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Abnormal global alternative RNA splicing in COVID-19 patients

['Changli Wang', 'Department Of Pathology', 'School Of Basic Medicine', 'Tongji Medical College', 'Huazhong University Of Science', 'Technology', 'Wuhan', 'Lijun Chen', 'Yaobin Chen', 'Institute Of Artificial Intelligence']

Date: 2022-06

Viral infections can alter host transcriptomes by manipulating host splicing machinery. Despite intensive transcriptomic studies on SARS-CoV-2, a systematic analysis of alternative splicing (AS) in severe COVID-19 patients remains largely elusive. Here we integrated proteomic and transcriptomic sequencing data to study AS changes in COVID-19 patients. We discovered that RNA splicing is among the major down-regulated proteomic signatures in COVID-19 patients. The transcriptome analysis showed that SARS-CoV-2 infection induces widespread dysregulation of transcript usage and expression, affecting blood coagulation, neutrophil activation, and cytokine production. Notably, CD74 and LRRFIP1 had increased skipping of an exon in COVID-19 patients that disrupts a functional domain, which correlated with reduced antiviral immunity. Furthermore, the dysregulation of transcripts was strongly correlated with clinical severity of COVID-19, and splice-variants may contribute to unexpected therapeutic activity. In summary, our data highlight that a better understanding of the AS landscape may aid in COVID-19 diagnosis and therapy.

Despite intensive studies on the transcriptional signatures of COVID-19 patients, how SARS-CoV-2 affects AS landscape and the contribution of AS to the pathogenesis of COVID-19 remain largely elusive. By profiling the lung transcriptome and lung proteome of nine patients who died of COVID-19 during the first wave of the pandemic in Wuhan, China, we obtained molecular insights into the AS of cellular transcripts upon SARS-CoV-2 infection. Interestingly, SARS-CoV-2 proteins directly engage host spliceosome to dysregulate essential steps of mature mRNA production and result in widespread dysregulation of cellular function. Taken together, our findings shed light on COVID-19 molecular mechanism and offer potential therapeutic targets for severe COVID-19 disease.

Funding: This work was supported by the Ministry of Science and Technology of P. R. China Plan (grant number 2020YFC0844700). T.X. is supported by the National Natural Science Foundation of China (61571202). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2022 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

In this study, we integrated multiple-omics datasets to analyze the dysregulation of host splicing machinery and the alteration of transcript isoforms in COVID-19 patients. We further explored the impact of the altered AS landscape on clinical outcomes, and identified potential disruptions of drug-binding sites of target proteins in COVID-19 patients. Our study provides additional insights into the complexity of the splicing landscape upon SARS-CoV-2 infection, which could aid in COVID-19 diagnosis and therapy.

To date, high-throughput sequencing data, especially transcriptomic and proteomic data, has been used to identify the genome-wide AS events. For transcriptomic data, a number of computational approaches have been developed to identify and quantify differentially spliced genes, such as LeafCutter [ 17 ], rMATS [ 18 ], and DEXSeq [ 19 ]. For proteomic data, approaches searched proteomic sequencing data against databases containing splice variant sequences and then confirmed the translation of a spliced sequence by detecting a peptide unique to that form [ 20 ]. In addition, the integration of transcriptomic and proteomic data has become a new way to identify the AS [ 21 ]. Recently, several studies have profiled the transcriptomes of cells, tissues and fluids from COVID-19 patients, which revealed the dysregulated type I and III interferon pathways, hyper-inflammatory responses and activation of humoral immunity [ 22 – 25 ]. Notably, NSP16 protein of SARS-CoV-2 was discovered to bind to the mRNA recognition domains of the U1 and U2 splicing RNAs and act to disrupt host global mRNA splicing and suppress host defenses [ 26 ]. Despite SARS-CoV-2 protein is reported to interact with cellular spliceosomal components, the global alteration of host gene splicing and the contribution of AS to the pathogenesis of COVID-19 remain largely elusive. Characterization of the splicing landscape upon SARS-CoV-2 infection in host cells may provide unique and novel insights on how SARS-CoV-2 regulates and hijacks the immune system for their evasion.

