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A Pathogen Secreted Protein as a Detection Marker for Citrus Huanglongbing

Overview of attention for article published in Frontiers in Microbiology, October 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

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23 X users
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1 Facebook page

Citations

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109 Mendeley
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Title
A Pathogen Secreted Protein as a Detection Marker for Citrus Huanglongbing
Published in
Frontiers in Microbiology, October 2017
DOI 10.3389/fmicb.2017.02041
Pubmed ID
Authors

Deborah Pagliaccia, Jinxia Shi, Zhiqian Pang, Eva Hawara, Kelley Clark, Shree P. Thapa, Agustina D. De Francesco, Jianfeng Liu, Thien-Toan Tran, Sohrab Bodaghi, Svetlana Y. Folimonova, Veronica Ancona, Ashok Mulchandani, Gitta Coaker, Nian Wang, Georgios Vidalakis, Wenbo Ma

Abstract

The citrus industry is facing an unprecedented crisis due to Huanglongbing (HLB, aka citrus greening disease), a bacterial disease associated with the pathogen Candidatus Liberibacter asiaticus (CLas) that affects all commercial varieties. Transmitted by the Asian citrus psyllid (ACP), CLas colonizes citrus phloem, leading to reduced yield and fruit quality, and eventually tree decline and death. Since adequate curative measures are not available, a key step in HLB management is to restrict the spread of the disease by identifying infected trees and removing them in a timely manner. However, uneven distribution of CLas cells in infected trees and the long latency for disease symptom development makes sampling of trees for CLas detection challenging. Here, we report that a CLas secreted protein can be used as a biomarker for detecting HLB infected citrus. Proteins secreted from CLas cells can presumably move along the phloem, beyond the site of ACP inoculation and CLas colonized plant cells, thereby increasing the chance of detecting infected trees. We generated a polyclonal antibody that effectively binds to the secreted protein and developed serological assays that can successfully detect CLas infection. This work demonstrates that antibody-based diagnosis using a CLas secreted protein as the detection marker for infected trees offers a high-throughput and economic approach that complements the approved quantitative polymerase chain reaction-based methods to enhance HLB management programs.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 109 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 24%
Student > Ph. D. Student 16 15%
Student > Master 15 14%
Professor > Associate Professor 5 5%
Student > Bachelor 4 4%
Other 12 11%
Unknown 31 28%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 39%
Biochemistry, Genetics and Molecular Biology 15 14%
Immunology and Microbiology 5 5%
Environmental Science 3 3%
Engineering 3 3%
Other 5 5%
Unknown 35 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 December 2017.
All research outputs
#2,358,502
of 24,554,073 outputs
Outputs from Frontiers in Microbiology
#1,824
of 27,883 outputs
Outputs of similar age
#45,730
of 333,009 outputs
Outputs of similar age from Frontiers in Microbiology
#55
of 537 outputs
Altmetric has tracked 24,554,073 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 27,883 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 333,009 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 537 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.