PERSPECTIVE published: 11 May 2022 doi: 10.3389/fmolb.2022.893067 Phase Separation Drives SARS-CoV-2 Replication: A Hypothesis Andrea Vandelli 1,2†, Giovanni Vocino 3† and Gian Gaetano Tartaglia 4,5,6* 1 Department of Biochemistry and Molecular Biology, Universitat Autònoma de Barcelona, Barcelona, Spain, 2Universitat Pompeu Fabra (UPF), Barcelona, Spain, 3Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy, 4Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy, 5Department of Biology ‘Charles Darwin’, Sapienza University of Rome, Rome, Italy, 6Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain Identifying human proteins that interact with SARS-CoV-2 genome is important to understand its replication and to identify therapeutic strategies. Recent studies have unveiled protein interactions of SARS-COV-2 in different cell lines and through a number of high-throughput approaches. Here, we carried out a comparative analysis of four experimental and one computational studies to characterize the interactions of SARS- CoV-2 genomic RNA. Although hundreds of interactors have been identified, only twenty- one appear in all the experiments and show a strong propensity to bind. This set of interactors includes stress granule forming proteins, pre-mRNA regulators and elements involved in the replication process. Our calculations indicate that DDX3X and several editases bind the 5′ end of SARS-CoV-2, a regulatory region previously reported to attract Edited by: a large number of proteins. The small overlap among experimental datasets suggests that Barbara Bardoni, UMR7275 Institut de Pharmacologie SARS-CoV-2 genome establishes stable interactions only with few interactors, while many Moléculaire et Cellulaire (IPMC), France proteins bind less tightly. In analogy to what has been previously reported for Xist non- Reviewed by: coding RNA, we propose a mechanism of phase separation through which SARS-CoV-2 Venu Raman, progressively sequesters human proteins hijacking the host immune response. Johns Hopkins Medicine, United States Keywords: viral RNA, phase separation, stress granules, protein-RNA interactions, RNA-binding proteins Lorena Zubovic, University of Trento, Italy *Correspondence: INTRODUCTION Gian Gaetano Tartaglia gian.tartaglia@iit.it Identification of viral interactions within the host cell can lead to the design of novel strategies against † These authors share first authorship infection. Recently, different high-throughput strategies have been implemented to characterize host interactions with SARS-CoV-2 proteins and genomic RNA. Specialty section: Non-structural proteins of SARS-CoV-2 have been used for affinity purification to retrieve host This article was submitted to binding partners using mass spectrometry in HEK-293T/17 cells (Gordon et al., 2020). A total of 332 RNA Networks and Biology, interactions between human and SARS-CoV-2 proteins have been identified. Around 40% of SARS- a section of the journal CoV-2 interacting proteins are associated with vesicle trafficking pathways and endomembrane Frontiers in Molecular Biosciences compartments. Received: 09 March 2022 Here, we focus on four experimental studies aiming to characterize interactions with SARS-CoV- Accepted: 25 April 2022 2 genomic RNA. Published: 11 May 2022 In one experiment, a multi-omic approach was employed to identify which viral and human Citation: RNA-binding proteins (RBPs) are involved in SARS-CoV-2 infection (Kamel et al., 2021). The Vandelli A, Vocino G and Tartaglia GG (2022) Phase Separation Drives SARS- “comparative RNA interactome capture” (cRIC) method was developed to find in which way the CoV-2 Replication: A Hypothesis. RNA-bound proteome responds to the infection. The results show that SARS-CoV-2 genome is the Front. Mol. Biosci. 9:893067. epicenter of critical interactions with host proteins: many cellular RBP networks are remodeled upon doi: 10.3389/fmolb.2022.893067 SARS-CoV-2 infection and around 300 proteins are affected, mostly related to RNA metabolic Frontiers in Molecular Biosciences | www.frontiersin.org 1 May 2022 | Volume 9 | Article 893067 Vandelli et al. Comparative Analysis of SARS-CoV-2 Interactions processes and antiviral defenses. A second approach called “viral approach (Bellucci et al., 2011; Agostini et al., 2013b; Cirillo RNA interactome capture” (vRIC) was employed to identify et al., 2017) to compute >100000 human protein interactions with cellular and viral proteins interacting