Alternative splicing (AS) is a fundamental mechanism for the regulation of proteome diversity through the splicing of a single RNA to generate alternative mRNAs encoding structurally and functionally distinct protein isoforms [ 5 ]. Multiple viruses hijack this pathway to favor their replication and evade host’s antiviral responses. Ddx58 protein of SARS-CoV-2 virus [ 6 ], NS5 protein of dengue virus [ 7 ], NS1 protein of influenza virus [ 8 ], and Vpr protein of HIV-1 [ 9 ] interact with the cellular spliceosome complex and inhibit the splicing reaction. On the other hand, viral infection can induce numerous AS of host RNAs that translate into altered proteins, which are critical for cell cycle, DNA synthesis, stress response nuclear transport, and immune responses [ 10 – 13 ]. For example, a virus-induced alternatively spliced isoform of TBK1 disrupts the interaction between RIG-I and MAVS and inhibits anti-viral IFN-beta signaling [ 14 ]. CD45, a critical molecule for T cell activation and function, has also been shown to express the smallest isoform CD45RO after HIV infection [ 15 , 16 ].

The current COVID-19 pandemic caused by SARS-CoV-2 poses significant challenges for not only global public health but also life normalcy [ 1 ]. While significant progresses have been made for the management of the disease, treatments are mainly supportive and symptomatic care [ 2 , 3 ]. Understanding the interaction between host and SARS-CoV-2 will provide new insights on COVID-19 pathogenesis and the development of effective antiviral therapies [ 4 ].

Results

SARS-CoV-2 infection induced widespread transcriptional dysfunction To investigate whether there was a systematic dysregulation of host gene expression at the transcriptome level in fatal COVID-19 patients, we performed bulk RNA sequencing to evaluate the alteration of transcript usage in 9 postmortem lung samples versus 10 controls. We identified 1,383 genes whose major transcript (the most highly expressed transcript) displayed differential transcript usage (DTU) changes between the COVID-19 patients and the controls. GO enrichment analysis identified a total of 540 enriched terms among the 1,383 genes. These terms were grouped into 57 clusters that were further categorized into 11 major processes: blood coagulation and cytokine production, immunity, regulation of metabolic process, metabolic and biosynthetic process, response to stimulis, developmental process, homeostasis, localization and transport, regulation of cellular process, cell cycle and cellular component organization (Fig 3A). Out of these clusters, two (C33 and C34) were associated with ‘cytokine and chemokine production’ and ‘blood coagulation’, respectively (S2 Table). PPT PowerPoint slide

