Abstract

Graphical Abstract

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Introduction

COVID-19 (coronavirus disease 2019) has become a global pandemic, fueled by the rapid spread of the coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), a positive-strand RNA virus (Wu et al., 2020a; Sanche et al., 2020). The scientific community is actively trying to understand SARS-CoV-2’s biological mechanisms and effects. Here, we computationally analyze the subcellular localization patterns of SARS-CoV-2 RNA transcripts. Our results suggest potential avenues for experimental validation and follow-up, while providing a template for in silico analyses of viral RNA.

RNA subcellular localization is critical to a myriad of cellular processes (Ryder and Lerit, 2018; Chin and Lécuyer, 2017; Buxbaum et al., 2015). Researchers have also discovered that RNA localization plays a significant role in the life cycle of viruses, with functions ranging from regulating sites of virion assembly (Becker and Sherer, 2017) to disrupting host mitochondrial function (Somasundaran et al., 1994). However, the subcellular localization of SARS-CoV-2 (and other conronavirus) RNA is largely unexplored. Gaining a better understanding of the behavior and localization of SARS-CoV-2’s RNA genome and transcripts can lead to a better understanding of its function and pathogenicity, potentially revealing targetable mechanisms.

To computationally study this aspect of SARS-CoV-2 biology, we built upon our recent work developing RNA-GPS, a state-of-the-art computational model for predicting high-resolution RNA localization in human cells (Wu et al., 2020b). RNA-GPS was trained on transcriptome-wide localization patterns of human RNAs across eight subcellular landmarks (Fazal et al., 2019). RNA-GPS’s strong performance, coupled with viruses’ dependence on hijacking and repurposing existing cell machinery for reproduction, suggests that RNA-GPS could provide insights into SARS-CoV-2’s localization behavior and can focus future experimental efforts.

We use RNA-GPS to interrogate the dominant subcellular residency patterns of SARS-CoV-2’s genome, which spans approximately 30 kilobases of single-stranded positive-sense RNA (Kim et al., 2020) (Figure 1 A). RNA-GPS predicts that SARS-CoV-2 and the transcripts it forms have enriched residency at the nucleolus and the mitochondria. We note that our analysis may suggest potential localization mechanisms for SARS-CoV-2, rather than direct physical localization, particularly with regard to our mitochondrial prediction. Comparison of SARS-CoV-2’s predicted residency with that of other human coronaviruses, including strains causing the common cold, Middle East respiratory syndrome (MERS), and the SARS outbreak of 2003, shows that SARS-CoV-2 exhibits a stronger mitochondrial and nuclear residency signal than a large majority of its coronavirus relatives. We additionally find that this residency signal appears to be driven by the 5′ and 3′ ends of the viral genome. We conclude by connecting our predictions to known RNA and viral biology and proposing possible explanatory mechanisms for previously observed phenomena. Our findings entreat experimental validation and serve as a framework for applying machine learning for principled hypothesis generation in viral biology.

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Figure 1

Depictions of the SARS-CoV-2 Genome, the Eight Compartments that RNA-GPS Predicts Viral Transcript Residency to, and the Predicted Residencies for SARS-CoV-2 sgRNAs and its 5′/CDS/3′ Sequence Segments

(A and B) The SARS-CoV-2 genome produces a series of sub-genomic RNAs (sgRNAs), each encoding one or more genes or proteins (A). These sgRNAs share a common leader 5′ sequence and a common trailing 3′ UTR sequence (arrow blocks). For each sgRNA, RNA-GPS predicts residency to each compartment in (B). Italicized text indicates the APEX2 fusion protein used to measure transcripts corresponding to each localization (see Table S1).

(C and D) (C) Heatmap of rank scores, indicating how strongly each sgRNA (rows) is predicted to exhibit subcellular residency at each compartment (columns), compared with endogenous human transcripts measured to localize to that compartment. Colors indicate rank scores; color scale is shared across all heatmaps. Most sgRNAs share similar residency patterns, exhibiting statistically significant enrichment toward the mitochondrial matrix and nucleolus (see Table S3). We also computed these rank scores against a baseline of other coronavirus residency signals (D). SARS-CoV-2 exhibits a stronger mitochondrial matrix residency signal than most other coronaviruses, along with greater overall nuclear residency, particularly at the nucleolus. For context, coronaviruses are generally predicted to have residency at the nucleolus, mitochondrial matrix, and ER membrane (see Figure S2). These predictions are also consistent across different models (see Figure S3) and negative-strand SARS-CoV-2 sgRNA precursors (see Figure S4).

(E) Shows the predicted residency rank scores for shared 5′ and 3′ segments and an averaged residency rank score for the variable coding segments. Even on their own, the short ~90–250 base pair 5′ and 3′ segments carry mitochondrial and nucleolar residency signals.

