Background: Emerging evidence suggest the critical role of circular RNAs (circRNAs) in disease development especially in various cancers. However, the oncogenic role of circRNAs in hepatocellular carcinoma (HCC) is still largely unknown. Methods: RNA sequencing was performed to identify significantly upregulated circRNAs in paired HCC tissues and non-tumor tissues. CCK-8 assay, colony formation, transwell, and xenograft mouse models were used to investigate the role of circRNAs in HCC proliferation and metastasis. Small interfering RNA (siRNA) was used to silence gene expression. RNA immunoprecipitation, biotin pull-down, RNA pull-down, luciferase reporter assay and western blot were used to explore the underlying molecular mechanisms. Results: Hsa_circ_0095868, derived from exon 5 of the MDK gene (named circMDK), was identified as a new oncogenic circRNA that was significantly upregulated in HCC. The upregulation of circMDK was associated with the modification of N6-methyladenosine (m⁶A) and poor survival in HCC patients. Mechanistically, circMDK sponged miR-346 and miR-874-3p to upregulate ATG16L1 (Autophagy Related 16 Like 1), resulting to the activation of PI3K/AKT/mTOR signaling pathway to promote cell proliferation, migration and invasion. Poly (β-amino esters) (PAEs) were synthesized to assist the delivery of circMDK siRNA (PAE-siRNA), which effectively inhibited tumor progression without obvious adverse effects in four liver tumor models including subcutaneous, metastatic, orthotopic and patient-derived xenograft (PDX) models. Conclusions: CircMDK could serve as a potential tumor biomarker that promotes the progression of HCC via the miR-346/874-3p-ATG16L1 axis. The PAE-based delivery of siRNA improved the stability and efficiency of siRNA targeting circMDK. The PAE-siRNA nanoparticles effectively inhibited HCC proliferation and metastasis in vivo. Our current findings offer a promising nanotherapeutic strategy for the treatment of HCC. Graphical

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a widespread outbreak of highly pathogenic coronavirus disease 2019 (COVID-19). It is therefore important and timely to characterize interactions between the virus and host cell at the molecular level to understand its disease pathogenesis. To gain insights, we performed high-throughput sequencing that generated time-series data simultaneously for bioinformatics analysis of virus genomes and host transcriptomes implicated in SARS-CoV-2 infection. Our analysis results showed that the rapid growth of the virus was accompanied by an early intensive response of host genes. We also systematically compared the molecular footprints of the host cells in response to SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV). Upon infection, SARS-CoV-2 induced hundreds of up-regulated host genes hallmarked by a significant cytokine production, followed by virus-specific host antiviral responses. While the cytokine and antiviral responses triggered by SARS-CoV and MERS-CoV were only observed during the late stage of infection, the host antiviral responses during the SARS-CoV-2 infection were gradually enhanced lagging behind the production of cytokine. The early rapid host responses were potentially attributed to the high efficiency of SARS-CoV-2 entry into host cells, underscored by evidence of a remarkably up-regulated gene expression of TPRMSS2 soon after infection. Taken together, our findings provide novel molecular insights into the mechanisms underlying the infectivity and pathogenicity of SARS-CoV-2.

COVID-19 is mainly associated with respiratory distress syndrome, but a subset of patients often present gastrointestinal (GI) symptoms. Imbalances of gut microbiota have been previously linked to respiratory virus infection. Understanding how the gut–lung axis affects the progression of COVID-19 can provide a novel framework for therapies and management. In this study, we examined the gut microbiota of patients with COVID-19 (n = 47) and compared it to healthy controls (n = 19). Using shotgun metagenomic sequencing, we have identified four microorganisms unique in COVID-19 patients, namely Streptococcus thermophilus, Bacteroides oleiciplenus, Fusobacterium ulcerans, and Prevotella bivia. The abundances of Bacteroides stercoris, B. vulgatus, B. massiliensis, Bifidobacterium longum, Streptococcus thermophilus, Lachnospiraceae bacterium 5163FAA, Prevotella bivia, Erysipelotrichaceae bacterium 6145, and Erysipelotrichaceae bacterium 2244A were enriched in COVID-19 patients, whereas the abundances of Clostridium nexile, Streptococcus salivarius, Coprococcus catus, Eubacterium hallii, Enterobacter aerogenes, and Adlercreutzia equolifaciens were decreased (p < 0.05). The relative abundance of butyrate-producing Roseburia inulinivorans is evidently depleted in COVID-19 patients, while the relative abundances of Paraprevotella sp. and the probiotic Streptococcus thermophilus were increased. We further identified 30 KEGG orthology (KO) modules overrepresented, with 7 increasing and 23 decreasing modules. Notably, 15 optimal microbial markers were identified using the random forest model to have strong diagnostic potential in distinguishing COVID-19. Based on Spearman’s correlation, eight species were associated with eight clinical indices. Moreover, the increased abundance of Bacteroidetes and decreased abundance of Firmicutes were also found across clinical types of COVID-19. Our findings suggest that the alterations of gut microbiota in patients with COVID-19 may influence disease severity. Our COVID-19 classifier, which was cross-regionally verified, provides a proof of concept that a set of microbial species markers can distinguish the presence of COVID-19.