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TIFF original image Download: Fig 3. Transcriptional profiling reveals widespread dysfunction of cellular gene expression in the lungs of fatal COVID-19 patients. (A) Functional analysis of 1383 genes with significant DTU. The clustering heatmap plot of functional sets of GO terms was obtained using ViSEAGO showing the major biological processes, cluster number and number of GO terms in each cluster. Based on BMA semantic similarity distance and Ward’s clustering criterion. (B-D) A volcano plot (posterior probability vs. proportion change) comparing gene transcript usage in cases versus controls is shown at mRNA level. DTUs that were related to blood coagulation (B), RNA splicing (C) and immunity (D) are named. The horizontal dashed line represents posterior probability of 0.5, and the vertical dashed lines represent proportion change of -0.1 and 0.1. * represents isoform switch. (E) Isoform switch of SERPINE2, IL2, IL34, NCBP1, PRPF6, NCBP2, LUC7L2 and HRAS. C represents canonical isoform. * posterior probability > 0.5; ** posterior probability > 0.7; *** posterior probability > 0.9. https://doi.org/10.1371/journal.pgen.1010137.g003 DTU genes in these blood coagulation pathways included SERPINB2, SERPINE2, SERPING1, DGKE, DGKQ, DGKB and DGKA (Figs 3B and S2), which were involved in the generation of serine protease inhibitor (SERPIN) and diacylglycerol kinase (DGK). Of note, we observed numerous DTU genes involved in RNA splicing, with 10 genes (e.g. HNRNPC, SNRPD3, QKI, and LUC7L2) increased major transcript usage and 18 genes (e.g. SNRNP48, NCBP1, CELF2, RALY, and PABPC1) decreased major transcript usage in the COVID-19 patients (Figs 3C and S3). Although patients with severe COVID-19 have been found to have a cytokine storm in peripheral blood and bronchoalveolar lavage fluid [32,33], in lung parenchyma we only identified a small number of cytokine genes with differently major transcript usage, including IL1B, IL2, IL6ST, IL34, CCL2, CCL7, CMTM8, CSPG5, PDGFA, and PDGFRL (Figs 3D and S4). Furthermore, we observed that the major transcript of multiple genes switched to a different transcript (isoform switching) between COVID-19 patients and controls, such as IL2, IL34, SERPINE2, NCBP1, HRAS, PRPF6, NCBP2, and LUC7L2 (Fig 3E). The canonical transcript usage of IL2 was significantly decreased and the ENST00000477645 (noncoding protein) transcript usage was significantly increased in COVID-19 cases. Notably, IL-2 plays a key role in the proliferation of T cells and in the generation of effector and memory T cells [34], suggesting that the transcript switch of IL2 may affect the activation of T cells in severe COVID-19 patients. Next, we expanded our analyses to the transcript isoform level and observed 3,937 differential transcript expression (DTE), with 1,890 up-regulated and 2,047 down-regulated transcripts in the fatal cases when compared to healthy controls. GO enrichment analysis on these up-regulated DTE genes identified four neutrophil-related GO terms as top enriched terms (S5A Fig), which is consistent with our previous observation of increased neutrophils in the lungs of fatal COVID-19 patients [28]. Notably, numerous genes with up-regulated transcript were related to oxidative stress response, which is consistent with clinical symptom of severe breathing difficulties in the fatal COVID-19 patients (S5A Fig). Additionally, ‘RNA catabolic process’, ‘mRNA catabolic process’, ‘regulation of DNA metabolic process’ and ‘RNA localization’ were significantly up-regulated in the COVID-19 patients, suggesting that SARS-CoV-2 reshapes central cellular pathways such as translation, carbon metabolism (S5A Fig). Surprisingly, the top GO terms that were down-regulated in these lung tissue samples represent neurobiological processes: axonogenesis, regulation of neuron projection development and axon guidance, suggesting that dysregulation of bidirectional interactions between lung and brain in fatal COVID-19 patients (S5B Fig).

AS events identified in lung tissues of COVID-19 patients Given the profound degree of splicing related protein changes observed in our proteomic data, we sought to determine how many cellular genes with AS events within these severe COVID-19 patients. Using the LeafCutter [17] analysis, we identified a total of 402 intron clusters (corresponding to 366 genes) that displayed altered splicing in the COVID-19 patients (S3 Table). The most commonly observed local splicing change was exon skipping (ES, 41.8%), followed by complex event (36.6%), intron retention (10.3%), alternative 3’ splice site (5.9%) and alternative 5’ splice site (5.4%) (S3 Table). Additionally, many AS events show predictable functional consequences on protein isoforms. For example, isoform 001–004 of CD74 are down-regulated in the COVID-19 patients (Fig 4B). We observed that an ES event in E7 (q = 4.1 × 10−2, dPSI = 17.0%) disrupts a thyroglobulin domain of CD74 (Fig 4A), changes that are predicted to encode a truncated protein deficient in its ability to induce antiviral activity. As another example, we observed significant AS, DTU, and DTE changes in LRRFIP1 transcripts (Fig 4C and 4E). The cytosolic nucleic acids sensor LRRFIP1 mediates the production of type I interferon, which plays an important role in exacerbating TNF- and IL-1-driven inflammation in the progression to severe COVID-19 [35]. An ES event in E7 (q = 5.0 × 10−2, dPSI = 19.0%) disrupts a LRRFIP domain in LRRFIP1, changes that are predicted to have major effects on its function. In addition, we deployed validation experiments for local splicing changes of 6 genes (CD74, LRRFIP1, SH3GLB1, MACF1, RPS24 and PDLIM5) between 9 COVID-19 cases and 10 controls by semiquantitative reverse transcription (RT)-PCR. The result showed percent spliced-in (PSI) changes consistent with those reported by LeafCutter (Figs 4F and S6). To validate DTE results, we performed similar semiquantitative RT-PCR on 4 selected transcripts (CD74-001, CD74-002, LRRFIP1-011, and LRRFIP1-005) and found concordance in transcript expression changing pattern (except for CD74-002) between RNA-seq data analysis and validation experiments (S7A and S7B Fig). Overall, this examination of local AS events in fatal COVID-19 patients, coupled with the analysis of isoform-level regulation, emphasizes the need to understand the critical impact of transcript isoform regulation on gene function. PPT PowerPoint slide

