key: cord-0901661-2ray8vrg
authors: Bermudez, Yahaira; Miles, Jacob; Muller, Mandy
title: Nonstructural protein 1 (nsp1) widespread RNA decay phenotype varies among Coronaviruses
date: 2022-04-19
journal: bioRxiv
DOI: 10.1101/2022.04.19.488803
sha: 76d06971cdf880221728f9c1bda5539387cbaccd
doc_id: 901661
cord_uid: 2ray8vrg

Extensive remodeling of the host gene expression environment by coronaviruses nsp1 proteins is a well-documented and conserved piece of the coronavirus-host takeover battle. However, whether and how the underlying mechanism of regulation or the transcriptional target landscape differ amongst coronaviruses remains mostly uncharacterized. In this study we use comparative transcriptomics to investigate the diversity of transcriptional targets between four different coronavirus nsp1 proteins (from MERS, SARS1, SARS2 and 229E). In parallel, we performed Affinity Purification followed by Mass-Spectrometry to identify common and divergent interactors between these different nsp1. For all four nsp1 tested, we detected widespread RNA destabilization, confirming that both α- and β-Coronavirus nsp1 broadly affect the host transcriptome. Surprisingly, we observed that even closely related nsp1 showed little similarities in the clustering of genes targeted. Additionally, we show that the RNA targeted by nsp1 from the α-CoV 229E partially overlapped with MERS nsp1 targets. Given MERS nsp1 preferential targeting of nuclear transcripts, these results may indicate that these nsp1 proteins share a similar targeting mechanism. Finally, we show that the interactome of these nsp1 proteins differ widely. Intriguingly, our data indicate that the 229E nsp1, which is the smallest of the nsp1 proteins tested here, interacts with the most host proteins, while MERS nsp1 only engaged with a few host proteins. Collectively, our work highlights that while nsp1 is a rather well-conserved protein with conserved functions across different coronaviruses, its precise effects on the host cell is virus specific. Significance Coronaviruses extensively co-opt their host gene expression machinery in order to quicky benefit from the host resources. The viral protein nsp1 plays a major role in this takeover as nsp1 is known to induce a widespread shutdown of the host gene expression, both at the RNA and the translational level. Previous work characterized the molecular basis for nsp1-mediated host shutdown. However, this was mostly conducted in the context of β-coronaviruses and in particular SARS-CoV1, CoV2 and MERS due to the important public health burden that these viruses represent. Here instead, we explored the impact of nsp1 on the host using a comparative approach, defining the influence of 4 nsp1 protein from α- and β-coronaviruses. We delineated the impact of these 4 nsp1 on the host transcriptome and mapped their interactome. We revealed that host target range and interactomes vary widely among different nsp1, suggesting a viral-specific targeting. Understanding how these differences shape infection will be important to better inform antiviral drug development.

The past 20 years have seen the emergence of three highly pathogenic human 

Intriguingly, only a and b-CoV encode nsp1, whereas g-and d-CoV lack this protein [16, 72 2, 17, 3, 18, 19] . The sizes of nsp1 in b-CoV also differ from the a-CoVs nsp1, with the a-CoVs 73 nsp1 being substantially smaller than their β-CoVs counterparts. While these differences may 74 have important consequences on the role of nsp1 during infection, it appears that nsp1 proteins 75 from HCoV-229E and HCoV-NL63 might still be able to bind the 40S ribosomal subunit to affect 76 host mRNA stability [20, 21, 17] reminiscing of how the b-CoV nsp1 trigger host shutoff.

The extensive study of the role of SARS2, SARS1 and MERS nsp1 has revealed a 78 pervasive role in reshaping the host gene expression environment. In this study, we set out to 79 compare the impact of nsp1 on the host cell not just from the highly pathogenic b-CoV but also 80 from the a-CoVs 229E. We hypothesized that the extent of nsp1-mediated RNA decay may vary 81 amongst the different HCoV, which could account for some of the severity of these infections. To 82 address this possibility, we generated a library of nsp1-inducible cells from 4 HCoV coronaviruses 83 and explored the extent of nsp1 effect on the host transcriptome by RNA-seq. Interestingly, we 84 found that widespread targeting of RNA is conserved among these nsp1 but the range of targets 85 is different. Moreover, we investigated the interactome of these nsp1 proteins by mass 86 spectrometry to identified common and divergent interactors and identified factors that may 87 contribute to nsp1 targeting of RNA. This work provides important insights into the fundamental 88 differences between highly pathogenic and common coronaviruses nsp1 and refines our 89 understanding of nsp1-meidated decay. 

