 0.010

0.00 0.00 −2.0 Intra-TAD interaction 0.005

Mock SARS-CoV-2 Aggregated TADs Aggregated TADs Aggregated TADs

d Insulation scores e Mock SARS-CoV-2 1 1 CTCF 0.2 0 0

0.0 20 20

RAD21 0 0 −0.2 Mock 10 10 −0. SARS-CoV-2 SMC3 0 0 log2 Insulation Score Genes −0. -500 -300 -100 100 300 500 chr14: 71.6 71.7 71.8 71.9 72 72.1 72.2 72.3 Mb 71.6 71.7 71.8 71.9 72 72.1 72.2 72.3 Mb Distance from TAD boundaries (kb) TAD

f Chromatin features of g 6 categories of TADs 1.5 NIPBL RAD21 SMC3 CTCF 0.0 CTCF 1.0 RAD21 0.5 −0.2 SMC3 H3K27ac 0.0 −0.

H3K4me3 (Log2) −0.5 RPB1 −0. Bottom 10% TAD H3K9me3 −1.0 Top 10% TAD Fold enrichment of features Portion of lost peaks per bin −0. H3K27me3 −1.5 TAD TAD TAD TAD TAD TAD Boundary Boundary Boundary Boundary Boundary Boundary

TADs TADs TADs Top 5% 50~90% 10%~50% Top 5~10% Bottom 5% Bottom 5~10% Intra-TAD Weakening bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

524 Fig. 3. SARS-CoV-2 pervasively weakens intra-TAD contacts and cohesin loading. 525 a. Snapshots of Hi-C contact matrices at the indicated region (hg19) in Mock and SARS-CoV-2 526 conditions. Also shown are the log2 fold changes of the Hi-C interactions (right panel), RefSeq gene 527 tracks (bottom), the Insulation score (IS score, bottom), and TAD location (pink bars). Genomic 528 coordinate is indicated on the left. 529 b. Aggregated domain analyses (ADA) plots showing genome-wide reduction of intra-TAD interactions 530 after SARS-CoV-2 infection. Right: log2 fold changes of signals. TAD location is denoted by a pink bar. 531 The plots include additional 0.5xTAD on each side for quantification. 532 c. A boxplot showing intra-TAD interactions in Mock and SARS-CoV-2 infected conditions. The boxplot 533 centre lines represent medians of intra-TAD interaction from 4,094 TADs; box limits indicate the 25th 534 and 75th percentiles; and whiskers extend 1.5 times the interquartile range. P-value: Mann-whitney U 535 test. 536 d. A profile plot of insulation scores calculated based on Mock (grey) and SARS-CoV-2 (red) infected 537 Hi-C at all TAD boundaries. 538 e. Snapshots of CTCF, RAD21, SMC3 ChIP-Seq tracks of both mock (left) and SARS-CoV-2 (right) 539 conditions at an example region also shown in the panel a (chr14:71.5-72.3Mb, hg19). 540 f. A heatmap showing the enrichment of cohesin/CTCF or epigenetic marks in six categories of TADs 541 ranked by their quantitative reduction of intra-TAD interactions (also see Extended Data Fig.6a and 542 Methods). 543 g. A meta-profile showing the portions of ChIP-Seq peaks reduced by SARS-CoV-2 for RAD21, SMC3 544 and CTCF, and the relative positioning of these changes to TADs (see Methods). Line plots show the 545 distribution of lost peaks over a region centered on the midpoint of each TAD (0.25xTAD on each side 546 out of TAD). Red lines indicate lost peaks belong to the top 10% weakened TADs, grey lines indicate the 547 10% least weakened TADs. The y-axis indicates the portion of lost peaks versus total peaks (e.g. -0.6 548 indicates 60% peaks in that bin were lost). 549

19 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Figure 4 available under aCC-BY-NC-ND 4.0 International license. Pol2 ChIP-Seq Activity by Contact Model: IFN gene transcription a c A: Enhancer H3K27ac signal d by ABC scores IFN genes Pro-inflammatory C: Hi-C contacts between E-P genes P: Promoter H3K4me3 signal 8 c1 Pearson R:0.61 6 P<3.87e-16 P< 9.31e-4 6 4 4 c2 Gene 2 2 0

Log2 FC 0 0 e1 e2 p n ABC score = Σ A*C −2 −2 i =1 i i n −2 − − ABC -P score = P*(Σ A*C) i =1 i i n 2 ABC -P score = P*P*(Σ Ai*Ci) log2 FC of RPB1 ChIP-Seq −2 0 SARS-CoV-2 IFNβ i =1 Log2 FC of ABC score IFIT1/2/3/5 loci IL-6 loci b Mock e Mock f PIF gene transcription by ABC-P2 scores

Pearson R:0.47 −2 −2 4 10 10

2 −3 Chr10: 90.95Mb-91.23Mb Chr7: 22.68Mb - 22.85Mb −3 10 10 SARS-CoV-2 SARS-CoV-2 0

50 25 Mock Mock −2

0 0 log2 FC of RPB1 ChIP-Seq 50 −2 0 2 4 25 2 RAD21 SARS-CoV-2 SARS-CoV-2 Log2 FC of ABC-P score RAD21 0 0 5.0 10 Mock 2.5 Mock 0 5.0 10 SARS-CoV-2 2.5 SARS-CoV-2 H3K4me3

0 H3K4me3 10 10 * * * Mock * * Mock 0 0 10 10 * * * SARS-CoV-2 * *

H3K27ac SARS-CoV-2

0 H3K27ac 0 IFIT2 IFIT3 IFIT1B IFIT5 LOC401312 Genes IFIT1 IL6

91 Mb 91.05 Mb 91.1 Mb 91.15 Mb 91.2 Mb 22.7 Mb 22.75 Mb 22.8 Mb 22.85 Mb g Mock SARS-CoV-2 Compartmentalization Regions TADs A weakening with weakened (domains) 1 euchromatin A-B mixing features

A compartment Cohesin

Compartmentalization B compartment

Compartmentalization A-B mixing A-A weakening

CTCF Cohesin TAD TAD loop 2 Cohesin depletion Regions Cohesin extrusion from intra-TADs with weakened euchromatin Reduced loop extrusion features Loop extrusion TADs / loop extrusion Reduced enhancer-promoter contacts 3 enhancer Reduced enhancer enhancer enhancer gene activity (H3K27ac) IFN genes Inflammation (DDX58) genes Inflammation gene promoters (IL-6) Enhancer promoter contacts H3K4me3 increase bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

550 Fig. 4. Chromatin restructuring underlies dichotomic transcriptional deregulation of interferon 551 response genes and inflammation genes. 552 a. Boxplots showing transcriptional deregulation of key interferon response gene (IFN) and pro- 553 inflammatory genes after SARS-CoV-2 infection or IFN-beta treatment (1000u, 6-hr), as shown by fold 554 changes of RNA Pol2 (RPB1) ChIP-Seq. The boxplot centre lines represent medians; box limits indicate 555 the 25th and 75th percentiles; and whiskers extend 1.5 times the interquartile range (IQR) from the 25th 556 and 75th percentiles. P-value: Mann-whitney U test. 557 b. Snapshots of Hi-C contact matrices and calibrated ChIP-Seq tracks for indicated factors at the IFIT 558 gene cluster. Top right: Mock; lower left: SARS-CoV-2. Blue arrows point to reduced dot-shaped loops. 559 White lines mark the TAD, with intra-TAD interactions weakened throughout. Red asterisks show virus- 560 reduced H3K27ac peaks. Green arrows show unchanged H3K4me3 peaks on IFIT1/3 promoters. 561 c. A diagram shows the method of Activity-by-contact (ABC) to model the transcriptional outputs of a 562 gene based on considering its enhancer activity and enhancer-promoter contacts; ABC-P or ABC-P2 are 563 revised ABC algorithms that also consider promoter strength (see Methods). 564 d. A scatter plot showing the correlation between the ABC scores (x-axis) and true transcriptional 565 changes of IFN genes caused by SARS-CoV-2 (y-axis, RPB1 ChIP-Seq). A high Pearson’s correlation 566 coefficient is achieved and labeled. A liner regression fitted line and its 95% confidence interval (shaded 567 area) are also shown. 568 e. Similar to panel b, the snapshots of Hi-C matrices and ChIP-Seq tracks at the IL-6 locus. Top right: 569 Mock; lower left: SARS-CoV-2. White lines mark the TADs, with intra-TAD interactions weakened 570 throughout. Red asterisks show reduced H3K27ac peaks. Green arrows show enhanced H3K4me3 on IL-6 571 promoter. 572 f. A scatter plot showing the correlation between the ABC-P2 scores (x-axis) and true transcriptional 573 changes of pro-inflammatory genes by SARS-CoV-2 (y-axis, RPB1 ChIP-Seq). Pearson’s correlation 574 coefficient is shown. A liner regression fitted line and its 95% confidence interval are shown. 575 g. A proposed model that summarizes our findings of the chromatin restructuring by SARS-CoV-2 at 576 scales of both 3D genome and 1D epigenome. They are categorized in compartments, TADs/loop 577 extrusion and enhancer-promoter contacts (see Extended Data Fig.1a,b for additional information). 578 These changes can explain transcriptional deregulation of interferon response and inflammation genes 579 that confer immunological phenotypes in COVID-19 patients. 580