with SARS-CoV-2 SARS-CoV-2 genomic RNA (Vandelli et al., 2020). The 5′ and 3′ genomic RNA (Kamel et al., 2021). Inhibition of specific end of SARS-CoV-2 were found to be highly structured, in proteins interacting with viral RNA was shown to impair agreement with subsequent experimental reports (Manfredonia SARS-CoV-2 infection. et al., 2020) and show strong propensity to interact with human In another study (Lee et al., 2021), the repertoire of host proteins. Among the identified interactors we identified there are proteins associated with SARS-CoV-2 and HCoV-OC43 several RNA editases and ATP-dependent RNA helicases that genomes was identified. The work relies on a robust play a role in viral RNA processing and have a high propensity to nucleoprotein (RNP) capture protocol. More than 100 host participate in large macromolecular complexes. A number of factors directly binding to SARS-CoV-2 RNA were detected. proteins are predicted to be sequestered by SARS-CoV-2 genome By applying RNP capture on HCoV-OC43, evolutionary and their recruitment contributes is thought to modify both the conserved interactions between the viral RNAs and the host transcriptional and post-transcriptional regulations of host cells. proteins could be identified. Upon knockdown experiments Here, we analyzed four experimental and one computational and transcriptome analysis, Lee et al. identified 17 antiviral studies on human RBPs interactions with SARS-CoV-2 genomic and 8 pro-viral RBPs that have a role in several steps of the RNA. We exploited the catRAPID algorithm to estimate the mRNA life cycle. The authors identified La-related protein 1 ability of proteins to bind SARS-CoV-2 and identified a tight (LARP1), a downstream target of the mTOR signaling pathway, correlation between the number of experiments in which a as an important antiviral host factor that interacts with SARS- specific protein is detected experimentally and its predicted CoV-2 RNA. binding affinity. Finally, we propose a model in which SARS- Another group exploited an approach in which a CoV-2 RNA promotes the formation of a phase-separated comprehensive identification of RBPs followed by mass assembly by sequestering specific human proteins. spectrometry (ChIRP-MS) led to the identification of host proteins that bind SARS-CoV-2 genomic RNA during active infection (Flynn et al., 2021). The results were corroborated RESULTS with analyses from three RNA viruses and contributed to characterize the specificity of virus-host interactions. Flynn Interactomes Comparison et al. also carried out a series of targeted CRISPR screens that To retrieve interactions relevant for SARS-CoV-2 infection, we highlighted the fact that a big portion of functional RNA-binding analysed four protein-RNA interactome experiments proteins act as host’s protectors from virus-induced cell death. (Supplementary Material S1). Comparative CRISPR screens, performed across seven RNA Twenty-one proteins were found in common to the four datasets viruses, reveal both shared and SARS-specific antiviral factors. (Flynn et al., 2021; Kamel et al., 2021; Lee et al., 2021; Schmidt et al., By combining the RNA-centric approach and the functional 2021) (Figure 1A). The list includes PABPC1 (Polyadenylate- CRISPR screens, the authors found a functional connection binding protein 1), SND1 (Staphylococcal nuclease domain- between SARS-CoV-2 and mitochondria, showing that this containing protein 1), PPIA (Peptidyl-prolyl cis-trans isomerase organelle is a platform for antiviral activity. A), DDX3X (ATP-dependent RNA helicase DDX3X), A slightly different experiment led to the identification of more HNRNPA2B1 (Heterogeneous nuclear ribonucleoproteins A2/B1), than 100 human proteins that directly and specifically bind to HNRNPA0 (Heterogeneous nuclear ribonucleoprotein A), G3BP1 SARS-CoV-2 RNAs in infected cells, performing RNA antisense (Ras GTPase-activating protein-binding protein 1), G3BP2 (Ras purification and mass spectrometry. Schmidt et al. linked SARS- GTPase-activating protein-binding protein 2), EIF4B (Eukaryotic CoV-2 interactome with changes in proteome abundance translation initiation factor 4B), RPS2 (40S ribosomal protein S2), induced by viral infection, identifying cellular pathways RPS3 (40S ribosomal protein S3), EIF3G (Eukaryotic translation relevant to SARS-CoV-2 infections. The authors demonstrated initiation factor 3 subunit G) and YBX1 (Y-box-binding protein 1), by genetic perturbation