Self-supervised contrastive learning for integrative single cell RNA-seq data analysis.

We present a novel self-supervised Contrastive LEArning framework for single-cell ribonucleic acid (RNA)-sequencing (CLEAR) data representation and the downstream analysis. Compared with current methods, CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events simultaneously. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43 695 single cells from peripheral blood mononuclear cells.

Invasive pneumococcal disease, pneumococcal pneumonia and all-cause pneumonia in Hong Kong during the COVID-19 pandemic compared with the preceding 5 years: a retrospective observational study

ObjectivesTo compare the incidence and severity of invasive pneumococcal diseases (IPDs), pneumococcal pneumonia and all-cause pneumonia during the COVID-19 pandemic period with universal masking and social distancing with that of previous 5 years.DesignRetrospective observational study on incidence of IPDs, pneumococcal pneumonia and all-cause pneumonia between January 2015–December 2019 and March 2020–March 2021. January–February 2020 was excluded from analysis as it was treated as a transitional period between normal time and pandemic.SettingEpisode-based data by retrieval of hospitalisation records from the Hospital Authority’s territory-wide electronic medical record database in Hong Kong.ParticipantsHospitalised patients with IPD (n=742), pneumococcal pneumonia (n=2163) and all-cause pneumonia (including COVID-19 pneumonia, n=453 999) aged 18 years or above. Control diagnoses were included to assess confounding from health-seeking behaviours.Primary and secondary outcomesPrimary outcome is the incidence of diseases between two periods. Secondary outcomes include disease severity surrogated by length of stay and mortality.ResultsMonthly average number of IPD, pneumococcal pneumonia and all-cause pneumonia hospitalisation significantly decreased by 88.9% (95% CI 79.8% to 98.0%, p<0.0005), 72.5% (95% CI 65.9% to 79.1%, p<0.0005) and 17.5% (95% CI 16.8% to 18.2%, p<0.0005), respectively. Changes in trend from January 2015–December 2019 to March 2020–March 2021 were −70% (95% CI −87% to −35%, p=0.0025), –43% (95% CI −59% to −19%, p=0.0014) and −11% (95% CI −13% to −10%, p<0.0005), respectively. Length of stay for IPD and pneumococcal pneumonia episodes were insignificantly different in the two periods. No reductions in hospitalisations for control diagnoses were observed.ConclusionsIncidence of IPD, pneumococcal pneumonia and all-cause pneumonia decreased during the COVID-19 pandemic. This was observed with universal masking and social distancing. We postulated this is related to reduced transmission of respiratory viruses and bacteria.

Network Pharmacology and Comparative Transcriptome Reveals Biotargets and Mechanisms of Curcumol Treating Lung Adenocarcinoma Patients With COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has led to 4,255,892 deaths worldwide. Although COVID-19 vaccines are available, mutant forms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have reduced the effectiveness of vaccines. Patients with cancer are more vulnerable to COVID-19 than patients without cancer. Identification of new drugs to treat COVID-19 could reduce mortality rate, and traditional Chinese Medicine(TCM) has shown potential in COVID-19 treatment. In this study, we focused on lung adenocarcinoma (LUAD) patients with COVID-19. We aimed to investigate the use of curcumol, a TCM, to treat LUAD patients with COVID-19, using network pharmacology and systematic bioinformatics analysis. The results showed that LUAD and patients with COVID-19 share a cluster of common deregulated targets. The network pharmacology analysis identified seven core targets (namely, AURKA, CDK1, CCNB1, CCNB2, CCNE1, CCNE2, and TTK) of curcumol in patients with COVID-19 and LUAD. Clinicopathological analysis of these targets demonstrated that the expression of these targets is associated with poor patient survival rates. The bioinformatics analysis further highlighted the involvement of this target cluster in DNA damage response, chromosome stability, and pathogenesis of LUAD. More importantly, these targets influence cell-signaling associated with the Warburg effect, which supports SARS-CoV-2 replication and inflammatory response. Comparative transcriptomic analysis on in vitro LUAD cell further validated the effect of curcumol for treating LUAD through the control of cell cycle and DNA damage response. This study supports the earlier findings that curcumol is a potential treatment for patients with LUAD and COVID-19.

A tRNA-derived fragment from Chinese yew suppresses ovarian cancer growth via targeting TRPA1

Drug discovery from plants usually focuses on small molecules rather than such biological macromolecules as RNAs. Although plant transfer RNA (tRNA)-derived fragment (tRF) has been associated with the developmental and defense mechanisms in plants, its regulatory role in mammals remains unclear. By employing a novel reverse small interfering RNA (siRNA) screening strategy, we show that a tRF mimic (antisense derived from the 5′ end of tRNAHis(GUG) of Chinese yew) exhibits comparable anti-cancer activity with that of taxol on ovarian cancer A2780 cells, with a 16-fold lower dosage than that of taxol. A dual-luciferase reporter assay revealed that tRF-T11 directly targets the 3′ UTR of oncogene TRPA1 mRNA. Furthermore, an Argonaute-RNA immunoprecipitation (AGO-RIP) assay demonstrated that tRF-T11 can interact with AGO2 to suppress TRPA1 via an RNAi pathway. This study uncovers a new role of plant-derived tRFs in regulating endogenous genes. This holds great promise for exploiting novel RNA drugs derived from nature and sheds light on the discovery of unknown molecular targets of therapeutics. Graphical

A tRNA-derived fragment from Chinese yew suppresses ovarian cancer growth via targeting TRPA1.

Drug discovery from plants usually focuses on small molecules rather than such biological macromolecules as RNAs. Although plant transfer RNA (tRNA)-derived fragment (tRF) has been associated with the developmental and defense mechanisms in plants, its regulatory role in mammals remains unclear. By employing a novel reverse small interfering RNA (siRNA) screening strategy, we show that a tRF mimic (antisense derived from the 5' end of tRNAHis(GUG) of Chinese yew) exhibits comparable anti-cancer activity with that of taxol on ovarian cancer A2780 cells, with a 16-fold lower dosage than that of taxol. A dual-luciferase reporter assay revealed that tRF-T11 directly targets the 3' UTR of oncogene TRPA1 mRNA. Furthermore, an Argonaute-RNA immunoprecipitation (AGO-RIP) assay demonstrated that tRF-T11 can interact with AGO2 to suppress TRPA1 via an RNAi pathway. This study uncovers a new role of plant-derived tRFs in regulating endogenous genes. This holds great promise for exploiting novel RNA drugs derived from nature and sheds light on the discovery of unknown molecular targets of therapeutics.