Biochemical characterization of caffeoyl coenzyme A 3-O-methyltransferase from wheat.

MAIN CONCLUSION: TaCCoAOMT1 is located in wheat chromosome 7A and highly expressed in stem and root. It is important for lignin biosynthesis, and associated with stem maturity but not lodging resistance. Caffeoyl coenzyme A 3-O-methyltransferases (CCoAOMTs) are one important class of enzymes to carry out the transfer of the methyl group from S-adenosylmethionine to the hydroxyl group, and play important roles in lignin and flavonoids biosynthesis. In the present study, sequences for CCoAOMT from the wheat genome were analyzed. One wheat CCoAOMT that belonged to bona fide subclade involved in lignin biosynthesis, namely TaCCoAOMT1, was obtained by the prokaryotic expression in E. coli. The three-dimensional structure prediction showed a highly similar structure of TaCCoAOMT1 with MsCCoAOMT. Recombinant TaCCoAOMT1 protein could only use caffeoyl CoA and 5-hydroxyferuloyl CoA as effective substrates and caffeoyl CoA as the best substrate. TaCCoAOMT1 had a narrow optimal pH and thermal stability. The TaCCoAOMT1 gene was highly expressed in wheat stem and root tissues, paralleled CCoAOMT enzyme activity. TaCCoAOMT1 mRNA abundance and enzyme activity increased linearly with stem maturity, but showed little difference between wheat lodging-resistant (H4546) and lodging-sensitive (C6001) cultivars in elongation, heading and milky stages. These data suggest that TaCCoAOMT1 is an important CCoAOMT for lignin biosynthesis that is critical for stem development, but not directly associated with lodging-resistant trait in wheat.

Development and evaluation of neutralizing antibodies for cross-protection against West Nile virus and Japanese encephalitis virus.

Background: West Nile virus is a severe zoonotic pathogen that can cause severe central nervous system symptoms in humans and horses, and is fatal for birds, chickens and other poultry. With no specific drugs or vaccines available, antibody-based therapy is a promising treatment. This study aims to develop neutralizing antibodies against West Nile virus and assess their cross-protective potential against Japanese encephalitis virus. Methods: Monoclonal antibodies against WNV and JEV were isolated by hybridoma technology. The therapeutic efficacy of these antibodies was evaluated using a mouse model, and a humanized version of the monoclonal antibody was generated for potential human application. Results: In this study, we generated eight monoclonal antibodies that exhibit neutralizing activity against WNV. Their therapeutic effects against WNV were validated both in vivo and in vitro. Among these antibodies, C9-G11-F3 also exhibited cross-protective activity against JEV. We also humanized the antibody to ensure that it could be used for WNV infection treatment in humans. Conclusion: This study highlights the importance of neutralizing antibodies as a promising approach for protection against West Nile virus infection and suggests their potential utility in the development of therapeutic interventions.

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.