Parthenolide reveals an allosteric mode to inhibit the deISGylation activity of SARS-CoV­2 papain-like protease.

The coronavirus papain-like protease (PLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for viral polypeptide cleavage and the deISGylation of interferon-stimulated gene 15 (ISG15), which enable it to participate in virus replication and host innate immune pathways. Therefore, PLpro is considered an attractive antiviral drug target. Here, we show that parthenolide, a germacrane sesquiterpene lactone, has SARS-CoV-2 PLpro inhibitory activity. Parthenolide covalently binds to Cys-191 or Cys-194 of the PLpro protein, but not the Cys-111 at the PLpro catalytic site. Mutation of Cys-191 or Cys-194 reduces the activity of PLpro. Molecular docking studies show that parthenolide may also form hydrogen bonds with Lys-192, Thr-193, and Gln-231. Furthermore, parthenolide inhibits the deISGylation but not the deubiquitinating activity of PLpro in vitro. These results reveal that parthenolide inhibits PLpro activity by allosteric regulation.

Intelligent classification of platelet aggregates by agonist type.

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

Platelets are small cells in the blood that primarily help stop bleeding after an injury by sticking together with other blood cells to form a clot that seals the broken blood vessel. Blood clots, however, can sometimes cause harm. For example, if a clot blocks the blood flow to the heart or the brain, it can result in a heart attack or stroke, respectively. Blood clots have also been linked to harmful inflammation and the spread of cancer, and there are now preliminary reports of remarkably high rates of clotting in COVID-19 patients in intensive care units. A variety of chemicals can cause platelets to stick together. It has long been assumed that it would be impossible to tell apart the clots formed by different chemicals (which are also known as agonists). This is largely because these aggregates all look very similar under a microscope, making it incredibly time consuming for someone to look at enough microscopy images to reliably identify the subtle differences between them. However, finding a way to distinguish the different types of platelet aggregates could lead to better ways to diagnose or treat blood vessel-clogging diseases. To make this possible, Zhou, Yasumoto et al. have developed a method called the "intelligent platelet aggregate classifier" or iPAC for short. First, numerous clot-causing chemicals were added to separate samples of platelets taken from healthy human blood. The method then involved using high-throughput techniques to take thousands of images of these samples. Then, a sophisticated computer algorithm called a deep learning model analyzed the resulting image dataset and "learned" to distinguish the chemical causes of the platelet aggregates based on subtle differences in their shapes. Finally, Zhou, Yasumoto et al. verified iPAC method's accuracy using a new set of human platelet samples. The iPAC method may help scientists studying the steps that lead to clot formation. It may also help clinicians distinguish which clot-causing chemical led to a patient's heart attack or stroke. This could help them choose whether aspirin or another anti-platelet drug would be the best treatment. But first more studies are needed to confirm whether this method is a useful tool for drug selection or diagnosis.