However, to date, it remains unclear how diverse is the impact on the host transcriptome between 96 a-and b-coronavirus nsp1 proteins. In order to assess the influence of different coronavirus nsp1 97 proteins on the host gene expression environment, we generated a library of nsp1-inducible cell 98 lines using the nsp1 protein of 4 different HCoV. To generate this library, along with SARS2 and 99 MERS, we constructed 229E and SARS1 coronavirus nsp1 lentiviral plasmids derived from the 100 pLVX-TetOne-Zeo-CoV2-nsp1-3xFlag gifted to us by the Glaunsinger Lab. Following production 101 of lentivirus plasmids, we performed lentiviral transduction using select pLVX CoV nsp1-Flag 102 plasmid and pMD2.G and psPAX2 envelop and packaging plasmids (Fig. 1A) . After transduction, 103 cells underwent selection using zeocin at a concentration of 325ug/ml. Following selection, to 104 verify that our cell lines were inducible and would produce CoV nsp1 protein, cells were induced 105 with 1ug/ml doxycycline. 24 hours post induction samples were collected and protein samples 106 were western blotted with a Flag antibody (Fig. 1B) . All four nsp1 proteins express well under 107 induction and at the expected size. Once we verified that all four CoV nsp1 proteins could be 108 properly expressed, we next determined if these induced nsp1 regulated gene expression as 109 expected. As reported before, nsp1 can efficiently degrade GFP transcripts and reduce GFP 110 expression by itself [9, 11]. We thus next expressed a GFP reporter, then the transduced cells 111 were either left in an uninduced state, or induced with doxycycline. 24 hours post transfection and 112 induction, GFP expression was measured with fluorescent microscopy (Fig. 1C) and quantified 113 using ImageJ (Fig. 1D) . As expected, induction led to a significant decrease in the intensity of 114 GFP expression in all CoV nsp1 expressing cells. This is in line with the observation that despite 115 being a smaller protein, the 229E nsp1 is able to regulate the expression of genes, likely due to Coronavirus nsp1 comparative RNA-seq shows differences in gene expression.

After confirming that our inducible cell lines function as expected, we sought to explore the 121 differences in the extent of nsp1 targeting on the host transcriptome. Using the lentivirally 122 transduced cell lines described above, we induced the expression of nsp1 from SARS1, SARS2,

MERS, or 229E (using uninduced cells as controls). 24h post induction, total RNA was extracted,

polyA selected and cDNA library were prepared and sequenced. Out of a total of 19331 genes 125 identified by RNA-seq, 15779 genes appeared in all 4 datasets (Supplementary Table 1 ). More 126 specifically, we identified 3121 genes that were consistently down-regulated upon expression of 127 all 4 nsp1 and 3833 that were consistently upregulated ( Fig. 2A) . Unsurprisingly, 229E nsp1, as 128 the only representative of an a-CoV, has the most unique pattern of genes up and down regulated,

perhaps indicating that its targeting mechanism differs from that of the b-CoV nsp1. Furthermore,

fold change patterns induced by 229E and MERS nsp1 as observed by volcano plots were non-131 standard as opposed to SARS1 and SARS2 plots (Fig. 2B) . This might suggest that the fold 132 change distribution may not follow a normal distribution. Previous studies had indicated that 133 MERS preferentially targets nuclear mRNA while our data here represents whole cell RNA pools,

which could skew our data representation. This might suggest that 229E nsp1 similarly only 135 targets a subset of RNA in cells. We next performed hierarchical clustering on this comparative 136 transcriptomics dataset. Figure 2C shows a heatmap of the correlation matrix across all 137 transcripts. In line with previous analyses, our data indicates that all the nsp1 tested trigger 138 massive RNA degradation with >50% of the detected genes downregulated upon each nsp1 139 expression (Fig. 2D) . Based on our analysis, between 30 and 45% of genes had a fold change 140 between 1 and 2, indicating that nsp1 had little to no effect on them. Surprisingly, we also detected 141 close to 15% of genes that seem to be upregulated upon nsp1 induction. Overall, our data 142 indicates that a-and b-coronavirus nsp1 share the ability to widely trigger RNA decay however, 9 the precise target of each of the nsp1 differs, suggesting that the host cell is likely differentially 

RNA-seq provided an extensive range of data about the effects of expression of the different 148 coronavirus nsp1s on host gene expression. After data sorting and processing, we noticed some 149 genes with particular patterns of degradation upon expression of the nsp1. We thus next wanted 150 to validate these patterns by RT-qPCR (Figure 3 ). The first gene that we tested was ANKRD1.

There are multiples links between ANKRD1 and coronaviruses, for example upon Porcine 12 were unique to 229E nsp1, 8 to MERS nsp1, 21 to SARS1 nsp1 and 9 to SARS2 nsp1 190 (Supplementary Table 2, Fig 4B) . 

We also verified our results using the SARS2 (R124A+K125A) and MERS 259 (R146A+K147A) nsp1 mutants, confirming that these mutants were not able to induce RNA decay 260 like their WT counterparts. As it has been suggested before, while these mutations lead to a loss 261 of mRNA decay potential in the nsp1 protein, this is likely not be linked directly to nsp1 containing

RNAse activity. As no nsp1 sequence contains amino acid sequences associated with RNA 263 catalysis, the mutations could potentially disrupt interactions with a protein that does may play 264 this role for nsp1.

Another challenging aspect of studying nsp1 biology is that it has been difficult to identify 111 amino acids to fold into complex structures. Given that MERS nsp1 is known to operate very 277 differently than the nsp1 from SARS1 and SARS2, it is perhaps less surprising to see that its 17 interactome is also very different. While we did not identify in this interactome any host protein 279 with direct RNAse activity that could account for nsp1's role in RNA decay, our gene ontology 280 revealed that many of the interactors that we detected were associated with mRNA catabolic 281 process. It would thus be interesting to explore these interactions and assess whether any of 282 these factors are essential for nsp1 activity.

In this work we sought to explore the similarities and differences between key RNA RT-qPCR. Total RNA was harvested using TRIzol according to the manufacture's protocol.

cDNAs were synthesized from 1 μg of total RNA using AMV reverse transcriptase (Promega) and 383 used directly for quantitative PCR (qPCR) analysis with the SYBR green qPCR kit (Bio-Rad).

Signals obtained by qPCR were normalized to those for 18S. 

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