20 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

781 Materials and Methods: 782 Cell culture 783 Human lung adenocarcinoma cells A549 expressing human ACE2 (A549-ACE2, #NR53821) was 784 acquired from BEI Resources (Manassas, VA). They were maintained in DMEM/F-12 (1:1, Corning) 785 medium supplemented with 10% FBS (GeneDepot) and blasticidin (100uM). Normal A549 cells were 786 purchased from ATCC (CCL-185) and were cultured in DMEM/F-12 (1:1, Corning) supplemented with 787 10% FBS. 293T cells were from ATCC and were cultured in DMEM with 10% FBS. Vero-E6 cells were 788 acquired from ATCC (CRL-1586). Mouse ESCs (F121-9) are a gift from David Gilbert lab (Florida State 789 University) and were cultured following standard procedure of the 4D nucleome consortium 790 (https://data.4dnucleome.org/biosources/4DNSRMG5APUM/). All these cells were cultured at 37°C with 791 5% CO2. Transfection of plasmids or siRNAs was performed using Lipofectamine 3000 or RNAiMAX 792 (Life technologies) following manufacturer's instructions. For CRISPRi experiments, in order to examine 793 enhancer functions during cell responses to RNA virus, we introduced poly(I:C) (333ng/ml, Sigma, 794 P9582) into A549 cells using lipofectamine 2000 and harvested the total cellular RNAs for gene 795 expression experiments after 4 hours. 796 797 SARS-CoV-2 infections in A549-ACE2 cells 798 SARS-CoV-2 isolate USA-WA1/2020 (NR-52281; BEI Resources, Manassas, VA) was employed to 799 infect human A549-ACE2 cells (NR53821; BEI Resources). For viral infections, serum/antibiotics-free 800 Eagle’s MEM medium supplemented with 1mM HEPES was used. Briefly, cells grown in 10-cm culture 801 dishes at about 70-80% confluency were washed with the serum free medium, viral inoculum was added 802 at 0.1 MOI for 1 hr. After that, non-adsorbed viral particles were gently aspirated out and the monolayers 803 were replenished with 10% FBS containing MEM supplemented with 1mM HEPES. Infected cells were 804 incubated at 37°C with 5% CO2 for 6hr or 24hr post-infection for experiments. 805 806 Preparation of SARS-CoV-2 stock 807 The stock SARS-CoV-2 was propagated in Vero-E6 cells. Briefly, Vero-E6 cells were grown to 80% 808 confluence in 10% FBS containing MEM medium supplemented with 1mM HEPES and 1X antibiotics 809 and antimycotics. Prior to infection, Vero-E6 cells were washed once with PBS and the viral inoculum 810 was added to the flask in the presence of 3 ml of serum-free and antibiotics-free MEM medium 811 supplemented with 1mM HEPES, and incubated for 1 hour at 37°C with 5% CO2. At the end of 812 incubation, non-adsorbed virus was aspirated out and cells were replenished with 25ml of MEM 813 supplemented with 10% FBS and 1mM HEPES. Infected cells were incubated for 48 hours at 37°C with 814 5% CO2. At 80% of cell lysis, SARS-CoV-2 was harvested by detaching all the cells with a cell scraper 815 and centrifuging at 300g for 3 minutes. Viral aliquots were stored in screw-cap vials at -80°C. 816 817 Determination of plaque forming units (PFU/ ml stock) 818 For the determination of infectious viral titers, plaque assays were performed using Vero-E6 cells. 819 Briefly, Vero-E6 grown in 6-well plates were infected with 12 serial dilutions (1:10) of the SARS-CoV-2 820 stock in serum/BSA/antibiotic-free MEM medium with 1mM HEPES for 1 hour at 37°C with 5% CO2. At 821 the end of incubation, non-adsorbed viral particles were aspirated and the infected cells were layered upon 822 with MEM medium containing 0.5% agarose, 2% BSA and 1mM HEPES, and incubated for 48 hours at 823 37°C with 5% CO2. Fixation was carried out using 3.75% buffered formaldehyde (in PBS) for 10 824 minutes. After aspirating formaldehyde, the agarose layers were gently removed. Infected cells were 825 stained with 0.3% crystal violet for 5 minutes, followed by washing once with PBS. Plates were air-dried 826 and visible infectious plaques were counted in each dilution to determine the plaque forming units/ ml of 827 the stock. 828 829 Lenti-viral Transduction and CRISPRi

27 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

830 We in-house generated a lentiviral construct expressing dCas9-KRAB-MeCP2 by PCR amplification of 831 the dCas9-KRAB-MeCP2 (contains a domain of MeCP2) from pB-CAGGS-dCas9-KRAB-MeCP2 832 (Addgene 110824), and then insert it to the pLenti-EF1a-dCas9-VP64-2A-Blast backbone (Addgene 833 61425) to replace th dCas9-VP64. The gRNAs used in CRISPRi were cloned into the Addgene 61427 834 backbone using BsmBI enzyme. To generate lentivirus, 293T cells were transfected with the lentiviral 835 transfer vector DNA, psPAX2 packaging and pMD2.G envelope plasmid DNA at a ratio of 4:3:1, 836 respectively, by lipofectamine 2000. After 16 h, the culturing media was changed to fresh one, and the 837 supernatants were collected twice at 48h to 72h post-transfection. The harvested lentiviral supernatants 838 were filtered using 0.45 μm syringe filter (Fisher) and used to infect target A549 cells (polybrene was 839 added at a final concentration of 8 μg/ml, Sigma). To infect A549 cells for CRISPRi, cells were first 840 infected by a lentivirus expressing dCas9-KRAB-MeCP2 for 24 hours and selected with appropriate 841 antibiotics (10 μg/ml blasticidin) for 7 days to generate a stable cell line. The stable cell line was then 842 subjected to viral infection by individual gRNAs targeting each enhancer, and they were further selected 843 by 100 μg/ml Zeocin for 4-7 days. These stable cells were then used for experiments. The gRNA cloning 844 oligos are shown in Extended Data Table 2. 845 846 RNA Extraction and RT-qPCR 847 RNA extraction of SARS-CoV-2 infected A549-ACE2 cells was performed by TRIzol (Thermo Fisher 848 Scientific, 15596-026) following manufacturer's instructions. In some other cases, RNAs of cells 849 expressing CRISPRi or other transfection were performed using Quick-RNA Miniprep Kit (Zymo 850 Research, 11-328). Reverse transcription was conducted by using Superscript™ IV first strand synthesis 851 kit (Thermo Fisher, 18091050), and often the random hexamer primer was used to test the expression 852 levels of target genes. Real-time qPCR (RT-qPCR) was performed using SsoAdvanced™ Universal 853 SYBR® Green Supermix (Bio-Rad, 172-5274). Primer sequences used in this study can be found in 854 Supplementary Table 2. Relative gene expression was normalized to internal control (18S RNA). 855 856 Western blots 857 Cells were lysed in RIPA (50 mM Tris, pH 7.4, 150 mM NaCl, 1 mM EDTA, 0.1% SDS, 1% NP-40, 858 0.5% sodium deoxycholate) with cOmplete™ Mini Protease Inhibitor Cocktail (Roche, 11836153001) on 859 ice for 30min. Lysates were sonicated in Qsonica 800R (25% amplitude, 3 min, 10 sec on 20 sec off 860 interval) and centrifuged at 14,000rpm. The supernatants were mixed with 2x Laemmli Sample Buffer 861 (Bio-Rad) and boiled at 95℃ for 10min. The boiled proteins were separated on 4% to 15% SDS-PAGE 862 gradient gels and transferred to LF PVDF membrane (Bio-Rad, Cat #1620260). The membranes were 863 blocked in 5% skim milk in TBST (20 mM-Tris, 150 mM NaCl, and 0.2% Tween-20 (w/v)) for an hour 864 and then briefly washed in TBST twice. Then, the membranes were incubated in TBST with primary 865 antibodies (GAPDH (Proteintech, 60004-1), RAD21 (Abcam, Ab992, Lot: GR214359-10), CTCF 866 (Millipore, 07-729), SMC3 (Abcam, Ab9263, Lot:GR466-7), Total Histone H3 (Abcam, Ab1791, 867 Lot:GR206754-1), H3K4me3 (Abcam, Ab8580, Lot: GR3264490-1), H3K9me3 (Abcam, Ab8898, Lot: 868 GR164977-4), H3K27ac (Abcam, Ab4729, Lot: GR3357415-1), and H3K27me3 (Cell Signaling 869 Technology, #9733S, Lot19)) at 4°C overnight. After washing 3 times in TBST, the blots were incubated 870 in TBST with secondary antibody (Horseradish peroxidase (HRP)-conjugated antibody) for an hour. After 871 6 times of washing in TBST, the blots were developed in a Bio-Rad ChemiDoc™ Gel Imaging System. 872 873 Immunofluorescence microscopy 874 Expression of spike protein of SARS-CoV-2 was measured by immunofluorescence microscopy. A549- 875 ACE2 cells seeded on glass slides were infected with SARS-CoV-2 at a MOI of 0.1. 24hr post infection 876 (24hpi), cells were fixed by 4% paraformaldehyde in phosphate buffered saline (PBS) for 1hr at room 877 temperature. The coverslips were washed with 0.1% BSA in 1xPBS (Wash buffer) and blocked with 1% 878 BSA with 0.3% Triton-X100 in PBST (Blocking buffer) for 45min at room temperature. Cells were 879 incubated with 1:500 anti-SARS-CoV-2 Spike glycoprotein antibody (Abcam, Catalog#ab272504) 880 diluted in blocking buffer for overnight at 4 degree. Subsequently, after washes, cells were incubated with