Results

We leverage our recent work developing RNA-GPS, a computational model predicting high-resolution RNA subcellular localization in human cells (Wu et al., 2020b). Our model was built using APEX-seq data, which fuses the APEX2 (engineered ascorbate peroxidase, version 2) protein to various protein localization sequences (Figure 1B; Table S1) to guide APEX2 to each subcellular region for subsequent proximity biotinylation of nearby RNAs (Fazal et al., 2019). The resultant transcripts captured and measured at the nucleolus, for example, are transcripts proximal to APEX2 in the nucleolus, as well as those proximal to APEX2 throughout its entire lifecycle, including its transport to the nucleolus. Such “en route” transcripts constitute a small proportion of total transcripts, except in the notable case of the mitochondrial matrix COX4 marker (Richter-Dennerlein et al., 2016), which picks up a sizable proportion of nuclear-encoded transcripts as it is imported to the mitochondria (Figure S1A). Though this is surprising, these nuclear-encoded, mitochondrial-enriched transcripts are reproducibly distinct from noise (Figures S1B and S1C) and actually enrich for cytoskeletal and intracellular transport processes (Figure S1D). For the sake of brevity, we will refer to these measurements using their final destinations, as confirmed by imaging: the cytosol, endoplasmic reticulum (ER), mitochondrial matrix, outer mitochondrial membrane, nucleus, nucleolus, nuclear lamina, and nuclear pore. RNA-GPS predicts localization to each of these eight neighborhoods (Figure 1B).

Although RNA-GPS is trained on human, not viral, RNA transcripts, its ability to generalize across cell types not used in training (Wu et al., 2020b), combined with the fact that viruses commandeer human cellular machinery, suggests that it offers a reasonable hypothesis of viral transcript-localization behavior given currently available data. Nonetheless, there is inherent uncertainty associated with generalizing our model across species, and we use the term dominant subcellular residency to indicate this predictive uncertainty where appropriate.

We consider viral transcript subcellular residency predictions to each compartment averaged across all released and annotated SARS-CoV-2 genomes available as of April 6, 2020 (n = 213) on GenBank (NCBI Resource Coordinators, 2018). SARS-CoV-2 is believed to enter the cell as a positive-strand genomic RNA, subsequently forming 11 positive-strand subgenomic RNA (sgRNA) transcripts encoding different open reading frames and sharing the same 5′ leader sequence and 3′ untranslated region (UTR) (Figure 1A) (Kim et al., 2020). Within each viral genome, we predict the residency of each sgRNA produced from the primary SARS-CoV-2 genome.

To better understand how strong these predicted residency probabilities are in a meaningful biological context, we frame them relative to predictions for other relevant baseline transcript sequences. We consider two such baselines: (1) the distribution of model predictions on transcripts exhibiting significant localization within the human HEK293T cell line (n = 366 transcripts), as measured by APEX-seq (Fazal et al., 2019), and (2) the distribution of model predictions on transcripts derived from human coronaviruses, excluding SARS-CoV-2 (n = 191 genomes, spanning diseases from the common cold to MERS, Table S2). The human baseline quantifies the strength of RNA residency signals in SARS-CoV-2 relative to naturally occurring human transcripts with well-characterized localization behaviors. The coronavirus baseline focuses on differences in the transcript residency behavior of SARS-CoV-2 relative to similar viral specimens—differences that may help researchers focus on the peculiarities of this virus. For both baselines, we calculate the proportion of the baseline distribution that the SARS-CoV-2 subcellular residency prediction exceeds, which we refer to as a rank score. For example, a residency rank score of 0.6 for the nucleolus relative to human transcripts suggests that the particular viral RNA is more likely to have been picked up by the nucleolus APEX-seq marker compared with 60% of human RNAs that are empirically measured to do so.

SARS-CoV-2 RNA Subcellular Residency Patterns

We find that, compared to transcripts with known localizations in human cells, SARS-CoV-2 has a notable residency signal toward the nucleolus, as well as the mitochondrial matrix (Figure 1C). These residency signals are consistent across different sgRNAs encoded by the virus (shown in each row of Figure 1C) and represent statistically significant predicted residency (Table S3). The nucleolus is known to play a prominent role in the viral life cycle, even for viruses that primarily replicate in the cytoplasm as SARS-CoV-2 presumably does (Salvetti and Greco, 2014). While some RNA viruses like human immunodeficiency virus (HIV) have been reported to localize RNA to the mitochondria (Somasundaran et al., 1994), there has not been direct evidence that SARS-CoV-2 does this. As previously discussed, since much of the APEX-seq mitochondrial data used to train RNA-GPS actually consists of nuclear-encoded transcripts likely picked up as the APEX-COX4 fusion protein is transported to the mitochondria, we hypothesize that our predicted mitochondrial residency is alluding to similarity in localization pathways, rather than localization destination.

