Hong Zheng
Profile Url: hong-zheng
Researcher at Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Palo Alto, CA 94305, USA
eLife, 2019-10-28
CARM1 is a cancer-relevant protein arginine methyltransferase that regulates many aspects of transcription. Its pharmacological inhibition is a promising anti-cancer strategy. Here SKI-73 is presented as a CARM1 chemical probe with pro-drug properties. SKI-73 can rapidly penetrate cell membranes and then be processed into active inhibitors, which are retained intracellularly with 10-fold enrichment for days. These compounds were characterized for their potency, selectivity, modes of action, and on-target engagement. SKI-73 recapitulates the effect of CARM1 knockout against breast cancer cell invasion. Single-cell RNA-seq analysis revealed that the SKI-73-associated reduction of invasiveness act via altering epigenetic plasticity and suppressing the invasion-prone subpopulation. Interestingly, SKI-73 and CARM1 knockout alter the epigenetic plasticity with remarkable difference, arguing distinct modes of action between the small-molecule and genetic perturbation. We therefore discovered a CARM1-addiction mechanism of cancer metastasis and developed a chemical probe to target this process.
Background Determining the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. Methods We developed the classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N=705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. Results We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1,417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.91 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N=97) and retrospectively (N=100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. Conclusions With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.