Rapid and accurate molecular diagnostic tools are critical to efforts to minimize the impact and spread of emergent pathogens. The identification of diagnostic markers for novel pathogens presents several challenges, especially in the absence of information about population diversity and where genetic resources are limited. The objective of this study was to use comparative genomics datasets to find unique target regions suitable for the diagnosis of two fungal species causing a newly emergent blight disease of boxwood. Candidate marker regions for loop-mediated isothermal amplification (LAMP) assays were identified from draft genomes of Calonectria henricotiae and C. pseudonaviculata, as well as three related species not associated with this disease. To increase the probability of identifying unique targets, we used three approaches to mine genome datasets, based on (i) unique regions, (ii) polymorphisms, and (iii) presence/absence of regions across datasets. From a pool of candidate markers, we demonstrate LAMP assay specificity by testing related fungal species, common boxwood pathogens, and environmental samples containing 445 diverse fungal taxa. This comparative-genomics-based approach to the development of LAMP diagnostic assays is the first of its kind for fungi and could be easily applied to diagnostic marker development for other newly emergent plant pathogens.
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http://dx.doi.org/10.1038/srep26140 | DOI Listing |
BMC Res Notes
January 2025
Department of Computer Engineering, Chungbuk National University, Chungdae-ro 1, Cheongju, 28644, Republic of Korea.
Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient.
View Article and Find Full Text PDFOrphanet J Rare Dis
January 2025
Ophthalmology Department, Centro Hospitalar e Universitário de Coimbra (CHUC), Hospitais da Universidade de Coimbra (HUC), ULS Coimbra, Praceta Prof. Mota Pinto, 3000-075, Coimbra, Portugal.
Background: Syndromic genetic disorders affecting vision can also cause hearing loss, and Usher syndrome is by far the most common etiology. However, many other conditions can present dual sensory impairment. Accurate diagnosis is essential for providing patients with genetic counseling, prognostic information, and appropriate resources.
View Article and Find Full Text PDFEnviron Microbiome
January 2025
Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA.
Background: Viruses that infect prokaryotes (phages) constitute the most abundant group of biological agents, playing pivotal roles in microbial systems. They are known to impact microbial community dynamics, microbial ecology, and evolution. Efforts to document the diversity, host range, infection dynamics, and effects of bacteriophage infection on host cell metabolism are extremely underexplored.
View Article and Find Full Text PDFBMC Res Notes
January 2025
Planta Piloto de Procesos Industriales Microbiológicos (PROIMI - CONICET), Tucumán, Argentina.
Background: Postharvest lemons are affected by several fungal infections, and as alternatives to chemical fungicides for combating these infections, different microbial biocontrol agents have been studied, with the Clavispora lusitaniae 146 strain standing out. Although strain 146 has proven to be an effective agent, the influence of a microbial biological control agent on the postharvest lemon microbiome has not been studied until now. Thus, this study aimed to evaluate how the epiphytic microbiome of postharvest lemons is affected by the application of the biocontrol yeast C.
View Article and Find Full Text PDFBMC Med Genomics
January 2025
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China.
Background: Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.
Results: Compared with other methods, regression-based drug target affinity is more representative of the binding ability.
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