Fluorescent chemosensors facilitate the visualization of plant health and their living environment in sustainable agriculture.

Globally, 91% of plant production encounters diverse environmental stresses that adversely affect their growth, leading to severe yield losses of 50-60%. In this case, monitoring the connection between the environment and plant health can balance population demands with environmental protection and resource distribution. Fluorescent chemosensors have shown great progress in monitoring the health and environment of plants due to their high sensitivity and biocompatibility. However, to date, no comprehensive analysis and systematic summary of fluorescent chemosensors used in monitoring the correlation between plant health and their environment have been reported. Thus, herein, we summarize the current fluorescent chemosensors ranging from their design strategies to applications in monitoring plant-environment interaction processes. First, we highlight the types of fluorescent chemosensors with design strategies to resolve the bottlenecks encountered in monitoring the health and living environment of plants. In addition, the applications of fluorescent small-molecule, nano and supramolecular chemosensors in the visualization of the health and living environment of plants are discussed. Finally, the major challenges and perspectives in this field are presented. This work will provide guidance for the design of efficient fluorescent chemosensors to monitor plant health, and then promote sustainable agricultural development.

GhHAM regulates GoPGF-dependent gland development and contributes to broad-spectrum pest resistance in cotton.

Cotton is a globally cultivated crop, producing 87% of the natural fiber used in the global textile industry. The pigment glands, unique to cotton and its relatives, serve as a defense structure against pests and pathogens. However, the molecular mechanism underlying gland formation and the specific role of pigment glands in cotton's pest defense are still not well understood. In this study, we cloned a gland-related transcription factor GhHAM and generated the GhHAM knockout mutant using CRISPR/Cas9. Phenotypic observations, transcriptome analysis, and promoter-binding experiments revealed that GhHAM binds to the promoter of GoPGF, regulating pigment gland formation in cotton's multiple organs via the GoPGF-GhJUB1 module. The knockout of GhHAM significantly reduced gossypol production and increased cotton's susceptibility to pests in the field. Feeding assays demonstrated that more than 80% of the cotton bollworm larvae preferred ghham over the wild type. Furthermore, the ghham mutants displayed shorter cell length and decreased gibberellins (GA) production in the stem. Exogenous application of GA3 restored stem cell elongation but not gland formation, thereby indicating that GhHAM controls gland morphogenesis independently of GA. Our study sheds light on the functional differentiation of HAM proteins among plant species, highlights the significant role of pigment glands in influencing pest feeding preference, and provides a theoretical basis for breeding pest-resistant cotton varieties to address the challenges posed by frequent outbreaks of pests.

Multi-level bioinformatics resources support drug target discovery of protein-protein interactions.

Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.

Transfer learning empowers accurate pharmacokinetics prediction of small samples.

Accurate assessment of pharmacokinetic (PK) properties is crucial for selecting optimal candidates and avoiding downstream failures. Transfer learning is an innovative machine learning approach enabling high-throughput prediction with limited data. Recently, transfer learning methods showed promise in predicting ADME/PK parameters. Given the prolific growth of research on transfer learning for PK prediction, a comprehensive review of its advantages and challenges is imperative. This study explores the fundamentals, classifications, toolkits and applications of various transfer learning techniques for PK prediction, demonstrating their utility through three practical case studies. This work will serve as a reference for drug design researchers.

Targeting Fks1 proteins for novel antifungal drug discovery.

Fungal infections are a major threat to human health. The limited availability of antifungal drugs, the emergence of drug resistance, and a growing susceptible population highlight the critical need for novel antifungal agents. The enzymes involved in fungal cell wall synthesis offer potential targets for antifungal drug development. Recent studies have enhanced our focus on the enzyme Fks1, which synthesizes ß-1,3-glucan, a critical component of the cell wall. These studies provide a deeper understanding of Fks1's function in cell wall biosynthesis, pathogenicity, structural biology, evolutionary conservation across fungi, and interaction with current antifungal drugs. Here, we discuss the role of Fks1 in the survival and adaptation of fungi, guided by insights from evolutionary and structural analyses. Furthermore, we delve into the dynamics of Fks1 modulation with novel antifungal strategies and assess its potential as an antifungal drug target.