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TIFF original image Download: Fig 4. Aberrant local splicing and transcript isoform usage in COVID-19. (A) A significant differential splicing intron cluster in CD74 (clu_15622; chr5: 149,782,188–149,784,243) showing increased exon 7 (E7) skipping in COVID-19. Increased intron usage in COVID-19 cases compared to control is highlighted in red. Protein domains are annotated as thyroglobulin_1, thyroglobulin type 1 repeats; MHCassoc_trimer, Class II MHC-associated invariant chain trimerisation domain; MHC2-interact, MHC2 interacting. Visualization of splicing events in cluster clu_15622 with changes in PSI (dPSI) for COVID-19 group comparisons. FDR-corrected P values (q) are indicated for each comparison. Covariate-adjusted average PSI levels in cases (red) versus controls (black) are indicated at each intron. (B) Bar plots for changes in gene expression for CD74-002, CD74-001, CD74-003 and CD74-004. CD74-003 is the canonical sequence. * posterior probability > 0.5; ** posterior probability > 0.7; *** posterior probability > 0.9. (C) Whole-gene view of LRRFIP1 highlighting (dashed lines) the intron cluster with significant differential splicing in COVID-19 (clu_2593_NA; chr2: 238,657,967–238,661,952). LRRFIP, Leucine-rich repeat flightless-interacting protein domain. (D and E) Bar plots for changes in transcript usage and gene expression for LRRFIP1 transcripts. * posterior probability > 0.5; ** posterior probability > 0.7; *** posterior probability > 0.9. (F) Scatter plots comparing the average percent spliced-in (PSI) of exon skipping events called by LeafCutter from RNA-seq data to semi-quantitative PCR. A total of 6 genes were tested in 9 COVID-19 cases and 10 controls. Gene names are indicated at each point. Regression lines with 95% confidence intervals are shown in blue and grey, respectively and the corresponding R2 values are shown at the top-left of the plot. https://doi.org/10.1371/journal.pgen.1010137.g004 Next, we created an independent gene co-splicing network in the cases and controls respectively through the MONET K1 method, which detected COVID-19 associated modules by kernel clustering with diffusion state distance as metric [36]. Although calculated separately, the case and control networks have similar genes and modules, and generally reflected equivalent biological processes. However, the co-splicing connections showed a great divergent network between the case and control groups. For example, the neutrophil activation process enriched in both case and control networks and exhibited a completely different structure and connection (S8A Fig), suggesting that there are significant phenotypical alterations of neutrophil from COVID-19 patients compared to healthy controls. Notably, DDX3X, the hub of neutrophil activation network, encodes a DEAD-box RNA helicase with putative roles in RNA metabolism, cell cycle progression, apoptosis, and viral immunity [37]. In addition, the 18th module shows greater enrichment for macrophage activation in cases and the 48th module is enriched for genes harboring B cell homeostasis in controls (S8B and S8C Fig), which could be used to distinguish fatal COVID-19 patient and healthy control.

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