28 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

881 1:500 Alexa Fluor 594-conjugated-anti-rabbit-IgG (Jackson Immunoresearch, Catalog#111-585-144) 882 diluted in blocking buffer for 1hr at room temperature, followed by incubation with 4,6-diamidino-2- 883 phenylindole (Invitrogen, Catalog#D1306) for 5 minutes at room temperature. Coverslips were mounted 884 in antifade mounting medium (Thermo scientific, Catalog#TA-030-FM) and the fluorescence images 885 were recorded using a Leica confocal microscope. 886 887 Hi-C 3.0 888 Hi-C 3.0 was performed based on a recent protocol12, which is largely modified based on in situ Hi-C15. 889 Briefly, ~5 million SARS-CoV-2 infected A549-ACE2 cells were washed once with PBS to remove 890 debris and dead cells, trypsinized off the culture plates, and were cross-linked using 1% formaldehyde for 891 10mins at room temperature, quenched with 0.75M Tris-HCl pH 7.5 for 5mins. These cells were further 892 cross-linked with 3mM disuccinimidyl glutarate (DSG) for 50mins, and again quenched with 0.75M Tris- 893 HCl pH 7.5 for 5mins. Cross-linked cell pellets were washed with cold PBS, and then resuspended in 0.5 894 ml ice-cold Hi-C lysis buffer (10 mM Tris-HCl, pH 8.0; 10mM NaCl, 0.2% NP-40 and protease inhibitor 895 cocktail), and rotated at 4°C for 30 mins. Nuclei were washed once with 0.5 ml ice-cold Hi-C lysis buffer. 896 After pelleting down the nuclei, 100 μl of 0.5% SDS was used to resuspend and permeabilize the nuclei at 897 62 °C for 10 mins. Then 260 μl H20 and 50 μl 10% Triton-X100 were added to quench the SDS at 37 °C 898 for 15 mins. Subsequently, enzyme digestion of chromatin was performed at 37 °C overnight by adding 899 an additional 50 μl of 10X NEB buffer 2, 100U MboI (NEB, R0147M) and 100U DdeI (NEB, R0175L). 900 After overnight incubation, the restriction enzyme was inactivated at 62 °C for 20 mins. To fill-in the 901 DNA overhangs and add biotin, 35U DNA polymerase I (Klenow, NEB, M0210) together with 10 μl of 902 1mM biotin-dATP (Jeana Bioscience), 1μl 10mM dCTP/dGTP/dTTP were added and incubated at 37°C 903 for 1 hour with rotation. Blunt end Hi-C DNA ligation was performed using 5000 U NEB T4 DNA ligase 904 with 10X NEB T4 Ligase buffer with 10mM ATP, 90 μl 10% Triton X-100, 2.2 μl 50mg/ml BSA at room 905 temperature for four hours with rotation. After ligation, nuclei were pelleted down and resuspended with 906 440 μl Hi-C nuclear lysis buffer (50mM Tris-HCl pH7.5, 10mM EDTA, 1% SDS and protease inhibitor 907 cocktail), and further sheared using the parameter of 10/20 secs ON/OFF cycle, 25% Amp, 4 mins by a 908 QSonica 800R sonicator. Around 10% of the sonicated chromatin was subjected to overnight 909 decrosslinking at 65°C, protein K treatment, and DNA extraction. After DNA extraction, biotin labelled 910 Hi-C 3.0 DNAs were purified by 20 μl Dynabeads® MyOne™ Streptavidin C1 beads (Thermo Fisher 911 65002). The biotinylated-DNA on C1 beads was used to perform on-beads library making with 912 NEBNext® Ultra™ II DNA Library Prep Kit for Illumina (NEB, E7645L) following manufacturer’s 913 instructions. The sequencing was done on a NextSeq 550 platform with PE40 mode. 914 915 Chromatin immunoprecipitation (ChIP-Seq) and spike-in calibrated ChIP-Seq 916 ChIP-Seq was performed as previously described with minor modifications26. For most ChIP-Seqs in 917 A549-ACE2 cells with Mock treatment or 24-hr SARS-CoV-2 infection, ~5 to 10% of mouse ESCs 918 (F121-9, a gift from David Gilbert) were added as spike-in controls before sonication with equal 919 proportions to the two human cell samples (see Extended Data Fig.4a). For cell cross-linking for ChIP- 920 Seq, briefly, the cells were trypsinized in Trypsin-EDTA (or Accutase for mESCs). After centrifugation, 921 the cells were crosslinked by 1% formaldehyde (FA) in PBS for 10 min. The fixation steps were stopped 922 in a quenching solution (0.75M Tris-HCl pH 7.5) for 10min. After centrifugation of the cells, we 923 extracted the nuclei first by buffer LB1 [50 mM HEPES-KOH (pH 7.5), 140 mM NaCl, 1 mM EDTA (pH 924 8.0), 10% (v/v) glycerol, 0.5% NP-40, 0.25% Triton X-100 and 1×cocktail protease inhibitor], and then 925 by LB2 [10 mM Tris-HCl (pH 8.0), 200 mM NaCl, 1 mM EDTA (pH 8.0), 0.5 mM EGTA (pH 8.0) and 926 1×cocktail protease inhibitor]. After centrifuge, the nuclei were suspended in buffer LB3 [10 mM Tris- 927 HCl (pH 8.0), 100 mM NaCl, 1 mM EDTA (pH 8.0), 0.5 mM EGTA (pH 8.0), 0.1% Na-deoxycholate, 928 0.5% N-lauroyl sarcosine and 1×cocktail protease inhibitor], and the chromatin was fragmented by using 929 Q800R3 sonicator (QSonica) using conditions of 10 seconds ON, 20 seconds OFF for 7-9 mins (at 20% 930 amplitube). Sheared chromatins were collected by centrifugation, and were incubated with appropriate 931 antibodies (often 2-3μg) at 4°C overnight. The next morning, the antibody-protein-chromatin complex

29 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

932 was retrieved by adding 25μl pre-washed Protein G Dynabeads (Thermo Fisher Scientific, 10004D). 933 Immunoprecipitated chromatin DNA was de-crosslinked by 65ºC heating overnight using elution buffer 934 (1% SDS, 0.1M NaHCO3), and then treated by RNase A and proteinase K, which was finally purified by 935 phenol chloroform. The DNAs were subjected to sequencing library construction using NEBNext® 936 Ultra™ II DNA Library Prep Kit for Illumina (NEB, E7645L), and were deep sequenced on a NextSeq 937 550 platform using 40nt/40nt pair-ended mode. The antibodies used for ChIP-Seq include RNA 938 Polymerase II (RPB1 N terminus, Cell Signaling Technology, #14958S, Lot4), RAD21 (Abcam, Ab992, 939 Lot: GR214359-10), SMC3 (Abcam, Ab9263, Lot:GR466-7), CTCF (Millipore, 07-729), H3K4me3 940 (Abcam, Ab8580, Lot: GR3264490-1), H3K9me3 (Abcam, Ab8898, Lot: GR164977-4), H3K27ac 941 (Abcam, Ab4729, Lot: GR3357415-1), H3K27me3 (Cell Signaling Technology, #9733S, Lot19) and HA 942 (Abcam, Ab9110, Lot:GR3231414-3). 943 944 Ribo-depleted total RNA sequencing (RNA-Seq) 945 Total RNAs from mock or virus infected A549-ACE2 cells were extracted by TRIzol, and 100-200ng of 946 total RNAs were used for making strand-specific ribosome-RNA-depleted sequencing library by the NEB 947 Ultra II Directional RNA library kit (E7760L) following manufacturer's instructions. Libraries were 948 sequenced on a NextSeq 550 using 40nt/40nt pair-ended mode. 949 950 Bioinformatic analyses: 951 Calibrated ChIP-Seq analyses 952 Sequencing reads were aligned to a concatenated genome of hg19 human genome assembly and mm9 953 mouse genome assembly with STAR v 2.7.027. Duplicated reads were removed, and only unique aligned 954 reads will be considered for later visualization and quantification. The scaling factor was calculated as the 955 ratio of the number of reads uniquely aligned to human chromosomes versus the number of reads aligned 956 to mouse chromosomes (Extended Data Fig.4a). Uniquely aligned human reads were extracted with 957 samtools28, and normalized by the corresponding scaling factor with deeptools29. For RBP1 ChIP-Seq 958 gene transcription quantification, hg19 RefSeq gene annotation coordinates were used. The peak-calling 959 of most ChIP-Seq were performed with the parameters of -f BAM -q 0.01 in MACS2 30. For each peak, 960 we considered it with a log2 fold change of calibrated ChIP-Seq signal greater than 1 or lower than one as 961 gained peak or reduced peak. ChIP-Seq reads are summarized in Extended Data Table 1. A public 962 NIPBL ChIP-Seq dataset was obtained from SRR3102878. 963 964 RNA-Seq analysis 965 RNA-Seq reads were aligned to the hg19 reference human genome or SARS-CoV-2 viral genome 966 (NC_045512.2) with STAR v 2.7.027. The percentage of reads uniquely aligned to SARS-CoV-2 genome 967 versus total reads was calculated to verify a high viral infection rate. For human gene quantification, only 968 uniquely aligned reads mapped to the hg19 genome were kept for further analysis. Differential gene 969 expression analyses were performed with EdgeR, and genes with |FC|>2, FDR<0.05 were considered as 970 significantly differential expressed genes.