that both Cellular Nucleic-acid Binding Supplementary Tables S1, S2). Protein (CNBP) and LARP1, which are two of the most enriched These proteins form a dense protein-protein network viral RNA binders, have the ability to restrict SARS-CoV-2 (Figure 1B) containing several stress granule components replication in infected cells and provide a general map of their (G3BP1, G3BP2, EIF4B, DDX3X, YBX1, PABPC1), ribosomal direct RNA contact sites. The authors demonstrated a reduced units (RPS2 and RPS3) and pre-mRNA processing units viral replication rate in two human cell lines after a (HNRNPA1/B2, HNRNPA0, YBX1) (Warde-Farley et al., 2010). pharmacological inhibition of three other binding partners The biological relevance of these interactions is confirmed by the fact (PPIA, ATP1A1, ARP2/3 complex). that SARS-CoV-2 N protein impairs stress granule by sequestering As experimental studies require time and resources and are G3BP1 (Lu et al., 2021; Zheng et al., 2021). RPS2 and RPS3 are affected by intrinsic limitations (for instance mass-spec cannot important because the NSP1 protein of SARS-CoV-2 is responsible identify every protein with the same efficiency), computational for the impairment of mRNA translation by blocking the entry methods can be exploited to prioritize candidate targets. We access to the ribosome. The docking within the ribosomal entry previously used the CROSS method (Delli Ponti et al., 2017) to channel occurs through binding with RPS2 and RPS3 together with predict secondary structure content of and the catRAPID 18S RNA (Mendez et al., 2021). Frontiers in Molecular Biosciences | www.frontiersin.org 2 May 2022 | Volume 9 | Article 893067 Vandelli et al. Comparative Analysis of SARS-CoV-2 Interactions FIGURE 1 | Datasets of protein interactions with SARS-CoV-2 genome. (A) experimental datasets (Flynn et al., 2021; Kamel et al., 2021; Lee et al., 2021; Schmidt et al., 2021). The name of each dataset is shown above the diagrams. (B) diagram showing the protein-protein interactions among the 21 proteins identified in the four experiments, as annotated by GeneMANIA (Warde-Farley et al., 2010). (C) catRAPID interaction scores (Agostini et al., 2013b; Armaos et al., 2021) correlate with the number of experiments reporting a protein to interact with SARS-CoV-2, indicating that strong binding proteins are more likely to be identified; *p-value < 0.05; **p-value < 0.01 (Wilcoxon rank sum test); I II, II, III and IV indicate proteins detected in 1,2,3 or 4 experiments, respectively. Some of these proteins have been shown to be also relevant for Relationship Between Experimental other viruses’ infection. SND1 is involved in Epstein-Barr infection Interactomes and Computational (Tong et al., 1995); PABPC1 positively regulates Dengue virus infection (Suzuki et al., 2016); PPIA acts as a mediator for SARS- Predictions We used the catRAPID method to understand the relationship CoV nucleoprotein during the cell invasion process and stimulates between experimental evidence of binding and predicted RNA-binding ability of HCV NS5A (Chen et al., 2005; Foster et al., interaction propensity that estimates interaction affinity 2011); EIF3G is involved in FCV infection process (Pöyry et al., (Agostini et al., 2013a; Cid-Samper et al., 2018). For this 2007) and DDX3X has been shown to facilitate the viral replication analysis we followed a procedure previously introduced to of other several viruses, such as HIV-1, Dengue, Zyka, Venezuelan study the interactome of the long non-coding RNA Xist equine encephalitis and hepatitis C virus (Yedavalli et al., 2004; (Cirillo et al., 2017). We computed all SARS-CoV-2 Amaya et al., 2016; Doñate-Macián et al., 2018). DDX3X has been interactions with proteins reported in the four experimental identified as a suitable target to fight against SARS-CoV-2 infection datasets and counted how many times they were identified by Ciccosanti et al. (2021). More precisely, DDX3X has the (Supplementary Material S1). We observed a distinct capability of unfolding viral RNA secondary structures correlation between occurrence and strength of interactions, (Kukhanova et al., 2020) as reported for HIV-1 (Brai et al., indicating that high-affinity interactions are more likely to be 2020) in which it enhances both translation and nucleus-to- detected (Figure 1C). We note that in the case of Xist, strong cytoplasm transport (Stunnenberg et al., 2018), and West Nile interaction proteins