There is an urgent need to identify the cellular and molecular mechanisms responsible for severe COVID-19 that results in death. We initially performed both untargeted and targeted lipidomics as well as focused biochemical analyses of 127 plasma samples and found elevated metabolites associated with secreted phospholipase A₂ (sPLA₂) activity and mitochondrial dysfunction in patients with severe COVID-19. Deceased COVID-19 patients had higher levels of circulating, catalytically active sPLA₂ group IIA (sPLA₂-IIA), with a median value that was 9.6-fold higher than that for patients with mild disease and 5.0-fold higher than the median value for survivors of severe COVID-19. Elevated sPLA₂-IIA levels paralleled several indices of COVID-19 disease severity (e.g., kidney dysfunction, hypoxia, multiple organ dysfunction). A decision tree generated by machine learning identified sPLA₂-IIA levels as a central node in the stratification of patients who died from COVID-19. Random forest analysis and least absolute shrinkage and selection operator–based (LASSO-based) regression analysis additionally identified sPLA₂-IIA and blood urea nitrogen (BUN) as the key variables among 80 clinical indices in predicting COVID-19 mortality. The combined PLA-BUN index performed significantly better than did either one alone. An independent cohort (n = 154) confirmed higher plasma sPLA₂-IIA levels in deceased patients compared with levels in plasma from patients with severe or mild COVID-19, with the PLA-BUN index–based decision tree satisfactorily stratifying patients with mild, severe, or fatal COVID-19. With clinically tested inhibitors available, this study identifies sPLA₂-IIA as a therapeutic target to reduce COVID-19 mortality.

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

As the most prevalent pathogen of duck viral hepatitis (DVH), duck hepatitis A virus genotype 3 (DHAV-3) has caused huge economic losses to the duck industry in China. Herein, we obtained whole-transcriptome sequencing data of susceptible (S) and resistant (R) Pekin duckling samples at 0 h, 12 h, and 24 h after DHAV3 infection. We found that DHAV-3 infection induces 5,396 differentially expressed genes (DEGs), 85 differentially expressed miRNAs (DEMs), and 727 differentially expressed lncRNAs (DELs) at 24 hpi in S vs. R ducks, those upregulated genes were enriched in inflammation and cell communications pathways and downregulated genes were related to metabolic processes. Upregulated genes showed high connectivity with the miR-33, miR-193, and miR-11591, and downregulated genes were mainly regulated by miR-2954, miR-125, and miR-146b. With the construction of lncRNA-miRNA-mRNA axis, we further identified a few aberrantly expressed lncRNAs (e.g., MSTRG.36194.1, MSTRG.50601.1, MSTRG.34328.7, and MSTRG.29445.1) that regulate expression of hub genes (e. g., THBD, CLIC2, IL8, ACOX2, GPHN, SMLR1, and HAO1) by sponging those highly connected miRNAs. Altogether, our findings defined a dual role of ncRNAs in immune and metabolic regulation during DHAV-3 infection, suggesting potential new targets for treating DHAV3 infected ducks.

Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA–cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs.

The balance between cell quiescence and proliferation is fundamental to tissue physiology and homeostasis. Recent studies have shown that quiescence is not a passive and homogeneous state but actively maintained and heterogeneous. These cellular characteristics associated with quiescence were observed primarily in cultured cells under a static medium. However, cells in vivo face different microenvironmental conditions, particularly, under interstitial fluid flows distributed through extracellular matrices. Interstitial fluid flow exerts shear stress on cells and matrix strain, and results in continuous replacement of extracellular factors. In this study, we analyzed individual cells under varying fluid flow rates in microfluidic devices. We found quiescence characteristics previously identified under conventional static medium, including serum signal-dependant quiescence entry and exit and time-dependant quiescence deepening, are also present under continuous fluid flow. Furthermore, increasing the flow rate drives cells to shallower quiescence and become more likely to reenter the cell cycle upon growth stimulation. This effect is due to flow-induced physical and biochemical cues. Specifically, increasing shear stress or extracellular factor replacement individually, without altering other parameters, results in shallow quiescence. We show our experimental results can be quantitatively explained by a mathematical model connecting extracellular fluid flow to an Rb-E2f bistable switch that regulates the quiescence-to-proliferation transition. Our findings uncover a previously unappreciated mechanism that likely underlies the heterogeneous responses of quiescent cells for tissue repair and regeneration in different physiological tissue microenvironments.