971 Hi-C 3.0 data processing 972 Hi-C 3.0 raw data was primarily processed with HiC-Pro31. The pair of reads were mapped to the human 973 reference genome assembly hg19, and multi-mapped pairs, duplicated pairs, and other unvalid 3C pairs 974 were filtered out following the standard procedure of HiC-Pro. All valid Hi-C pairs were merged between 975 replicates (unless specified noted), and were further converted to Juicebox format32 or cooler format33 for 976 visualization and further analyses. Hi-C contact matrices were normalized with cooler balance function33. 977 Reads numbers of Hi-C are listed in Extended Data Table 1. The SCC correlation coefficients between 978 two replicates were calculated to assess the reproducibility of Hi-C experiments13. The P(s) curve was 979 calculated as a function between contact frequency (P) and genomic distances(s) (Fig. 1e). Only intra- 980 chromosomal pairs (cis) were used to calculate P(s) curve. 981

30 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

982 A/B Compartment analyses 983 A/B nuclear compartments were identified based on decomposed eigenvectors (E1) from 20kb or 100kb 984 Hi-C contact matrices using cooltools. A/B compartmental scores (E1) were corrected by GC densities in 985 each bin. Saddle plot analyses were performed to measure the compartmentalization strength in a 986 genome-wide scale using cooltools compute-saddle (similar to8,10). Briefly, we first sorted the rows and 987 the columns in the order of increasing compartmental scores within observed/expected (O/E) contact 988 maps based on the data in Mock cells. Then we aggregated the rows and the columns of the resulting 989 matrix into 50 equally sized aggregate bins, and plotted the aggregated observed/expected Hi-C matrices 990 as the “saddle” plots (Fig.2e). In Fig.1c and other few places, Pearson correlation Hi-C matrices were 991 used to emphasize the compartmental checkerboard pattern. We first calculated the observed/expected Hi- 992 C maps as OE matrices (bin size = 80,000 bp). Each value (i,j) in Pearson matrices indicates the Pearson 993 correlation coefficient between the i-th column of OE matrices and the j-th column of OE matrices (bin 994 size = 80,000bp). The sliding correlation score (Extended Data Fig.3b) was obtained based on Pearson 995 correlation matrices, and we largely follow a previous work9. Briefly, for each genomic bin i (bin size = 996 80,000bp), we calculated the Pearson correlation coefficient between the i-th column and i+1-th column 997 of Pearson correlation matrices, as the sliding correlation score. This score indicates correlation for each 998 region as compared to the neighboring region. Valleys of SC score imply strong differences in long-range 999 contact pattern observed at a locus as compared to its neighboring loci, indicating a change in 1000 compartment. Compartmental domains are genomic regions with continuous positive or negative 1001 compartmental scores (E1), identified by applying HOMER tool (findHiCCompartments.pl) on E1 scores. 1002 For changes of compartmental strength (Fig.2a and Extended Data Fig.3a), the changes for each 1003 genomic region between Mock and SARS-CoV-2 samples were identified based on 100kb-binned 1004 compartmental scores (E1) of two Hi-C 3.0 replicates, largely following a previous study16. For each 1005 100kb, a student’s t-test was first performed on Mock and SARS-CoV-2 compartmental scores (E1). Only 1006 the 100kb bins that have |delta E1| > 0.2 and P-value < 0.05 were considered as bins with changed 1007 compartmental strength. Different categories of compartment changes (in Fig. 2a) were defined as below 1008 (similar to16): A to stronger A: (Mock-E1 - SARS-CoV-2-E1) < -0.2, Mock-E1>0.2; B to A: (Mock E1 - 1009 SARS-CoV-2 E1) <-0.2, Mock-E1 < -0.2, SARS-CoV-2-E1>0; B to weaker B: (Mock- E1 - SARS-CoV- 1010 2 E1) < -0.2, Mock-E1 <-0.2, SARS-CoV-2-E1<0; B to stronger B: (Mock-E1 - SARS-CoV-2-E1)>0.2, 1011 Mock-E1 < -0.2; A to B: (Mock-E1 - SARS-CoV-2-E1)<-0.2, Mock-E1 > 0.2, SARS-CoV-2-E1<0; A to 1012 weaker A: (Mock-E1 - SARS-CoV-2-E1)<-0.2, Mock-E1 > 0.2, SARS-CoV-2-E1>0. 1013 1014 TADs and insulation scores 1015 Hi-C 3.0 data were used to identify topologically associating domains (TADs) in A549-ACE2 cells 1016 following standard 4D Nucleome consortium protocol (github.com/4dn-dcic/docker-4dn-insulation- 1017 scores-and-boundaries-caller). First, insulation scores 18 and boundary strengths of each 10kb bin with a 1018 200kb window size were measured to quantify the TAD insulation using cooltools 1019 (https://github.com/open2c/cooltools/blob/master/cooltools/cli/diamond_insulation.py). Then, we 1020 identified TAD boundaries respectively in Mock and SARS-CoV-2 infected samples by using a boundary 1021 score cutoff of 0.5. We further merged TAD boundaries identified in these two conditions, and compared 1022 insulation scores at merged TAD boundaries (Fig. 3d). Merged TAD coordinates were used to perform 1023 downstream analyses. For each TAD, we quantified its mean Hi-C contacts throughout the domain 1024 (excluding very short distant interactions <15kb), which is considered intra-TAD interaction used in the 1025 paper. Based on the log2 fold changes of intra-TAD mean Hi-C contacts (SARS-CoV-2 / Mock), we 1026 ranked all TADs into six categories (Top 5%, top 5~10%, 10~50%, 50~90%, bottom 5~10%, bottom 1027 5%), and calculated different epigenomic features of these six categories. For histone modifications or 1028 chromatin regulatory factors that have sharp peaks in ChIP-Seq (like H3K27ac, H3K4me3, CTCF or 1029 cohesin subunits), we quantified the numbers of peaks or the numbers of gained or lost peaks in different 1030 TADs. For modifications or factors that have broad ChIP-Seq patterns (like H3K9me3 and H3K27me3), 1031 we quantified the calibrated ChIP-Seq reads throughout the TADs. The enrichments of these ChIP-seq