were predicted to initiate the formation of a (Brai et al., 2019). DDX3X belongs to the DEAD-box family of phase-separated assembly (Cerase et al., 2019, 2022), as recently ATP-dependent RNA helicases and assumes a crucial role in an confirmed experimentally (Markaki et al., 2021; Jachowicz et al., important variety of processes concerning RNA metabolism, 2022). including transcription, splicing, and initial phase of translation (Ariumi, 2014). Importantly, DDX3X interacts with the N protein of SARS-CoV-2 and is required to infect both Vero E6 and Calu-3 cells (Ciccosanti et al., 2021). Additionally, SARS-CoV-2 protein N Evaluation of the Predictions of interacts with DDX3X to inhibit its activity in the antiviral response SARS-CoV-2 Protein Interactions (Winnard et al., 2021). For these reasons, treating cells with The vRIC dataset by Kamel et al. contains both enriched and DDX3X inhibitors represents a promising approach to block depleted interactions (Kamel et al., 2021) and thus can be used to SARS-CoV-2 replication and viral production (Maga et al., assess the ability of catRAPID to distinguish between binding and 2011; Brai et al., 2020). non-binding proteins. To analyze the vRIC interactome, we Frontiers in Molecular Biosciences | www.frontiersin.org 3 May 2022 | Volume 9 | Article 893067 Vandelli et al. Comparative Analysis of SARS-CoV-2 Interactions FIGURE 2 | catRAPID and catGRANULE predictions of protein interactions. (A) catRAPID performance evaluation. On the X axis we report different portions of the experimental dataset ranked by fold change and on the Y axis there is the corresponding predictive power (Area Under the ROC Curve, AUC). On the right, we report a summary table showing the Uniprot IDs of top 2.5%, 5% and 7.5% experimental cases. (B) Distribution of specific binders for Kamel et al. dataset (Kamel et al., 2021). The most contacted SARS-CoV-2 genomic regions correspond to the 5’ (first fragment) e 3’ (30th fragment). (C) catGRANULE phase separation propensity scores correlate with the number of experiments reporting a protein to interact with SARS-CoV-2 (Bolognesi et al., 2016; Cid-Samper et al., 2018); *p-value < 0.05; ****p-value < 0.0001 (Wilcoxon rank sum test); I II, II, III and IV indicate proteins detected in 1,2,3 or 4 experiments, respectively. computed catRAPID predictions of interactions with an order to evaluate the binding at the different levels of stringency. experimental FDR <0.10 for SARS-CoV-2 RNA following a Higher Z-scores correspond to higher interaction strength procedure detailed in a previous work (Vandelli et al., 2020) (Supplementary Material S5). (Supplementary Material S1). Regions encompassing nucleotides 1–1000, 1001–2000, As shown in Figure 2A, catRAPID performs extremely well 22001–23000, 26001–27000, 28001–29000, 29001–29903 when the proteins are ranked according to their experimental (Fragments 1, 2, 23, 27, 29 and 30 respectively) are the most scores (fold change; Supplementary Table S3): the predictive contacted SARS-CoV-2 regions, with a high number of power is proportional to the significance of protein interactions: interactors in fragments 1, 2 and 30. (Figure 2B; Supplementary the Area Under the ROC Curve (AUC) increases from 0.60 to Figures S1–S3). In particular, fragment 1, corresponding to the 5′ 0.99 while the experimental scores move from 30% (i.e., the 30% end of SARS-CoV-2 genome, is the region showing the highest strongest positives vs. the 30% strongest negatives) to 2.5% number of specific interactors in all four datasets, as previously (i.e., the 2.5% strongest positives vs. the 2.5% strongest discovered (Vandelli et al., 2020). DDX3X is the only common negatives). Thus, in agreement with the results presented in interactor reported in the experimental and computational studies. Figure 1C, computational approaches such as catRAPID can At a Z ≥ 1.75 we DDX3X is found to bind specifically to fragment 1 be exploited to address the problem of which proteins bind more of SARS-CoV-2. tightly to SARS-CoV-2 genome. Experimental Interactors Have a High Specific Binders to SARS-CoV-2 Genomic Propensity to Phase-Separate Fragments Stress granules facilitate the establishment of an antiviral state by catRAPID was employed for the localization of protein binding sites limiting viral protein accumulation and regulating signaling cascades on SARS-CoV-2 genomic RNA. To identify which regions of SARS- that affect replication (McCormick and