Statistical analysis supports UTR (untranslated region) deletion theory in SARS-CoV-2.

It was noticed that the mortality rate of SARS-CoV-2 infection experienced a significant declination in the early stage of the epidemic. We suspect that the sharp deterioration of virus toxicity is related to the deletion of the untranslated region (UTR) of the virus genome. It was found that the genome length of SARS-CoV-2 engaged a significant truncation due to UTR deletion after a mega-sequence analysis. Sequence similarity analysis further indicated that short UTR strains originated from its long UTR ancestors after an irreversible deletion. A good correlation was discovered between genome length and mortality, which demonstrated that the deletion of the virus UTR significantly affected the toxicity of the virus. This correlation was further confirmed in a significance analysis of the genetic influence on the clinical outcomes. The viral genome length of hospitalized patients was significantly more extensive than that of asymptomatic patients. In contrast, the viral genome length of asymptomatic was considerably longer than that of ordinary patients with symptoms. A genome-level mutation scanning was performed to systematically evaluate the influence of mutations at each position on virulence. The results indicated that UTR deletion was the primary driving force in alternating virus virulence in the early evolution. In the end, we proposed a mathematical model to explain why this UTR deletion was not continuous.

Extended ensemble simulations of a SARS-CoV-2 nsp1-5'-UTR complex.

Nonstructural protein 1 (nsp1) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a 180-residue protein that blocks translation of host mRNAs in SARS-CoV-2-infected cells. Although it is known that SARS-CoV-2's own RNA evades nsp1's host translation shutoff, the molecular mechanism underlying the evasion was poorly understood. We performed an extended ensemble molecular dynamics simulation to investigate the mechanism of the viral RNA evasion. Simulation results suggested that the stem loop structure of the SARS-CoV-2 RNA 5'-untranslated region (SL1) binds to both nsp1's N-terminal globular region and intrinsically disordered region. The consistency of the results was assessed by modeling nsp1-40S ribosome structure based on reported nsp1 experiments, including the X-ray crystallographic structure analysis, the cryo-EM electron density map, and cross-linking experiments. The SL1 binding region predicted from the simulation was open to the solvent, yet the ribosome could interact with SL1. Cluster analysis of the binding mode and detailed analysis of the binding poses suggest residues Arg124, Lys47, Arg43, and Asn126 may be involved in the SL1 recognition mechanism, consistent with the existing mutational analysis.

Lipid Nanoparticle-mRNA Formulations for Therapeutic Applications.

After decades of extensive fundamental studies and clinical trials, lipid nanoparticles (LNPs) have demonstrated effective mRNA delivery such as the Moderna and Pfizer-BioNTech vaccines fighting against COVID-19. Moreover, researchers and clinicians have been investigating mRNA therapeutics for a variety of therapeutic indications including protein replacement therapy, genome editing, and cancer immunotherapy. To realize these therapeutics in the clinic, there are many formidable challenges. First, novel delivery systems such as LNPs with high delivery efficiency and low toxicity need to be developed for different cell types. Second, mRNA molecules need to be engineered for improved pharmaceutical properties. Lastly, the LNP-mRNA nanoparticle formulations need to match their therapeutic applications.In this Account, we summarize our recent advances in the design and development of various classes of lipids and lipid derivatives, which can be formulated with multiple types of mRNA molecules to treat diverse diseases. For example, we conceived a series of ionizable lipid-like molecules based on the structures of a benzene core, an amide linker, and hydrophobic tails. We identified N1,N3,N5-tris(3-(didodecylamino)propyl)benzene-1,3,5-tricarboxamide (TT3) as a lead compound for mRNA delivery both in vitro and in vivo. Moreover, we tuned the biodegradability of these lipid-like molecules by introducing branched ester or linear ester chains. Meanwhile, inspired by biomimetic compounds, we synthesized vitamin-derived lipids, chemotherapeutic conjugated lipids, phospholipids, and glycolipids. These scaffolds greatly broaden the chemical space of ionizable lipids for mRNA delivery. In another section, we highlight our efforts on the research direction of mRNA engineering. We previously optimized mRNA chemistry using chemically-modified nucleotides to increase the protein expression, such as pseudouridine (ψ), 5-methoxyuridine (5moU), and N1-methylpseudouridine (me1ψ). Also, we engineered the sequences of mRNA 5' untranslated regions (5'-UTRs) and 3' untranslated regions (3'-UTRs), which dramatically enhanced protein expression. With the progress of LNP development and mRNA engineering, we consolidate these technologies and apply them to treat diseases such as genetic disorders, infectious diseases, and cancers. For instance, TT3 and its analog-derived lipid-like nanoparticles can effectively deliver factor IX or VIII mRNA and recover the clotting activity in hemophilia mouse models. Engineered mRNAs encoding SARS-CoV-2 antigens serve well as vaccine candidates against COVID-19. Vitamin-derived lipid nanoparticles loaded with antimicrobial peptide-cathepsin B mRNA enable adoptive macrophage transfer to treat multidrug resistant bacterial sepsis. Biomimetic lipids such as phospholipids formulated with mRNAs encoding costimulatory receptors lead to enhanced cancer immunotherapy.Overall, lipid-mRNA nanoparticle formulations have considerably benefited public health in the COVID-19 pandemic. To expand their applications in clinical use, research work from many disciplines such as chemistry, engineering, materials, pharmaceutical sciences, and medicine need to be integrated. With these collaborative efforts, we believe that more and more lipid-mRNA nanoparticle formulations will enter the clinic in the near future and benefit human health.