31 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

1032 signals were calculated by dividing the median or mean quantification inside these six categories by the 1033 genome-wide median or mean quantification. 1034 1035 Chromatin loop calling and enhancer-promoter contacts 1036 For loop calling, we largely followed a recent 4DN benchmarking paper12. In brief, we used a 1037 reimplement of HICCUPS loop-calling tool, call-dots function inside cooltools 1038 (https://github.com/open2c/cooltools/blob/master/cooltools/cli/call_dots.py) to identify structural 1039 chromatin loops in different samples. We first called loops at 5kb and 10 kb resolution separately, then 1040 used the following strategy to merge 5kb and 10kb loops. 5kb loops called at both 10kb and 5kb 1041 resolution were first kept, all unique 10kb resolution loops were kept, and only unique 5kb loops that are 1042 smaller than 100kb were kept. Differential loops were identified by first quantifying the Hi-C raw 1043 contacts at 40kb resolution of each called loop, and then by performing DESeq2 differential analyses on 1044 these raw counts. We considered loops with a DESeq2 FDR <0.1 and a log2FC >0 or <0 as virus- 1045 strengthened or weakened chromatin loops. The APA (Aggregation Peak Analysis) was performed by 1046 superimposing observed/expected Hi-C matrices on merged loops with the coolpuppy tool34. 1047 1048 Activity by Contact (ABC) score 1049 ABC score calculation largely follows a previous study2 with modifications. For A score (enhancer 1050 activity) of a gene, we first identified all putative enhancers of this gene by selecting H3K27ac ChIP-Seq 1051 peaks located within 1Mb of the promoter. Then we quantified the calibrated H3K27ac ChIP-Seq signals 1052 on these putative enhancers (extended 150bp from MACS2 peaks) as A scores. The A-only quantification 1053 of enhancer activity for this gene will be the sum of the A scores for all putative enhancers. For C score 1054 (enhancer-promoter contact) between a gene and putative enhancers, we quantified the normalized Hi-C 1055 contacts formed in between the 5kb bins harboring the gene promoter and the putative enhancer. For the 1056 ABC score, we multiplied the A score of each enhancer by the C score, and generated the summation of 1057 these if multiple putative enhancers exist for a gene. P score of any gene was calculated as the calibrated 1058 H3K4me3 ChIP-Seq signal at its promoter region (+/- 2.5kb from TSS) of a gene. For ABC-P or ABC-P2 1059 scores, we multiplied the summed ABC score of a gene by its P score (promoter H3K4me3 signal) or by 1060 the square of its P score. The transcriptional changes of any gene were calculated based on the log2 fold 1061 change of RBP1 ChIP-Seq reads over the whole gene body (average of three ChIP-Seq replicates). 1062 Pearson correlation coefficient was used to measure the correlation between ABC score change and 1063 transcriptional change. The list of interferon response (IFN) genes was obtained from GSEA molecular 1064 signature databases (Interfero_Alpha_Response), and the list of pro-inflammatory (PIF) genes was 1065 manually curated based on recent literature11 studying immuno-pathology of SARS-CoV-2 infection (see 1066 Extended Data Table 3). 1067 1068 Statistics 1069 qPCR data was analyzed by Prism and presented as mean±SD, which are indicated in figure legends. At 1070 least two biological replicates were conducted for RNA-Seq, ChIP-Seq or Hi-C sequencing. Student’s t- 1071 test (two-tailed) was commonly used to compare means between two qPCR groups; p < 0.05 was 1072 considered significant, and we labeled the p values with asterisks in each figure panel (*, p < 0.05; **, p < 1073 0.01; ***: p < 0.001). Statistical analyses for sequencing data were performed with Python or R scripts. 1074 Key softwares or algorithms used in our analysis of sequencing data are listed in methods. 1075

32 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Fig.1

a Compartmentalization Intra-TAD interactions and (A / B compartments) TADs and loops finer-scale enhancer-promoter contacts kbps to multiple Mbs (tens of kbs to 1Mb) (tens to hundreds of kbs)

CTCF

dot-shaped TADs Cohesin loop (domains) enhancer gene

b enhancer-promoter contacts

Compartmentalization TADs (domains) Loop extrusion enhancer gene A compartment Loop extrusion Compartmentalization B compartment

SARS-CoV-2 viral reads d (RNA-Seq) c (3D genome) Deep Hi-C 3.0 100 Mock 80 (Epigenome) v.s. Spike-in Calibrated 60 SARS-Cov-2 ChIP-Seq (24hpi) 40

(Transcriptome) in RNA-Seq 20 RNA-Seq 0

Percentage of viral reads Mock 0.1 MOI 0.1 MOI 6hpi 24hpi e Mock SARS-CoV-2 (0.1 MOI, 24hpi)

25m 25m Spike protein Spike protein / DAPI Spike protein Spike protein / DAPI bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Extended Data Fig.2available under aCC-BY-NC-ND 4.0 International license. a b Mock SARS-CoV-2 SCC correlation coefficient between replicates SCC correlation coefficient between replicates Mock 1.00 1.00 SARS-CoV-2 0.75 0.75 0.50 0.50 SCC R SCC R 0.25 0.25 0.00 0.00 0 5 10 15 20 0 5 10 15 20 different chromosomes different chromosomes

Mock rep1 SARS-CoV-2 rep1 Mock rep2 SARS-CoV-2 rep2 c

−2 −2 10 10 10−2 10−2 −3 −3 10 10 10−3 10−3 −4 −4 10 10 10−4 10−4

100 Mb 105 Mb 110 Mb 115 Mb 120 Mb 125 Mb 130 Mb 100 Mb 105 Mb 110 Mb 115 Mb 120 Mb 125 Mb 130 Mb 100 Mb 105 Mb 110 Mb 115 Mb 120 Mb 125 Mb 130 Mb 100 Mb 105 Mb 110 Mb 115 Mb 120 Mb 125 Mb 130 Mb 1 1 E1 0 E1 0 0 E1 0 −1 −1 E1 −1 −1

d Inter-chromosomal interaction changes (Log2 FC) e SARS-CoV-2 / Mock

1 Trans/Cis 2 0.6 3 1.6 4 5

6 0.5 7 0.8 8

9 0.4 10

11 0.3

12 0.0 13 14

15 0.2

16 −0. 17 Trans/Cis Hi-C contacts

18 0.1 19 20

21 −1. 0.0 22 Mock SARS-CoV-2 X 1 2 3 4 5 6 7 8 9 10111213141516171819202122 X bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Extended Data Fig.3available under aCC-BY-NC-ND 4.0 International license.

a A/B compartment changes b Sliding correlation score by SARS-CoV-2 Sliding Correlation score on (24hpi v.s. Mock) compartmental domain transition A -> 0.9 weaker A 0.8 A->B 0.7 B-> 0.6 stronger B SC score 0.5 B-> weaker B 0.4 Mock SARS-CoV-2 B->A 0.3 -500 -300-100 100 300 500 A -> Distance from stronger A compartmental domain boundary(kb) 0 5 10 15 Percentage (%) of 100kb bins

c Mock SARS-CoV-2 SARS-CoV-2 / Mock −2 0 2 −2 0 2

chr17 chr17 chr17

2 −2 −2 10 10 1 0 −4 −4 10 10 −1 −2

chr18 chr18 chr18 log2 (SARS-CoV-2 / Mock)

2 2 0 0 −2 −2 chr18 chr18 chr18 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Extended Data Fig.4available under aCC-BY-NC-ND 4.0 International license.

a Mock SARS-CoV-2 human A549 cells : mouse ESC cells human A549 cells : mouse ESC cells 5:1 5:1

mouse mouse

human human

ChIP-seq reads ratio (human / mouse): ChIP-seq reads ratio (human / mouse): 5:1 3:1 Calibrated scale factor: 3/5 = 0.6 (i.e., virus infected chromatin has 40% of this mark reduced)

H3K27ac (rep #1) H3K27ac (rep #2) b c H3K4me3 H3K27me3 H3K9me3 ~10% mESC spike-in ~20% mESC spike-in scale factor:0.59 scale factor:0.54 scale factor:1.12 scale factor:1.24 scale factor:1.25 7 3.5 7 3.0 8 5 6 4 2.0 4 3 4 2 2 1.0 2 2 0 0.0 0 0 0 Mock SARS-CoV-2 Mock SARS-CoV-2 Mock SARS-CoV-2 Mock SARS-CoV-2 Mock SARS-CoV-2 Human reads/mouse reads ratios Human reads/mouse reads ratios d e f g H3K27ac H3K4me3 H3K27me3 H3K9me3 12.5 12.5 12.5 12.5

10 10 10 10

7.5 7.5 7.5 7.5 SARS-CoV-2 SARS-CoV-2 SARS-CoV-2 (reads per 100kb bin) (reads per 100kb bin) SARS-CoV-2 (reads per 100kb bin) e e 5 5 (reads per 100kb bin) 5 5 e e log 5 7.5 10 12.5 5 7.5 10 12.5 5 7.5 10 12.5 log 5 7.5 10 12.5 log log log (reads per 100kb bin) log (reads per 100kb bin) e e loge(reads per 100kb bin) loge(reads per 100kb bin) Mock Mock Mock Mock h Mock SARS-CoV-2 SARS-CoV-2 - Mock

0.2 0.2 0.2 0.0 0.0 0.0 −0.2 −0.2

STEAP3-AS1C2orf76TMEM37CFAP221TMEM177PTPN4 EPB41L5TMEM185BRALBINHBBLINC01101 GLI2 TFCP2L1CLASP1 NIFKTSN CNTNAP5 GYPC LOC101929926BIN1CYP27C1ERCC3PROCIWS1MYO7BGPR17SFT2D3POLR2DSAP130UGGT1HS6ST1 LOC101927881LOC151121 STEAP3-AS1C2orf76TMEM37CFAP221TMEM177PTPN4 EPB41L5TMEM185BRALBINHBBLINC01101 GLI2 TFCP2L1CLASP1 NIFKTSN CNTNAP5 GYPC LOC101929926BIN1CYP27C1ERCC3PROCIWS1MYO7BGPR17SFT2D3POLR2DSAP130UGGT1HS6ST1 LOC101927881LOC151121