Khaperskyy, 2017). The CoV-2 bind to specific proteins, we computed interactions for the sequestration of G3BP1, G3BP2, EIF4B, DDX3X, YBX1, PABPC1, four experimental protein datasets (30 fragments; Supplementary among other proteins, is part of a mechanism through which SARS- Material S4), a procedure already proven to be efficient in a previous CoV-2 eludes the host immune response by weakening the work (Vandelli et al., 2020). formation of stress granules (Lu et al., 2021; Zheng et al., 2021). For each dataset the proteins bound to one fragment at a Biochemically, stress granule proteins form labile protein-protein certain interaction threshold were retained as interactors. We and protein-RNA interactions (Balcerak et al., 2019; Vandelli et al., applied three Z-score thresholds (Z ≥ 1.5, Z ≥ 1.75 and Z ≥ 2) in 2022), which induces the condensation in liquid-liquid phase Frontiers in Molecular Biosciences | www.frontiersin.org 4 May 2022 | Volume 9 | Article 893067 Vandelli et al. Comparative Analysis of SARS-CoV-2 Interactions separated assemblies (Gotor et al., 2020). We reasoned that the their reproducibility. Moreover, the fact that proteins with the relatively small overlap among experimental datasets (Figure 1A) highest interaction and phase separation propensities were could be caused by the establishment of weak molecular interactions identified in all experimental studies suggests that they could with SARS-CoV-2 RNA. In agreement with this observation, act as the primary attractors to ignite the formation of an previous studies have suggested that phase separation could be a assembly that is capable of using host elements for replication. mechanism through which SARS-CoV-2 attracts host proteins Further work is needed to study this fundamental aspect of SARS- (Iserman et al., 2020; Vandelli et al., 2020). CoV-2 biology and how it could be exploited to prevent viral Using the catGRANULE algorithm to predict phase infection. For example, molecular chaperones (Tartaglia et al., separation propensities (Bolognesi et al., 2016; Cid-Samper 2010; Alagar Boopathy et al., 2022) could be important players et al., 2018) we analyzed the interactomes of the four (Guihur et al., 2020) to be investigated in more detail. experimental datasets. We discovered that the phase separation propensity correlates with how many times proteins are identified experimentally (Figure 2C). Considering that DATA AVAILABILITY STATEMENT strong binding propensities are associated with proteins reported in the four experiments (Figure 1C) and the The original contributions presented in the study are included in reliability of our approach (Figure 2A), we speculate that a the article/Supplementary Material, further inquiries can be possible mechanism of action for SARS-CoV-2 is to target directed to the corresponding author. proteins that attract other partners through phase separation. AUTHOR CONTRIBUTIONS DISCUSSION AV and GT conceived the study. AV and GV carried out the This work is a comparative analysis on protein-RNA interactomes analysis. AV, GV, and GT wrote the paper. reported in experimental and computational studies. We found several proteins shared by the four experiments, including PABPC1, SND1, PPIA, EIF3G and DDX3X, which previous studies have shown to regulate replication of viruses. FUNDING DDX3X is found in all the experimental studies and it has been The research leading to these results has been supported by proven fundamental in SARS-CoV-2 biological processes and in European Research Council (RIBOMYLOME_309545 and the replication process of other viruses (Maga et al., 2011; Ariumi, ASTRA_855923), the H2020 projects (IASIS_727658 and 2014; Stunnenberg et al., 2018; Brai et al., 2019, 2020; Kukhanova INFORE_825080) and the Spanish Ministry of Science and et al., 2020; Ciccosanti et al., 2021; Winnard et al., 2021). Innovation (RYC 2019-026752-I and PID 2020-117454RA-I00). catRAPID predictions of human protein interactions with SARS-CoV-2 showed a prevalence of specific binders to the 5′ end of the virus, with DDX3X being one of them. Since catRAPID reproduces experimental data to a remarkable extent, as assessed ACKNOWLEDGMENTS by directly comparing performances at different cut-offs, we believe that this information on the localization of protein The authors would like to thank Prof. Alberto Danielli and Prof. interactions is to be taken into account for future analyses. Marc Torrent Burgas for illuminating