Generation and Screening of T-DNA Insertion Mutants Mediated by Agrobacterium tumefaciens in the Garden Asparagus Stem Blight Pathogen Phomopsis asparagi

Abstract

The garden asparagus stem blight caused by filamentous fungus Phomopsis asparagi exposes a serious threat on asparagus production globally. However, to present, we understand poorly about the molecular mechanisms of fungal pathogenicity. To facilitate functional genomics research of P. asparagi, here we developed a highly efficient and stable Agrobacterium tumefaciens-mediated transformation approach which yielded 150-200 transformants per 1 × 10 conidia. Our results indicated that 25 °C, acetosyringone concentration of 150 μmol/L, and 72 h were recommended as optimal co-cultivation conditions for the transformation. Using this transformation approach, we constructed a T-DNA insertion mutant library containing 1253 strains. Twenty randomly selected T-DNA insertion mutants were able to grow on 0.2 × PDA selective media after five successive subcultures without selective pressure, indicating that the exogenous T-DNA was stably integrated into the P. asparagi genome. We confirmed several randomly selected mutants using PCR with primers specific to the hph gene. Southern blots suggested that three out of the five selected mutants have a single T-DNA insertion. Interestingly, multiple mutant candidates with growth defects were obtained from the growth assay. Moreover, several mutants were selected for further analysis on the T-DNA flanking sequences through TAIL-PCR analysis. A sequence comparison of total junction fragments implied that the insertion of T-DNA within P. asparagi genome appeared to be a random event. The transformation technology and genetic resources developed here will facilitate studies of pathogenic mechanisms in this devastating filamentous fungal pathogen of garden asparagus.

Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns

“…The goal of single-hidden layer neural network learning is to minimize the output training error, which can approach an arbitrary ε > 0, that is, there are ω i , β i , and b i , so that Equation (19)…”

“…If the disease is more severe the diseased spots join together and circle the stem, and the affected stem dies and becomes brittle and easily broken [14][15][16]. Conventional methods for the detection of asparagus stem blight mainly include morphological identification, physiological and biochemical identification [17,18], and molecular biological identification [19][20][21]. Morphological identification is subjectively dependent and inefficient [22,23].…”

“…Asparagus stem blight mainly affects the stem, and the asparagus mother stem is occluded by canopy branches and leaves, making it difficult to detect asparagus stem blight. An innovative idea is to identify stem blight by the canopy of the asparagus mother stem, but there is no significant difference in the appearance and morphology Conventional methods for the detection of asparagus stem blight mainly include morphological identification, physiological and biochemical identification [17,18], and molecular biological identification [19][20][21]. Morphological identification is subjectively dependent and inefficient [22,23].…”