A PCR amplicon-based SARS-CoV-2 replicon for antiviral evaluation.

The development of specific antiviral compounds to SARS-CoV-2 is an urgent task. One of the obstacles for the antiviral development is the requirement of biocontainment because infectious SARS-CoV-2 must be handled in a biosafety level-3 laboratory. Replicon, a non-infectious self-replicative viral RNA, could be a safe and effective tool for antiviral evaluation. Herein, we generated a PCR-based SARS-CoV-2 replicon. Eight fragments covering the entire SARS-CoV-2 genome except S, E, and M genes were amplified with HiBiT-tag sequence by PCR. The amplicons were ligated and in vitro transcribed to RNA. The cells electroporated with the replicon RNA showed more than 3000 times higher luminescence than MOCK control cells at 24 h post-electroporation, indicating robust translation and RNA replication of the replicon. The replication was drastically inhibited by remdesivir, an RNA polymerase inhibitor for SARS-CoV-2. The IC50 of remdesivir in this study was 0.29 µM, generally consistent to the IC50 obtained using infectious SARS-CoV-2 in a previous study (0.77 µM). Taken together, this system could be applied to the safe and effective antiviral evaluation without using infectious SARS-CoV-2. Because this is a PCR-based and transient replicon system, further improvement including the establishment of stable cell line must be achieved.

Detecting and Profiling Endogenous RNA G-Quadruplexes in the Human Transcriptome.

G-quadruplexes are the non-canonical nucleic acid structures that are preferentially formed in G-rich regions. This structure has been shown to be associated with many biological functions. Regardless of the broad efforts on DNA G-quadruplexes, we still have limited knowledge on RNA G-quadruplexes, especially in a transcriptome-wide manner. Herein, by integrating the DMS-seq and the bioinformatics pipeline, we profiled and depicted the RNA G-quadruplexes in the human transcriptome. The genes that contain RNA G-quadruplexes in their specific regions are significantly related to immune pathways and the COVID-19-related gene sets. Bioinformatics analysis reveals the potential regulatory functions of G-quadruplexes on miRNA targeting at the scale of the whole transcriptome. In addition, the G-quadruplexes are depleted in the putative, not the real, PAS-strong poly(A) sites, which may weaken the possibility of such sites being the real cleaved sites. In brief, our study provides insight into the potential function of RNA G-quadruplexes in post-transcription.

COVIDOUTCOME-estimating COVID severity based on mutation signatures in the SARS-CoV-2 genome.

Numerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome. We used an automated machine learning approach where 1594 viral genomes with available clinical follow-up data were used as the training set (797 'severe' and 797 'mild'). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm, was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV) and then adjusted for multiple testing with Bootstrap Bias Corrected CV. We identified 26 protein and Untranslated Region (UTR) mutations significantly linked to severe outcome. The best classification algorithm uses a mutation signature of 22 mutations as well as the patient's age as the input and shows high classification efficiency with an area under the curve (AUC) of 0.94 [confidence interval (CI): [0.912, 0.962]] and a prediction accuracy of 87% (CI: [0.830, 0.903]). Finally, we established an online platform (https://covidoutcome.com/) that is capable to use a viral sequence and the patient's age as the input and provides a percentage estimation of disease severity. We demonstrate a statistical association between mutation signatures of SARS-CoV-2 and severe outcome of COVID-19. The established analysis platform enables a real-time analysis of new viral genomes.