Luteolin inhibits GPVI-mediated platelet activation, oxidative stress, and thrombosis

Introduction: Luteolin inhibits platelet activation and thrombus formation, but the mechanisms are unclear. This study investigated the effects of luteolin on GPVI-mediated platelet activation in vitro and explored the effect of luteolin on thrombosis, coagulation, and platelet production in vivo.Methods: Washed human platelets were used for aggregation, membrane protein expression, ATP, Ca2+, and LDH release, platelet adhesion/spreading, and clot retraction experiments. Washed human platelets were used to detect collagen and convulxin-induced reactive oxygen species production and endogenous antioxidant effects. C57BL/6 male mice were used for ferric chloride-induced mesenteric thrombosis, collagen-epinephrine induced acute pulmonary embolism, tail bleeding, coagulation function, and luteolin toxicity experiments. The interaction between luteolin and GPVI was analyzed using solid phase binding assay and surface plasmon resonance (SPR).Results: Luteolin inhibited collagen- and convulxin-mediated platelet aggregation, adhesion, and release. Luteolin inhibited collagen- and convulxin-induced platelet ROS production and increased platelet endogenous antioxidant capacity. Luteolin reduced convulxin-induced activation of ITAM and MAPK signaling molecules. Molecular docking simulation showed that luteolin forms hydrogen bonds with GPVI. The solid phase binding assay showed that luteolin inhibited the interaction between collagen and GPVI. Surface plasmon resonance showed that luteolin bonded GPVI. Luteolin inhibited integrin αIIbβ3-mediated platelet activation. Luteolin inhibited mesenteric artery thrombosis and collagen- adrenergic-induced pulmonary thrombosis in mice. Luteolin decreased oxidative stress in vivo. Luteolin did not affect coagulation, hemostasis, or platelet production in mice.Discussion: Luteolin may be an effective and safe antiplatelet agent target for GPVI. A new mechanism (decreased oxidative stress) for the anti-platelet activity of luteolin has been identified

Genetic Dissection of Leaf Senescence in Rice

Leaf senescence, the final stage of leaf development, is a complex and highly regulated process that involves a series of coordinated actions at the cellular, tissue, organ, and organism levels under the control of a highly regulated genetic program. In the last decade, the use of mutants with different levels of leaf senescence phenotypes has led to the cloning and functional characterizations of a few genes, which has greatly improved the understanding of genetic mechanisms underlying leaf senescence. In this review, we summarize the recent achievements in the genetic mechanisms in rice leaf senescence

Advances in Sensing, Response and Regulation Mechanism of Salt Tolerance in Rice

Soil salinity is a serious menace in rice production threatening global food security. Rice responses to salt stress involve a series of biological processes, including antioxidation, osmoregulation or osmoprotection, and ion homeostasis, which are regulated by different genes. Understanding these adaptive mechanisms and the key genes involved are crucial in developing highly salt-tolerant cultivars. In this review, we discuss the molecular mechanisms of salt tolerance in rice—from sensing to transcriptional regulation of key genes—based on the current knowledge. Furthermore, we highlight the functionally validated salt-responsive genes in rice

Integrated Multi-Omics Perspective to Strengthen the Understanding of Salt Tolerance in Rice

Salt stress is one of the major constraints to rice cultivation worldwide. Thus, the development of salt-tolerant rice cultivars becomes a hotspot of current rice breeding. Achieving this goal depends in part on understanding how rice responds to salt stress and uncovering the molecular mechanism underlying this trait. Over the past decade, great efforts have been made to understand the mechanism of salt tolerance in rice through genomics, transcriptomics, proteomics, metabolomics, and epigenetics. However, there are few reviews on this aspect. Therefore, we review the research progress of omics related to salt tolerance in rice and discuss how these advances will promote the innovations of salt-tolerant rice breeding. In the future, we expect that the integration of multi-omics salt tolerance data can accelerate the solution of the response mechanism of rice to salt stress, and lay a molecular foundation for precise breeding of salt tolerance

Protein-protein interactions prediction via multimodal deep polynomial network and regularized extreme learning machine

Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs prediction is highly desirable. PPIs endow with protein amino acid mutation rate and two physicochemical properties of protein (e.g., hydrophobicity and hydrophilicity). Deep polynomial network (DPN) is well-suited to integrate these modalities since it can represent any function on a finite sample dataset via the supervised deep learning algorithm. We propose a multimodal DPN (MDPN) algorithm to effectively integrate these modalities to enhance prediction performance. MDPN consists of a two-stage DPN, the first stage feeds multiple protein features into DPN encoding to obtain high-level feature representation while the second stage fuses and learns features by cascading three types of high-level features in the DPN encoding. We employ a regularized extreme learning machine to predict PPIs. The proposed method is tested on the public dataset of H. pylori, Human, and Yeast and achieves average accuracies of 97.87%, 99.90%, and 98.11%, respectively. The proposed method also achieves good accuracies on other datasets. Furthermore, we test our method on three kinds of PPI networks and obtain superior prediction results.Accepted versio