DBISCTR RNU4ATACNIFK-AS1 MAP3K2MIR4783LOC105373609LIMS2WDR33AMMECR1L DBISCTR RNU4ATACNIFK-AS1 MAP3K2MIR4783LOC105373609LIMS2WDR33AMMECR1L −0.2 Genes Genes

120 Mb 122 Mb 124 Mb 126 Mb 128 Mb 130 Mb 120 Mb 122 Mb 124 Mb 126 Mb 128 Mb 130 Mb SARS-CoV-2 - Mock 1 1 E1 E1 0 0 −1 −1

1 1 SC SC 0 0

2 2 H3K27ac 0 0

0.25 0.25 H3K9me3 0.00 0.00 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Extended Data Fig.5available under aCC-BY-NC-ND 4.0 International license. -2

a V b CTCF RAD21 SMC3 Mock SARS -Co (24hpi) scale factor:0.9 scale factor:0.88 scale factor:0.97 WB: GAPDH 5 8 4 8 6 WB: CTCF 4 2 4 WB: RAD21 2 0 0 0

Human reads/mouse reads Mock SARS Mock SARS Mock SARS WB: SMC3 -CoV-2 -CoV-2 -CoV-2

gained RAD21 peaks reduced RAD21 peaks RAD21 4 c d 2.0 Mock 60000 SARS-CoV-2 3 50000 1.5 40000 2 1.0 30000 1 ChIP-Seq reads 20000 ChIP-Seq reads 0.5

ChIP-Seq Peak number 10000 0 0 -5.0 -3.0 -1.0 1.0 3.0 5.0 -5.0 -3.0 -1.0 1.0 3.0 5.0 Total Gained Reduced Distance from peak (kb) Distance from peak (kb)

e SMC3 f gained SMC3 peaks reduced SMC3 peaks 4 60000 Mock 2.0 SARS-CoV-2 50000 3

40000 1.5 2 30000 1.0

20000 1 ChIP-Seq reads ChIP-Seq reads 0.5 10000

ChIP-Seq Peak number 0 0 -5.0 -3.0 -1.0 1.0 3.0 5.0 -5.0 -3.0 -1.0 1.0 3.0 5.0 Total Gained Reduced Distance from peak (kb) Distance from peak (kb)

g CTCF h gained CTCF peaks reduced CTCF peaks

Mock 40000 0.8 0.6 SARS-CoV-2 0.5 30000 0.6 0.4

20000 0.4 0.3

0.2

0.2 ChIP-Seq reads 10000 ChIP-Seq reads 0.1 ChIP-Seq Peak number

0 -5.0 -3.0 -1.0 1.0 3.0 5.0 -5.0 -3.0 -1.0 1.0 3.0 5.0 Total Gained Reduced Distance from peak (kb) Distance from peak (kb) bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Extended Data Fig.6available under aCC-BY-NC-ND 4.0 International license.

a b 2 22 P< 5.1

−0.2 2

−0. 0

−0. −2 2

− 22 5 500 1050 510 5 5 500 510 1050 5 510 510

c 2 d 22 P< .2 1.5 1.0 1.0 0.5 0.5 0.0 0.0 −0.5 −0.5 2 5 22 500 5 500 1050 5 1050 510 510 5 510 510 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Extended Data Fig.7available under aCC-BY-NC-ND 4.0 International license.

a b Loops Mock SARS-CoV-2 with quantitative changes (DEseq2) 5.97 5.96 strengthened weakened n = 11,926 4 100 HICUUPS loops from Hi-C 3.0 2 -Log10(FDR)

0 −2 0 2 c d log2 loop FC 6.27 3.99 Loop Sizes

Weakened P<1.33e-66 n=353 100 (by SARS-CoV-2) 1500

1000 Mock SARS-CoV-2 4.93 7.56 loop size (kb) 500 Strengthened 0 n=560 10 (by SARS-CoV-2) Strengthened Weakened

Mock SARS-CoV-2

strengthened weakened e f distances between g loop anchors to the closest TAD boundaries loop anchor distribution SMC3 RAD21 P< relative to TADs P< P< 600 2.35e-21 0.08 3.17e-66 1.12e-74 1 500 1 0.06 400 0 0 300 −1 0.04 −1 200 log2 FC of ChIP log2 FC of ChIP −2 100 −2 0.02 Distance to Boundary (kb) Portion of loop anchors per bin 0 − TAD TAD Boundary Boundary

Anchors of Anchors of Anchors of Anchors of strengthened loops weakened loops strengthened loops weakened loops intra TADs bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Extended Data Fig.8available under aCC-BY-NC-ND 4.0 International license. a b c RNA-Seq RNA-Seq RPB1 Fold changes (Log2) RNA-Seq Fold changes (Log2) Pro-inflammatory 3 IFN genes IFIT3 IFIT1 genes 1.6 IFIT1 IFIT3 P< 2.93e-7 8 P< 9.99e-4 2 IFIT2 IFIT2 6 0.8 6 DDX58 DDX58 1 IFN genes 4 IFN genes 4 IFIH1 IFIH1 0.0 0 IFIT5 2 2 IFIT5 IL6 CCL2 −1 0 −0. 0 CXCL2 CXCL2 Log2 FC −2 −2 CCL2 IL6 −2 genes

genes −1. CXCL8 CXCL8 − − − Pro-inflammatory Pro-inflammatory SARS-CoV-2 IFNb Mock IFNb Mock IFNb

SARS-CoV-2 SARS-CoV-2 d DDX58 loci e IFIH1 loci Mock SARS-CoV-2 Mock SARS-CoV-2

−2 − 2 10 10

ACO1 TOPORS ACO1 TOPORS −3 DDX58 DDX58 IFIH1 IFIH1 − 3 NDUFB6 NDUFB6 10 FAP FAP Genes Genes 10 Genes Genes

32.35 Mb 32.4 Mb 32.45 Mb 32.5 Mb 32.55 Mb 32.6 Mb 32.35 Mb 32.4 Mb 32.45 Mb 32.5 Mb 32.55 Mb 32.6 Mb 163 Mb 163.05 Mb 163.1 Mb 163.15 Mb 163.2 Mb 163 Mb 163.05 Mb 163.1 Mb 163.15 Mb 163.2 Mb

2.5 2.5 2.5 2.5 CTCF 0.0 0.0 CTCF 0.0 0.0

RAD21 25 25 25 25 0 0 RAD21 0 0

10 10 H3K27ac * * * * 5 5 0 0 H3K27ac 0 * * 0 * *

H3K4me3 10 10 10 10 0 0 H3K4me3 0 0 DDX58 DDX58 IFIH1 IFIH1 (DDX58-e)

IFN gene transcription - C score only DDX58 f g IFN gene transcription - A score only i 15 Pearson R:0.13 Pearson R:0.33 10 2 2 *** 5 *** 0 0

Relative Expression Level 0 Poly(I:C) - + - + - + −2 −2

−0.5 0.0 −0.5 0.0 8-e 8-e log2 FC of transcriptional changes (RPB1 ChIP-Seq) log2 FC of transcriptional changes (RPB1 ChIP-Seq) gRNA1 gRNA2 Log2 FC of C score Log2 FC of A score Control DDX5 DDX5 CRISPRi

200 IFIT1 100 IFIT3

h 80 Mock 150 SARS-CoV-2 ** TAD TAD 60 * 100 ** *** *** ** ** *** 40 ** ** RAD21 25 25 50 20 10 10 H3K27ac * * * * * * Relative Expression Level 0 Relative Expression Level 0 - Poly(I:C) - + - + - + - + - + - + IFIT3 IFIT1 IFIT3 IFIT1 Poly(I:C) - + - + - + - + - + +

(IFIT-e1) (IFIT-e2) (IFIT-e3) IFIT-e1 IFIT-e1 IFIT-e2 IFIT-e3 IFIT-e3 IFIT-e1 IFIT-e1 IFIT-e2 IFIT-e3 IFIT-e3 gRNA1 gRNA2 gRNA1 gRNA1 gRNA2 Control Control gRNA1 gRNA2 gRNA1 gRNA1 gRNA2 CRISPRi CRISPRi bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Extended Data Fig.9available under aCC-BY-NC-ND 4.0 International license.

a Pro-inflammation gene transcription b - ABC scores Pearson R:0.51 IL-6 CXCL8 2 2 4 1 1 2 0 0 0 Log2 FC −1 −1 True Log2 FC Pol2 ChIP-Seq

−2 −2 −2 −2 0 2 4 TXN ABC ABC-P ABC-P2 TXN ABC ABC-P ABC-P2

log2 FC of transcriptional changes (RPB1 ChIP-Seq) Log2 FC of ABC scores *TXN = true transcription change by RNA Pol2 ChIP-Seq

c d e CXCL8 locus H3K4me3 peaks Genes close to virus-gained H3K4me3 peaks Mock 30000 TNF-alpha signaling via NF-kB 25000 TGF-beta Signaling 20000 15000 UV Response Dn −2