discussions. Predictive studies always have a margin of error, so further work will be necessary for a complete understanding of the specific binding sites and the role(s) of proteins in the context of infection. SUPPLEMENTARY MATERIAL In a recent study (Cirillo et al., 2017), we reported that the long non-coding RNA Xist physically interacts with few specific The Supplementary Material for this article can be found online at: proteins that attract several other proteins (Cerase et al., 2019) https://www.frontiersin.org/articles/10.3389/fmolb.2022.893067/ forming a phase-separated assembly that silences the X full#supplementary-material chromosome (Cerase et al., 2022; Jachowicz et al., 2022). The Supplementary Table S1 | Twenty-one proteins shared by the four experimental relatively poor overlap of interactors among SARS-CoV-2 studies studies. Uniprot IDs and gene names of the proteins are provided. (only 21 proteins in common out of hundreds identified in total) Supplementary Table S2 | Proteins found in each different experimental study. suggests a mechanism similar to the one identified for Xist. The Supplementary Table S3 | Dataset used for the evaluation of catRAPID fact that SARS-CoV-2 binding proteins are either stress granules performances. For each protein, its Uniprot ID, experimental fold-change and components or have high phase separation propensity supports normalized catRAPID score are reported. The top experimental cases are our hypothesis. Indeed, phase separation is caused by weak highlighted in different colors according to the relative portion of the dataset. protein-protein or protein-RNA interactions (Balcerak et al., Supplementary Table S4 | Sequence and genomic location of each of the 30 2019; Vandelli et al., 2022), which renders the identification of fragments of SARS-CoV-2. binding partners particularly difficult at the experimental level Supplementary Table S5 | List of interactors for each experimental dataset, (Tartaglia, 2016; Cerase and Tartaglia, 2020) and could hamper predicted to bind univocally each of the 30 SARS-CoV-2 fragments. 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(2016). The Grand Challenge of Characterizing Ribonucleoprotein Conflict of Interest: The authors declare that the research was conducted in the Networks. Front. Mol. Biosci. 3, 24. doi:10.3389/fmolb.2016.00024 absence of any commercial or financial relationships that could be construed as a Tong, X., Drapkin, R., Yalamanchili, R., Mosialos, G., and Kieff, E. (1995). The potential conflict of interest. Epstein-Barr Virus Nuclear Protein 2 Acidic Domain Forms a Complex with a Novel Cellular Coactivator that Can Interact with TFIIE. Mol. Cell. Biol. 15, Publisher’s Note: All claims expressed in this article are solely those of the authors 4735–4744. doi:10.1128/MCB.15.9.4735 and do not necessarily represent those of their affiliated organizations, or those of Vandelli, A., Cid Samper, F., Torrent Burgas, M., Sanchez de Groot, N., and the publisher, the editors and the reviewers. Any product that may be evaluated in Tartaglia, G. G. (2022). The Interplay between Disordered Regions in RNAs and this article, or claim that may be made by its manufacturer, is not guaranteed or Proteins Modulates Interactions within Stress Granules and Processing Bodies. endorsed by the publisher. J. Mol. Biol. 434, 167159. doi:10.1016/j.jmb.2021.167159 Vandelli, A., Monti, M., Milanetti, E., Armaos, A., Rupert, J., Zacco, E., et al. (2020). Copyright © 2022 Vandelli, Vocino and Tartaglia. This is an open-access article Structural Analysis of SARS-CoV-2 Genome and Predictions of the Human distributed under the terms of the Creative Commons Attribution License (CC BY). Interactome. Nucleic Acids Res. 48, 11270–11283. doi:10.1093/nar/gkaa864 The use, distribution or reproduction in other forums is permitted, provided the Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., original author(s) and the copyright owner(s) are credited and that the original et al. (2010). The GeneMANIA Prediction Server: Biological Network publication in this journal is cited, in accordance with accepted academic practice. Integration for Gene Prioritization and Predicting Gene Function. Nucleic No use, distribution or reproduction is permitted which does not comply with Acids Res. 38, W214–W220. doi:10.1093/nar/gkq537 these terms. Frontiers in Molecular Biosciences | www.frontiersin.org 7 May 2022 | Volume 9 | Article 893067