Necrosis and ethylene-inducing peptide 1 (Nep1) -like proteins (NLP) are secreted by multiple taxonomically unrelated plant pathogens (bacteria, fungi, and oomycete) and are best known for inducing cell death and immune responses in dicotyledonous plants. A group of putative genes from obligate biotrophic oomycete were predicted by RNA-Seq in our previous study, but their activity has not been established. Therefore, we analyzed the () family and identified seven genes. They all belong to type 1 genes and form a -specific cluster when compared with other pathogen genes. The expression of was induced during early infection process and the expression patterns could be categorized into two groups. -mediated transient expression assays revealed that only PvNLP7 was cytotoxic and could induce resistance in . Functional analysis showed that PvNLP4, PvNLP5, PvNLP7, and PvNLP10 significantly improved disease resistance of to . Moreover, the four genes caused an inhibition of plant growth which is typically associated with enhanced immunity when over-expressed in Arabidopsis. Further research found that PvNLP7 could activate the expression of defense-related genes and its conserved NPP1 domain was critical for cell death- and immunity-inducing activity. This record of genes from showed a functional diversification, laying a foundation for further study on pathogenic mechanism of the devastating pathogen.
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http://dx.doi.org/10.1080/15592324.2021.2000791 | DOI Listing |
Curr Eye Res
January 2025
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Purpose: This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
Methods: The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups.
World J Pediatr
January 2025
Cardiac Arrhythmia Center, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 Beilishi Road, Xicheng District, Beijing, 100037, China.
Background: Heart failure (HF) significantly impacts the cardiovascular health of children and adolescents. This study aims to assess epidemiologic trends in HF across sex, age, region, and time period.
Methods: The number and age-standardized rate (ASR) of prevalence and years lived with disability (YLDs) were derived from the Global Burden of Disease Study 2019.
Neurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
View Article and Find Full Text PDFGenes Genomics
January 2025
Plant Molecular Breeding and Bioinformatics Laboratory, Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh.
Background: TCP proteins are plant-specific transcription factors that play essential roles in various developmental processes, including leaf morphogenesis and senescence, flowering, lateral branching, hormone crosstalk, and stress responses. However, a comprehensive analysis of genome-wide TCP genes and their expression patterns in melon is yet to be done.
Objective: The present study aims to identify and analyze the TCP genes in the melon genome and understand their putative functions.
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