In addition to framing our localization results in the context of endogenous human transcripts, we also compare predicted residency of SARS-CoV-2 sgRNAs with that of other human coronaviruses (Figure 1D). Here, we observe similar overall trends in our residency predictions. Consistent with the comparison with human transcripts, we find that the SARS-CoV-2 mitochondrial matrix residency signal is stronger than that of many other coronaviruses. Additionally, we see an overall pattern suggesting that SARS-CoV-2 may have a greater affinity for nuclear neighborhoods (nuclear pore, nucleus, nucleolus, and nuclear lamina) compared to other coronaviruses.

We also compared the dominant subcellular residency patterns of the coronavirus family (excluding SARS-CoV-2) with human transcripts using RNA-GPS. We found that the most prominent residency signals for general human coronaviruses pointed toward the nucleolus, mitochondrial matrix, and ER membrane (Figure S2). Overall, our computational analysis suggests that SARS-CoV-2’s predicted sgRNA transcript residency enriching for the mitochondrial matrix and nucleolus may be amplifications of behaviors that were already present in coronaviruses.

While direct experimental data measuring coronavirus sgRNA transcript localization is not currently available, we sought to validate our predictions on other human viruses with known subcellular localizations. After conducting a systematic literature search, we found one such example: the human cytomegalovirus (CMV) β2.7 mRNA transcript, which localizes to the inner mitochondrial membrane (Williamson et al., 2012) and is approximately 2.5 kilobases long. RNA-GPS predicts this transcript to reside at the mitochondrial matrix with a rank score of 0.81; no other compartments have a rank score exceeding 0.5 (Figure 2 A). Thus, the algorithm’s residency prediction is in close agreement with experimental evidence for β2.7 mRNA localization. While large-scale comparisons are not currently feasible due to lack of datasets measuring viral transcript localization, this example provides some reassurance that RNA-GPS’ predicted viral residencies are reasonable.

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Figure 2

Validation of SARS-CoV-2 Residency Predictions

(A) RNA-GPS predictions for the human cytomegalovirus β2.7 transcript, which has been shown to localize to the inner mitochondrial membrane. RNA-GPS correctly predicts its residency to the closest compartment it has been trained on—the mitochondrial matrix. This provides support that RNA-GPS can make reasonable predictions on viral RNA.

(B) To evaluate the effect of the potentially noisy mitochondrial examples in our APEX-seq training set on predicted SARS-CoV-2 residencies, we trained a “denoised” variant of RNA-GPS on a subsetted dataset that excludes these examples. This denoised model predicts the same residency pattern for the three components of the SARS-CoV-2 sgRNAs (compare with Figure 1E). For additional analysis of the mitochondrial dataset and predictions, see Figure S1.

To further validate the robustness of these results, we also trained a different predictive algorithm (a recurrent neural network; see STAR Methods for additional details) on the APEX-seq data and performed a similar set of experiments, comparing SARS-CoV-2 dominant subcellular residency predictions with human and coronavirus baselines (Figures S3A and S3B). This alternative model also predicts strong mitochondrial matrix and nucleolus residency for SARS-CoV-2. Since this algorithm uses a very different modeling strategy from RNA-GPS and nonetheless converges to similar findings, this suggests that the mitochondrial matrix and nucleolus residency predictions are not artifacts of a particular computational modeling strategy, but rather arise from a consistent signal in the data, thus increasing our confidence in our findings.

In addition to evaluating robustness of our results to modeling strategies, we also evaluated robustness with respect to the APEX-seq data used to train the models. As we previously mentioned, many APEX-seq transcripts used to train RNA-GPS’s mitochondrial predictions are actually nuclear encoded. These transcripts exhibited relatively low (albeit significant) enrichment compared with transcripts natively encoded in the mitochondrial genome. To ensure that our results have not been driven by potentially noisy data in this regime, we excluded nuclear-encoded, “noncanonical” mitochondrial matrix transcripts with relatively low APEX-seq enrichment signal (lowest 20% of log-fold-change enrichment scores) and retrained RNA-GPS on this adjusted dataset. This “denoised” model recapitulates the same SARS-CoV-2 residency toward the mitochondrial matrix and nucleolus (Figure 2B), suggesting that our predictions are robust to potential noise in the training data. In summary, our predicted residencies are robust across different modeling strategies and across variation in the data used to train these models.