10 Peak numbers 10000 Epithelia Mesenchymal Transition 5000 Inflammatory Response −3 0 10 Total Gained Reduced 0 4 8 12 SARS-CoV-2 -log10(p-value) 25 Mock f 0 Top DNA motifs 25 RAD21 SARS-CoV-2 in gained H3K4me3 peaks 0 10 GT G CC G GT A G T G Mock AA CCGA C AG GT TGGT A GCAC T CA AA A A TA G C C T A CG CA A T C G G GT T C A C G C A T C G G CC T T T T C T G CAA TG 0 C TG A CTT CTA AAGT 10 H3K4me3 SARS-CoV-2 IRF2, P-value = 1e-27, Fosl2, P-value = 1e-24, 0 6.95% Targets, 2.24% Background 10.47% Targets, 4.61% Background 10 * * Mock 0 C 10 GA C TCA H3K27ac G GT A T TA C G C T * SARS-CoV-2 A T A C A A C C T * C T CG T CG T C T C T CG T CG TA TAGAG T G CG C TG T G T G T G TA A C TC AG AC G C C G A GA GA C GG 0 AA AA TGA TCA Genes CXCL8 IRF1, P-value = 1e-19, Jun-AP1, P-value = 1e-23, 74.5 Mb 74.54 Mb 74.58 Mb 74.62 Mb 74.66 Mb 8.03% Targets, 3.44% Background 8.03% Targets, 3.14% Background bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

581 Extended Data Figure legends: 582 Extended Data Figure 1. Overview of several layers of 3D chromatin architectures; overview and 583 quality control of viral infection in our study. 584 a. A diagram showing the typical contact map patterns in Hi-C (and Hi-C 3.0 or other modified Hi-C 585 approaches) that define A/B compartments, Topologically Associating Domains (TADs), chromatin loops 586 or intra-TAD interactions (which perhaps include most enhancer-promoter contacts). This is an overall 587 summary of these structures, but the exact definition of some structures may be subjected to variable 588 interpretation, and the terminology may not always be used consistently5–7,15. 589 Often, A/B compartmentalization is illustrated by a checkerboard pattern of Hi-C contact 590 matrices over large genomic sizes, indicating preferential interactions between genomic regions belonging 591 to the same type of compartments (A: euchromatin and transcriptionally active; B: heterochromatin, 592 transcriptionally inactive). TADs or chromatin domains are often characterized as a square or triangle-like 593 structure on chromatin contact maps, reflecting a higher contact frequency between any regions inside the 594 same TAD than with regions outside of the TAD. Intra-TAD enhancer-promoter contacts are considered 595 to be facilitated by TADs, while TAD boundaries prevent aberrant interaction with regions outside of 596 TADs.

597 In Hi-C maps, the dot-shaped structures on the tip of domains suggests local enrichment of spatial 598 interaction between a pair of two loci over nearby regions, and is regarded as a chromatin loop in this 599 work. But loops may be subjected to other definitions in other studies. For example, enhancer-promoter 600 contacts often do not appear as dot-shaped structures in Hi-C, but may be defined as loops by other work 601 or other methods. Additional discussion, see5,7.

602 b. Cartoon diagrams describe A-A and B-B association preference within regions of similar epigenetic 603 features, which compartmentalizes chromosomes into A and B (the left part of the diagram). The diagram 604 in the middle depicts a current model of cohesin loop extrusion inside TADs that generated such 605 structures. The right side shows a zoom-in view of a part of a TAD that harbors enhancer-promoter 606 contact that may play roles in gene transcriptional regulation. 607 c. A workflow showing the experimental design. 608 d. A barplot showing the percentage of RNA-Seq reads mapped to SARS-CoV-2 genome in Mock, 6-hr 609 post infection (6hpi, 0.1 MOI), and 24 hpi (0.1 MOI) conditions. Mean and standard deviation (error bar) 610 were calculated based on two biological replicates of RNA-Seq. 611 e. Confocal images showing immunofluorescence staining of DAPI (DNA, blue) and the Spike protein of 612 SARS-CoV-2 (red) in Mock and 24hpi (0.1 MOI) infected A549-ACE2 cells. Scale bars are shown. 613

21 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

614 Extended Data Figure 2. Replicates consistency of Hi-C 3.0, and the increase of trans-chromosomal 615 interactions after SARS-CoV-2 infection. 616 a,b. Barplots showing the SCC correlation coefficients13 between two Hi-C 3.0 replicates of Mock or 617 SARS-CoV-2 conditions for different chromosomes. 618 c. Snapshots showing two replicates of Hi-C contact matrices and compartmental score (E1) tracks in the 619 same genomic region shown in Fig.1b. The left two matrices show data of replicate 1 (rep1), and the right 620 two matrices show replicate 2 (rep2). Green boxes show two regions with increased A-B compartmental 621 mixing or weakened A compartment after virus infection. 622 d. A heatmap shows the log2 fold change of inter-chromosomal interactions between two pairs of any two 623 chromosomes (SARS-CoV-2/Mock). 624 e. A barplot showing the trans/cis Hi-C contacts ratio in two replicates of Mock or SARS-CoV-2 infected 625 cells. Trans contacts indicate chromatin interactions formed in between two different chromosomes. Mean 626 and standard deviation (error bar) were calculated based on two biological replicates of Hi-C. 627 628 629 Extended Data Figure 3. Additional data of A/B compartmental changes. 630 a. A barplot showing the percentage of genomic bins (100kb bin size) that can be categorized into six 631 groups based on their compartmental score changes (E1 value). These six categories are: A to weaker A, 632 A to B, B to stronger B, B to weaker B, B to A, and A to stronger A. 633 b. A diagram showing the basis of sliding correlation score to examine changes of compartmental 634 interactions based on Pearson’s correlation matrices (see9, see Methods). On the right, a meta-profile plot 635 of SC (sliding correlation) scores near the A/B transition (compartmental domain boundaries). Grey: 636 Mock; Red: SARS-CoV-2. Compartmental domains were defined with E1 scores using HOMER 637 (findHiCCompartments.pl)(see Methods). 638 c. Snapshots of inter-chromosomal Hi-C contact matrices between chr17 and chr18 (upper), and intra- 639 chromosomal Hi-C contact matrices within chr18 (lower) in Mock and SARS-CoV-2 infected samples. 640 On the right, differential contacts are shown as log2 fold changes of SARS-CoV-2/Mock. PCA E1 scores 641 were put at sides to show the A or B compartments. Red and black arrowheads respectively point to 642 increased A-B and reduced A-A interactions after SARS-CoV-2 infection. 643 644 645 Extended Data Figure 4. Calibrated ChIP-Seqs demonstrate epigenome reprogramming by SARS- 646 CoV-2 infection.

22 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

647 a. A diagram illustrating the design of spike-in calibrated ChIP-Seq using mouse ESCs as spike-in for 648 human A549 cells (with or without infection). 649 b,c. Barplots showing the human/mouse reads ratio in both Mock and SARS-CoV-2 conditions which 650 permit calibrated ChIP-Seq analyses of H3K27ac, H3K4me3, H3K27me3, and H3K9me3. A scale factor 651 for each histone mark ChIP-seq was denoted above each plot. 652 d,e,f,g. Scatter plots show virus-caused genome-wide changes of histone mark ChIP-Seq signals at 100kb 653 bins for H3K27ac, H3K4me3, H3K9me3 and H3K27me3. The x,y-axis are natural logarithmically scaled 654 reads densities from calibrated ChIP-Seq data. Dotted lines denote changes by two folds. 655 h. Snapshots of Pearson’s correlation matrices, E1 compartmental scores, sliding correlation scores (SC), 656 as well as ChIP-Seq tracks of H3K27ac and H3K9me3 in Mock or SARS-CoV-2 infected cells. The right 657 side shows the difference of Pearson’s correlation matrices between SARS-CoV-2 and Mock (pink shows 658 decrease). Red arrowheads on top of the H3K27ac peaks show strong reduction of this active mark after 659 infection, which was accompanied by quantitative increase of H3K9me3 signals at the same region (black 660 arrowheads). Accordingly, this entire A compartment shows reduced PCA E1 scores (yellow in the E1 661 track), showing less compartmental interactions within the same compartment but more interactions with 662 nearby B compartments (see the differential Pearson’s correlation matrices to the right). 663 664 665 Extended Data Figure 5. Cohesin depletion specifically from intra-TAD regions after SARS-CoV-2 666 infection. 667 a. Western blots showing the protein abundance of cohesin (RAD21, SMC3) and CTCF in Mock and 668 SARS-CoV-2 infected cells. GAPDH was used as a loading control. 669 b. Barplots showing the human/mouse reads ratio in both Mock and SARS-CoV-2 conditions that permit 670 calibrated ChIP-Seqs of CTCF, RAD21, SMC3 and RNA Pol2 (RPB1). These factors were not globally 671 affected by virus infection (so the ratios are comparable in mock and infected conditions). 672 c. Barplot showing the number of total, gained or lost RAD21 ChIP-Seq peaks after SARS-CoV-2 673 infection at 24hpi. 674 d. Profile plots showing the signals of RAD21 ChIP-Seq on the gained or lost ChIP-Seq peaks in Mock 675 and SARS-CoV-2 conditions. 676 e,f,g,h. Similar to panels c,d, these panels are generated based on calibrated ChIP-Seqs of SMC3 and 677 CTCF respectively. 678 679

