SARS-CoV-2-encoded inhibitors of human LINE-1 retrotransposition.

The ongoing pandemic of severe acute respiratory coronavirus 2 (SARS-CoV-2) is causing a devastating impact on public health worldwide. However, details concerning the profound impact of SARS-CoV-2 on host cells remain elusive. Here, we investigated the effects of SARS-CoV-2-encoded viral proteins on the intracellular activity of long interspersed element 1 (L1) retrotransposons using well-established reporter systems. Several nonstructural or accessory proteins (Nsps) of SARS-CoV-2 (i.e., Nsp1, Nsp3, Nsp5, and Nsp14) significantly suppress human L1 mobility, and these viral L1 inhibitors generate a complex network that modulates L1 transposition. Specifically, Nsp1 and Nsp14 inhibit the intracellular accumulation of L1 open reading frame proteins (ORF1p), whereas Nsp3, Nsp5, and Nsp14 repress the reverse transcriptase activity of L1 ORF2p. Given recent findings concerning the roles of L1 in antiviral immune activation and host genome instability, the anti-L1 activities mediated by SARS-CoV-2-encoded inhibitors suggest that SARS-CoV-2 employs different strategies to optimize the host genetic environment.

Transcriptome and targeted metabolome analysis revealed the effects of combined red and blue light on the growth and secondary metabolism of Scutellaria baicalensis Georgi

Scutellaria baicalensis Georgi is a medicinal plant commonly used in China and other parts of East Asia. A high flavonoid content endows it with multiple beneficial biological activities that have anti-inflammatory, antibacterial, antiviral, and anti-COVID19 properties. Light-emitting diodes (LEDs) have been recognized as effective artificial lights to enhance plant growth and the accumulation of secondary metabolites for commercial plant production. However, little is known regarding the effects of LEDs on S. baicalensis. Here, we explored the effects of monochromatic blue light (B, 460 nm), monochromatic red light (R, 660 nm), white light (CK), and different combinations of R and B (R9B1, R7B3, R5B5, R3B7, and R1B9) on the growth of, and flavonoid accumulation in S. baicalensis. The results revealed that under R:B ratios of 9:1 or 7:3, the whole plant and roots of S. baicalensis seedlings had a higher biomass and flavonoid content. Targeted metabolomics analysis showed that 48 differentially expressed metabolites (DEMs) were verified between different groups, and the number of upregulated DEMs, particularly flavonoids, were higher in the R9B1 and R7B3 groups compared with the CK. Transcriptome data identified 1412 and 1508 differentially expressed genes (DEGs) in the R9B1 and R7B3 groups, respectively, in contrast to the CK. KEGG pathway analysis indicated that DEGs in both the R9B1 and R7B3 groups were primarily enriched in the phenylpropane biosynthesis, plant hormone signal transduction, flavonoid biosynthesis, starch and sucrose metabolism, galactose metabolism, cartenoid biosynthesis, zeatin biosynthesis, and nitrogen metabolism pathways. The qRT-PCR results showed that SbPAL, SbCLL-7, SbCHI, SbFNS, and SbOMT encoding enzymes for the flavonoid biosynthesis pathway were significantly upregulated in S. baicalensis, which was consistent with the transcriptome data. Finally, based on correlation analysis between the main flavonoids in S. baicalensis and the genes encoding transcription factors and enzymes for the flavonoid metabolism pathway, a co-expression network map was developed, which provided a basis for the mining of light responsive genes related to flavonoids biosynthesis in S. baicalensis. This is the first report to articulate how the combination of red light and blue light influences the growth and secondary metabolism of S. baicalensis.

Safety and Immunogenicity of Inactivated and Recombinant Protein SARS-CoV-2 Vaccines in Patients With Thyroid Cancer.

Background: This study aimed at assessing the safety and immunogenicity of SARS-CoV-2 vaccines in patients with thyroid cancer. Methods: This observational study included thyroid cancer patients between April 1, 2021, and November 31, 2021, in the Second Affiliated Hospital of Chongqing Medical University. All participants received at least one dose of the SARS-CoV-2 vaccine. SARS-CoV-2 IgG was tested, and the interval time between the last dose and humoral response test ranged from <1 to 8 months. The complications after SARS-CoV-2 vaccines were recorded. Results: A total of 115 participants at least received one dose of SARS-CoV-2 vaccines with a 67.0% IgG-positive rate. Among them, 98 cases had completed vaccination, and the positivity of SARS-CoV-2 IgG antibodies was 96% (24/25) with three doses of ZF2001. SARS-CoV-2 IgG antibodies' positivity was 63.0% (46/73) of two doses of CoronaVac or BBIBP-CorV vaccine. Additionally, after 4 months of the last-dose vaccination, the IgG-positive rate (31.6%, 6/19) significantly decreased in thyroid cancer patients. The IgG-positive rate (81.0%, 64/79) was satisfactory within 3 months of the last-dose vaccination. Ten (10.2%) patients had side effects after SARS-CoV-2 vaccination. Among them, two (2.0%) patients had a fever, five (5.1%) patients had injection site pain, one (1.0%) patient felt dizzy, and one patient felt dizzy and had injection site pain at the same time. Conclusion: SARS-CoV-2 vaccines (CoronaVac, BBIBP-CorV, and ZF2001) are safe in thyroid cancer patients. The regression time of SARS-CoV-2 IgG is significantly shorter in thyroid cancer patients than in healthy adults. Therefore, a booster vaccination dose may be earlier than the systematic strategy for thyroid cancer patients.

Linking genomic and epidemiologic information to advance the study of COVID-19.

The outbreak of Coronavirus Disease 2019 (COVID-19) at the end of 2019 turned into a global pandemic. To help analyze the spread and evolution of the virus, we collated and analyzed data related to the viral genome, sequence variations, and locations in temporal and spatial distribution from GISAID. Information from the Wikipedia web page and published research papers were categorized and mined to extract epidemiological data, which was then integrated with the public dataset. Genomic and epidemiological data were matched with public information, and the data quality was verified by manual curation. Finally, an online database centered on virus genomic information and epidemiological data can be freely accessible at https://www.biosino.org/kgcov/ , which is helpful to identify relevant knowledge and devising epidemic prevention and control policies in collaboration with disease control personnel.