The enzymatic hydrolysate of fucoidan from Sargassum hemiphyllum triggers immunity in plants

Fucoidans are polysaccharides that consist predominantly of sulfated L-fucoses, from which, fucoidan oligosaccharides (FOSs) are prepared through different methods. Fucoidan has versatile physiological activities, like antiviral functions against SARS CoV-2 and bioactivitiy in enhancing immune responses. Although fucoidan or FOS has been widely used in mammals as functional foods and new drugs, its application in plants is still very limited. Moreover, whether fucoidan or its derived hydrolytic products can trigger immune responses in plants remained unknown. In this work, we demonstrate that the fucoidan enzymatic hydrolysate (FEH) prepared from Sargassum hemiphyllum triggers various immune responses, such as ROS production, MAPK activation, gene expression reprogramming, callose deposition, stomatal closure, and plant resistance to the bacterial strain Pseudomonas syringae pv. tomato (Pst) DC3000. Notably, FEH did not induce Arabidopsis root growth inhibition at the concentration used for triggering other immune responses. Our work suggests that EHF can potentially be used as a non-microbial elicitor in agricultural practices to protect plants from pathogen infection.

The enzymatic hydrolysate of fucoidan from Sargassum hemiphyllum triggers immunity in plants.

Fucoidans are polysaccharides that consist predominantly of sulfated L-fucoses, from which, fucoidan oligosaccharides (FOSs) are prepared through different methods. Fucoidan has versatile physiological activities, like antiviral functions against SARS CoV-2 and bioactivitiy in enhancing immune responses. Although fucoidan or FOS has been widely used in mammals as functional foods and new drugs, its application in plants is still very limited. Moreover, whether fucoidan or its derived hydrolytic products can trigger immune responses in plants remained unknown. In this work, we demonstrate that the fucoidan enzymatic hydrolysate (FEH) prepared from Sargassum hemiphyllum triggers various immune responses, such as ROS production, MAPK activation, gene expression reprogramming, callose deposition, stomatal closure, and plant resistance to the bacterial strain Pseudomonas syringae pv. tomato (Pst) DC3000. Notably, FEH did not induce Arabidopsis root growth inhibition at the concentration used for triggering other immune responses. Our work suggests that EHF can potentially be used as a non-microbial elicitor in agricultural practices to protect plants from pathogen infection.

Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 Food and Drug Administration (FDA)-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.

Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations.

Although social distancing can effectively contain the spread of infectious diseases by reducing social interactions, it may have economic effects. Crises such as the COVID-19 pandemic create dilemmas for policymakers because the long-term implementation of restrictive social distancing policies may cause massive economic damage and ultimately harm healthcare systems. This paper proposes an epidemic control framework that policymakers can use as a data-driven decision support tool for setting efficient social distancing targets. The framework addresses three aspects of the COVID-19 pandemic that are related to social distancing or community mobility data: modeling, financial implications, and policy-making. Thus, we explore the COVID-19 pandemic and concurrent economic situation as functions of historical pandemic data and mobility control. This approach allows us to formulate an efficient social distancing policy as a stochastic feedback control problem that minimizes the aggregated risks of disease transmission and economic volatility. We further demonstrate the use of a deep learning algorithm to solve this control problem. Finally, by applying our framework to U.S. data, we empirically examine the efficiency of the U.S. social distancing policy.