23 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

680 Extended Data Figure 6. Globally weakened intra-TADs chromatin interactions and the association 681 with altered repressive histone modifications. 682 a. A boxplot showing the log2 fold changes of intra-TAD Hi-C contacts for six categories of TADs. All 683 TADs are ranked based on the quantitative changes of intra-TADs interactions and the six categories 684 include the Top 5%, top 5~10%, 10~50%, 50~90%, bottom 5~10%, and the bottom 5%, respectively. 685 b,c,d. Boxplots showing the log2 fold changes of H3K27ac, H3K9me3 and H3K27me3 ChIP-Seq signals 686 in the six categories of TADs as shown in panel a. P-value in panel d: Mann-whitney U test. 687 For all boxplots, the centre lines represent medians; box limits indicate the 25th and 75th percentiles; and 688 whiskers extend 1.5 times the interquartile range (IQR) from the 25th and 75th percentiles. 689 690 691 Extended Data Figure 7. Dot-shaped chromatin loops are largely unaltered after SARS-CoV-2 692 infection, but a subset of them are changed. 693 a. Aggregated peak analysis (APA) shows the strength of all chromatin loops (observed/expected) in 694 Mock (left) and SARS-CoV-2 (right) infected cells for 11,926 dot-shaped loops called by HICCUPS 695 algorithm. 696 b. Volcano plot generated by DEseq2 using the two replicates of Hi-C 3.0 that defines quantitatively 697 changed loops after SARS-CoV-2 infection (FDR < 0.1, see Methods25). 698 c. APA plots for the subsets of virus-weakened and strengthened loops. The numbers of such loops are 699 shown. For APA plots in panels a and c, the bin size for plotting the heatmap is 5kb, and the heatmaps 700 show genomic regions +/- 100kb surrounding the loop anchors. The numbers on the heatmaps indicate the 701 central pixel values. 702 d. A boxplot showing the loop sizes of strengthened and weakened loops. P-values: Mann-whitney U test. 703 e. Boxplots showing the virus induced fold changes of cohesin binding (measured by calibrated cohesin 704 ChIP-Seq reads) on the loop anchors of the two groups of loops: those quantitatively strengthened or 705 weakened. P-values: Mann-whitney U test. 706 f. Boxplots showing the distances of each loop anchor to its closest TAD boundary for two groups of 707 virus-affected loops: those quantitatively strengthened or weakened. P-values: Mann-whitney U test. 708 g. The distribution of loop anchors based on locations relative to its hosting TAD for the two groups of 709 virus-affected loops: those quantitatively strengthened or weakened. Y-axis indicates the percentage of 710 loop anchors in each position relative to hosting TADs. 711 For all boxplots, the centre lines represent medians; box limits indicate the 25th and 75th percentiles; and 712 whiskers extend 1.5 times the interquartile range (IQR) from the 25th and 75th percentiles. 713

24 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

714 Extended Data Figure 8. SARS-CoV-2 disrupts chromatin architecture to antagonize the 715 transcriptional activation of interferon response genes. 716 a. Boxplots showing the expression deregulation of key interferon response (IFN) and pro-inflammatory 717 genes after SARS-CoV-2 infection or IFN-beta treatment (1000u, 6-hr), as shown by RNA-Seq changes. 718 For boxplots, the centre lines represent medians; box limits indicate the 25th and 75th percentiles; and 719 whiskers extend 1.5 times the interquartile range (IQR) from the 25th and 75th percentiles. P-value: 720 Mann-whitney U test. 721 b,c. Heatmaps of select IFN or pro-inflammatory genes showing their fold changes in Pol2 ChIP-Seq or 722 RNA-seq after SARS-CoV-2 infection or IFN-beta treatment (1000u, 6-hr). 723 d,e. Snapshots of Hi-C contact matrices and calibrated ChIP-Seq tracks for indicated factors at two key 724 loci coding for virus RNA sensors: DDX58 (coding for RIG-I) and IFIH1 (coding for MDA5). Left: 725 Mock; right: SARS-CoV-2. Blue arrows point to reduced dot-shaped loops. The intra-TAD interactions 726 were weakened throughout these two TADs. Red asterisks show reduced H3K27ac peaks by virus 727 infection. Green arrows show H3K4me3 peaks that are not changed for these genes. 728 f. A scatter plot showing a poor correlation between the C score only (from Hi-C contact, x-axis) and the 729 true transcriptional changes of IFN genes by SARS-CoV-2 (y-axis, RPB1 ChIP-Seq). A liner regression 730 fitted line and its 95% confidence interval are also shown. 731 g. Similar to panel f, this is a scatter plot showing a poor correlation between the A score only (from 732 enhancer H3K27ac activity, x-axis) and the true transcriptional changes of IFN genes by SARS-CoV-2 733 (y-axis, RPB1 ChIP-Seq). 734 h. (Left) Design of gRNAs/CRISPRi that target the weakened enhancers seen in SARS-CoV-2 infected 735 cells (the red asterisks); this IFIT locus is also shown in Fig.4b with more information there. (Right) RT- 736 qPCR results showing that CRISPRi inhibition of the enhancers reduced IFIT gene expression in response 737 to poly(I:C), a synthetic viral mimicry. gRNA1 and gRNA2 are two gRNAs targeting the same enhancer 738 (see Extended Data Table 2). 739 i. Similar to panel h, this is the RT-qPCR result showing that CRISPRi inhibition of the enhancer in 740 DDX58 locus reduced its response to poly(I:C). The DDX58 enhancer location for the CRISPRi is shown 741 in panel d. Data in panels h,i show Mean +/- SD from three biological replicates (n=3); p values: two 742 tailed student’s T-test (*, p<0.05; **, p<0.01; ***, p<0.001) 743 744 745 Extended Data Figure 9. Pro-inflammatory genes were induced by SARS-CoV-2 via augmented 746 promoter activity.

25 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.20.453146; this version posted July 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

747 a. A scatter plot showing good correlation between the ABC score (x-axis) and the true transcriptional 748 changes of pro-inflammatory (PIF) genes by SARS-CoV-2 (y-axis, RPB1 ChIP-Seq). But the true 749 changes (y axis) are much higher than the modeled changes based on ABC scores (x-axis), as all the data 750 points are above the diagonal (also see Fig.4f for revised ABC-P2 scores). A liner regression fitted line 751 and its 95% confidence interval are also shown. 752 b. Bar graphs showing the fold changes of two key PIF genes, IL-6 and CXCL8, in several conditions: 753 true RPB1 ChIP-Seq fold changes after SARS-CoV-2 infection (TXN); fold changes modeled by ABC 754 score; modeled by ABC algorithm with inclusion of promoter H3K4me3 strength (ABC-P); modeled by 755 ABC algorithm with inclusion of a square of promoter H3K4me3 strength (ABC-P2). Promoter strength is 756 required to revise the ABC algorithm to model transcriptional increases of pro-inflammatory genes after 757 SARS-CoV-2 infection. 758 c. Snapshots of Hi-C contact matrices and calibrated ChIP-Seq tracks for indicated factors at another key 759 gene loci coding for pathologically critical proinflammatory cytokines in COVID-19 patients: CXCL8 760 (a.k.a. IL-8). Red asterisks show reduced H3K27ac peaks. Green arrows show increased H3K4me3 peaks 761 at its promoter by SARS-CoV-2 infection. The intra-TAD interactions were weakened throughout the 762 TAD. 763 d. Barplot showing the numbers of total, gained or reduced H3K4me3 ChIP-Seq peaks after SARS-CoV- 764 2 infection for 24hpi. 765 e. Hallmark signature analysis of genes close to H3K4me3 peaks gained in virus-infected condition show 766 gene signatures associated with TNF-alpha, TGF-beta signaling or inflammatory responses, which are 767 associated with pathological symptoms in COVID-19 patients. 768 g. Motif analysis of H3K4me3 peaks increased in SARS-CoV-2 infected cells show that the top ranked 769 motifs are IRF1/2 and Jun/AP1, suggesting their potential roles in transcriptional activation of 770 inflammation genes. Motif analysis was done by HOMER, and the P values and percentages of sites with 771 motifs are shown. 772 773 Extended Data Table 1. A summary of the Hi-C 3.0, calibrated ChIP-Seq, RNA-Seq datasets generated 774 in this study. 775 776 Extended Data Table 2. Primers and oligos used in this study. 777 778 Extended Data Table 3. Gene lists of IFN (interferon response genes) and PIF (pro-inflammatory) 779 genes used in this study. 780