Visual and supersensitive detection of <i>E. pyrifoliae</i> infection in pear trees through dot enzyme‐linked immunosorbent assay and colloidal gold immunochromatographic strip techniques

Abstract Asian pear fire blight, caused by Erwinia pyrifoliae , is a devastating disease affecting pear trees. Effective integrated management of this disease depends on the timely and accurate detection and removal of diseased trees within orchards. Here, we developed four highly sensitive and specific monoclonal antibodies (MAbs) against E. pyrifoliae using isolated E. pyrifoliae as the immunogen and then established two serological techniques, dot enzyme‐linked immunosorbent assay (dot‐ELISA) and colloidal gold immunochromatographic strip (CGICS) assay, for detecting E. pyrifoliae using the obtained MAbs as detection antibodies. Both dot‐ELISA and CGICS can detect eight different E. pyrifoliae strains from China and have no cross‐reactivity with other 11 tested plant control bacteria. Furthermore, dot‐ELISA and CGICS can detect E. pyrifoliae in bacterial suspensions diluted up to 7.81 × 10 3 CFU/mL and 3.91 × 10 3 CFU/mL, respectively, which were about 4 and 8 times more sensitive than conventional polymerase chain reaction (PCR). Additionally, CGICS was capable of detecting E. pyrifoliae in pear leaf homogenates diluted up to 1:10240 (w/v, g/mL). The detection results of 20 blinded pear tissue samples using both serological techniques were consistent with those obtained from PCR. Therefore, we conclude that these two newly developed techniques are suitable for large‐scale detection of E. pyrifoliae in pear orchards.

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Publication Details

Journal
New plant protection.
Published
2025-03-01
DOI
https://doi.org/10.1002/npp2.70001
Citations
1
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
0.81
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article

Visual and supersensitive detection of E. pyrifoliae infection in pear trees through dot enzyme‐linked immunosorbent assay and colloidal gold immunochromatographic strip techniques

Xi Zhang, Binggan Lou, Xueping Zhou, Cui Zhang et al.
1 citations
New plant protection.
Identification and Quantification in Food
0.81
article

Visual and supersensitive detection of E. pyrifoliae infection in pear trees through dot enzyme‐linked immunosorbent assay and colloidal gold immunochromatographic strip techniques

Xi Zhang, Binggan Lou, Xueping Zhou, Cui Zhang, Jie Dong, Jianxiang Wu, Nairu Liu
article en
1 citations

Abstract

Abstract Asian pear fire blight, caused by Erwinia pyrifoliae , is a devastating disease affecting pear trees. Effective integrated management of this disease depends on the timely and accurate detection and removal of diseased trees within orchards. Here, we developed four highly sensitive and specific monoclonal antibodies (MAbs) against E. pyrifoliae using isolated E. pyrifoliae as the immunogen and then established two serological techniques, dot enzyme‐linked immunosorbent assay (dot‐ELISA) and colloidal gold immunochromatographic strip (CGICS) assay, for detecting E. pyrifoliae using the obtained MAbs as detection antibodies. Both dot‐ELISA and CGICS can detect eight different E. pyrifoliae strains from China and have no cross‐reactivity with other 11 tested plant control bacteria. Furthermore, dot‐ELISA and CGICS can detect E. pyrifoliae in bacterial suspensions diluted up to 7.81 × 10 3 CFU/mL and 3.91 × 10 3 CFU/mL, respectively, which were about 4 and 8 times more sensitive than conventional polymerase chain reaction (PCR). Additionally, CGICS was capable of detecting E. pyrifoliae in pear leaf homogenates diluted up to 1:10240 (w/v, g/mL). The detection results of 20 blinded pear tissue samples using both serological techniques were consistent with those obtained from PCR. Therefore, we conclude that these two newly developed techniques are suitable for large‐scale detection of E. pyrifoliae in pear orchards.

New plant protection.Vol. 2(1)
Institute of Plant Protection (CN), Chinese Academy of Agricultural Sciences (CN), Sanya University (CN), China National Rice Research Institute (CN), Zhejiang University (CN)
Openalex Percentile: Top 30%
Identification and Quantification in Food
0.81
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