Discussion

In this work, we apply computational models of human RNA transcript localization to better understand the subcellular localization behavior of the SARS-CoV-2 genome and its constituent sgRNAs. This approach builds upon the idea that the virus uses existing host cell machinery to reproduce and, consequently, that sequence-based localization signals are likely shared between human and coronavirus transcripts. The strengths of this approach include (1) the potential to understand viral RNA localization without the risk of live viral cultures; (2) the ability to examine hundreds of viral isolates and related coronaviruses and thousands of RBP motif ablations; (3) the ability to examine viral genes, UTRs, and negative strands individually, which may otherwise require the ability to precisely synchronize and arrest the viral life cycle. We find that SARS-CoV-2 appears to harbor strong transcript residency signals toward the mitochondrial matrix and nuclear compartments, often comparable to human RNAs and more so than other coronaviruses. This intriguing hypothesis suggests future experimental exploration and validation.

As we mentioned previously, we believe that our predicted mitochondrial residency signal is more indicative of a localization pathway than a destination; in the context of coronavirus biology, this may specifically be related to double-membrane vesicles (DMVs). Coronaviruses are known to produce DMVs to serve functions like concealing the virus from cellular defenses (Hagemeijer et al., 2012; Knoops et al., 2008). While these DMVs are generally believed to be formed via viruses manipulating the ER membrane (Blanchard and Roingeard, 2015), the mechanism for importing and packaging proteins and RNA into these miniature organelles is not as clearly understood. One possible mechanism for importing viral RNA involves the virus exploiting the RNA localization mechanisms that the cell already possesses for endogenous double-membrane organelles, namely, the mitochondria. Indeed, introducing just two amino acid point mutations in the murine coronavirus causes both a significant drop in the number of DMV structures observed, as well as a sharp increase in viral protein localization at the mitochondria (Clementz et al., 2008). This alludes to a high degree of resemblance between DMV and mitochondrial localization mechanisms—leading to our hypothesis that our mitochondrial matrix residency predictions are capturing this similarity between the DMV and mitochondria. Furthermore, DMVs have been shown to contain double-stranded RNA (Hagemeijer et al., 2012); our strand-agnostic residency predictions are concordant with this evidence and might even encourage formation of such complexes. Under this model, SARS-CoV-2’s strong mitochondrial residency signal relative to other coronaviruses might even contribute to its similarly high infectivity by increasing its efficacy in forming these DMV structures.

Another possible interpretation of these predicted residencies is that previously studied viral protein localizations are influenced by transcript-level localizations, a mechanism that is highly prevalent for proteins in normal human cells (Blower, 2013). Protein-protein interaction studies performed on SARS-CoV-2 have found that its NSP5 (within ORF1a), NSP13 (within ORF1b), ORF6, and ORF10 proteins interact with host proteins that predominantly localize to nuclear compartments (Gordon et al., 2020). The same study found that the ORF9b protein, produced by the “N” sgRNA, interacts with TOMM70, a mitochondrial import receptor that plays a critical role in modulating interferon response—a key antiviral cellular defense pathway (Liu et al., 2010). In both cases, localized viral transcripts could help drive viral protein localization, enabling more focused protein-protein interactions.

A limitation of our work lies in its application of models trained on human RNA transcript localization data to viral transcripts. It is possible that SARS-CoV-2 infection could alter the host subcellular structures and RNA transport machinery so drastically that our learned localization patterns from human cells no longer hold. If RNA-GPS’s predictions turn out to be wrong for this reason, this might suggest that coronavirus infection devastates host cell RNA trafficking and localization—a previously unrecognized feature of COVID-19 pathobiology. After all, the vast majority of RBPs in the host cell, which are key drivers of transcript localization, recognize and process RNAs irrespective of whether they are endogenous or foreign, and the inability to “properly” localize viral RNAs should mirror a similar breakdown for host cell transcripts. As we are unable to use existing experimental evidence to thoroughly evaluate and cross-reference the predictions discussed here, future experiments in this vein are clearly necessary. Given the historical scarcity of studies focusing on viral transcript localization, such experiments would likely reveal interesting, crucial insights into viral pathobiology, whether they confirm our specific mitochondrial and nucleolus predictions or not. It is worth pointing out, though, that this is but one of many complex, interconnected viral mechanisms at play.

In summary, we build upon recent computational models of RNA subcellular localization to study, in silico, the localization properties of SARS-CoV-2 transcripts. Our results suggest that predicted transcript residency signals, specifically toward the nucleolus and mitochondrial matrix, may be important, unique characteristics of SARS-CoV-2 that warrant additional study. We connect these observations to known viral biology regarding DMV structures in viral replication, as well as SARS-CoV-2 protein localization patterns. In doing so, we propose potential cellular mechanisms that underpin viral biology—mechanisms that warrant experiments validating their accuracy and perhaps even their potential as therapeutic targets. More broadly, we hope that our study helps define a framework for applying machine learning models to enable focused hypothesis generation, enabling similar studies that leverage data science to rapidly respond to emerging epidemiological challenges.

STARMethods

Acknowledgments

Notes

Published: June 20, 2020

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