Evaluation of the immunoprotective effect of the recombinant Eimeria intestinalis rhoptry protein 25 and rhoptry protein 30 on New Zealand rabbits.

BACKGROUND: Rabbit coccidiosis is a parasitism caused by either one or multiple co-infections of Eimeria species. Among them, Eimeria intestinalis is the primary pathogen responsible for diarrhea, growth retardation, and potential mortality in rabbits. Concerns regarding drug resistance and drug residues have led to the development of recombinant subunit vaccines targeting Eimeria species as a promising preventive measure. The aim of this study was to assess the immunoprotective efficacy of recombinant subunit vaccines comprising EiROP25 and EiROP30 (rhoptry proteins (ROPs)) against E. intestinalis infection in rabbits. METHODS: Cloning, prokaryotic expression, and protein purification were performed to obtain EiROP25 and EiROP30. Five groups of fifty 35-day-old Eimeria-free rabbits were created (unchallenged control group, challenged control group, vector protein control group, rEiROP25 group, and rEiROP30 group), with 10 rabbits in each group. Rabbits in the rEiROP25 and rEiROP30 groups were immunized with the recombinant proteins (100 µg per rabbit) for primary and booster immunization (100 µg per rabbit) at a two-week intervals, and challenged with 7 × 104 oocysts per rabbit after an additional two-week interval. Two weeks after the challenge, the rabbits were euthanized for analysis. Weekly collections of rabbit sera were made to measure changes in specific IgG and cytokine level. Clinical symptoms and pathological changes after challenge were observed and recorded. At the conclusion of the animal experiment, lesion scores, the relative weight increase ratio, the oocyst reduction rate, and the anticoccidial index were computed. RESULTS: Rabbits immunized with rEiROP25 and rEiROP30 exhibited relative weight gain ratios of 56.57% and 72.36%, respectively. Oocysts decreased by 78.14% and 84.06% for the rEiROP25 and rEiROP30 groups, respectively. The anticoccidial indexes were 140 and 155. Furthermore, there was a noticeable drop in intestinal lesions. After the primary immunization with rEiROP25 and rEiROP30, a week later, there was a notable rise in specific IgG levels, which remained elevated for two weeks following challenge (P < 0.05). Interleukin (IL)-2 levels increased markedly in the rEiROP25 group, whereas IL-2, interferon gamma (IFN-γ), and IL-4 levels increased substantially in the rEiROP30 group (P < 0.05). CONCLUSION: Immunization of rabbits indicated that both rEiROP25 and rEiROP30 are capable of inducing an increase in specific antibody levels. rEiROP25 triggered a Th1-type immune protection response, while rEiROP30 elicited a Th1/Th2 mixed response. EiROP25 and EiROP30 can generate a moderate level of immune protection, with better efficacy observed for EiROP30. This study provides valuable insights for the promotion of recombinant subunit vaccines targeting rabbit E. intestinalis infection.

Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era.

Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.

Design, Synthesis, and Herbicidal Activity of Substituted 3-(Pyridin-2-yl)Phenylamino Derivatives.

To discover protoporphyrinogen oxidase (PPO) inhibitors with robust herbicidal activity and crop safety, three types of substituted 3-(pyridin-2-yl)phenylamino derivatives bearing amide, urea, or thiourea as side chain were designed via structure splicing strategy. Postemergence herbicidal activity assessment of 33 newly prepared compounds revealed that many of our compounds such as 6a, 7b, and 8d exhibited superior herbicidal activities against broadleaf and monocotyledon weeds to commercial acifluorfen. In particular, compound 8d exhibited excellent herbicidal activities and high crop safety at a dosage range of 37.5-150 g ai/ha. PPO inhibitory studies supported our compounds as typical PPO inhibitors. Molecular docking studies revealed that compound 8d provided effective interactions with Nicotiana tabacum PPO (NtPPO) via diverse interaction models, such as π-π stacking and hydrogen bonds. Molecular dynamics (MD) simulation studies and degradation studies were also conducted to gain insight into the inhibitory mechanism. Our study indicates that compound 8d may be a candidate molecule for the development of novel herbicides.

Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection.

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.