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Critical Reviews™ in Immunology

Publicado 6 números por año

ISSN Imprimir: 1040-8401

ISSN En Línea: 2162-6472

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.3 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 2.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00079 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.24 SJR: 0.429 SNIP: 0.287 CiteScore™:: 2.7 H-Index: 81

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Analysis of Early Host Responses for Asymptomatic Disease Detection and Management of Specialty Crops

Volumen 30, Edición 3, 2010, pp. 277-289
DOI: 10.1615/CritRevImmunol.v30.i3.50
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Abhaya M. Dandekar
Plant Sciences, UC Davis, Davis, CA 95616, USA
Federico Martinelli
Department Plant Sciences, MS2, University of California
Cristina E. Davis
Mechanical and Aerospace Engineering Department
Abhinav Bhushan
Mechanical and Aerospace Engineering Department
Weixiang Zhao
Mechanical and Aerospace Engineering Department
Oliver Fiehn
Genome Center and Bioinformatics Program
Kirsten Skogerson
Genome Center and Bioinformatics Program
Gert Wohlgemuth
Genome Center and Bioinformatics Program
Raissa D'Souza
Mechanical and Aerospace Engineering Department; Genome Center and Bioinformatics Program
Soumen Roy
Center for Computational Science and Engineering
Russell L. Reagan
Department Plant Sciences, MS2, University of California, Davis
Dawei Lin
Genome Center and Bioinformatics Program
R. Bruce Cary
Mesa Tech International, Inc. Santa Fe, New Mexico, USA
Paige Pardington
Biosciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Goutam Gupta
Biosciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

SINOPSIS

The rapid and unabated spread of vector-borne diseases within US specialty crops threatens our agriculture, our economy, and the livelihood of growers and farm workers. Early detection of vector-borne pathogens is an essential step for the accurate surveillance and management of vector-borne diseases of specialty crops. Currently, we lack the tools that would detect the infectious agent at early (primary) stages of infection with a high degree of sensitivity and specificity. In this paper, we outline a strategy for developing an integrated suite of platform technologies to enable rapid, early disease detection and diagnosis of huanglongbing (HLB), the most destructive citrus disease. The research has two anticipated outcomes: i) identification of very early, disease-specific biomarkers using a knowledge base of translational genomic information on host and pathogen responses associated with early (asymptomatic) disease development; and ii) development and deployment of novel sensors that capture these and other related biomarkers and aid in presymptomatic disease detection. By combining these two distinct approaches, it should be possible to identify and defend the crop by interdicting pathogen spread prior to the rapid expansion phase of the disease. We believe that similar strategies can also be developed for the surveillance and management of diseases affecting other economically important specialty crops.

PALABRAS CLAVE: biological regulatory network, differential mobility spectrometry, gas chromatography-coupled time-of-fight mass spectrometry, huanglongbing, induced volatile organic compound, lateral flow microarray, microbe-associated molecular pattern, specialty crops, Twister
CITADO POR

  1. Ibáñez Ana M., Martinelli Federico, Reagan Russell L., Uratsu Sandra L., Vo Anna, Tinoco Mario A., Phu My L., Chen Ying, Rocke David M., Dandekar Abhaya M., Transcriptome and metabolome analysis of Citrus fruit to elucidate puffing disorder, Plant Science, 217-218, 2014. Crossref

  2. Martinelli F., Remorini D., Saia S., Massai R., Tonutti P., Metabolic profiling of ripe olive fruit in response to moderate water stress, Scientia Horticulturae, 159, 2013. Crossref

  3. Martinelli F., Scalenghe R., Giovino A., Marino P., Aksenov A. A., Pasamontes A., Peirano D. J., Davis C. E., Dandekar A., Proposal of aCitrustranslational genomic approach for early and infield detection of Flavescence dorée in Vitis, Plant Biosystems - An International Journal Dealing with all Aspects of Plant Biology, 150, 1, 2016. Crossref

  4. Martinelli Federico, Scalenghe Riccardo, Davino Salvatore, Panno Stefano, Scuderi Giuseppe, Ruisi Paolo, Villa Paolo, Stroppiana Daniela, Boschetti Mirco, Goulart Luiz R., Davis Cristina E., Dandekar Abhaya M., Advanced methods of plant disease detection. A review, Agronomy for Sustainable Development, 35, 1, 2015. Crossref

  5. Banerjee Soumya Jyoti, Azharuddin Mohammad, Sen Debanjan, Savale Smruti, Datta Himadri, Dasgupta Anjan Kr, Roy Soumen, Using complex networks towards information retrieval and diagnostics in multidimensional imaging, Scientific Reports, 5, 1, 2015. Crossref

  6. Martinelli Federico, Ibanez Ana Maria, Reagan Russell L., Davino Salvatore, Dandekar Abhaya M., Stress responses in citrus peel: Comparative analysis of host responses to Huanglongbing disease and puffing disorder, Scientia Horticulturae, 192, 2015. Crossref

  7. Giovino Antonio, Bertolini Edoardo, Fileccia Veronica, Al Hassan Mohamad, Labra Massimo, Martinelli Federico, Transcriptome analysis of Phoenix canariensis Chabaud in response to Rhynchophorus ferrugineus Olivier attacks, Frontiers in Plant Science, 6, 2015. Crossref

  8. Bue Brian D., Thompson David R., Sellar R. Glenn, Podest Erika V., Eastwood Michael L., Helmlinger Mark C., McCubbin Ian B., Morgan John D., Leveraging in-scene spectra for vegetation species discrimination with MESMA-MDA, ISPRS Journal of Photogrammetry and Remote Sensing, 108, 2015. Crossref

  9. Chakraborty Sandeep, Britton Monica, Martínez-García P. J., Dandekar Abhaya M., Deep RNA-Seq profile reveals biodiversity, plant–microbe interactions and a large family of NBS-LRR resistance genes in walnut (Juglans regia) tissues, AMB Express, 6, 1, 2016. Crossref

  10. Martinelli Federico, Dolan David, Fileccia Veronica, Reagan Russell L., Phu My, Spann Timothy M., McCollum Thomas G., Dandekar Abhaya M., Wang Zonghua, Molecular Responses to Small Regulating Molecules against Huanglongbing Disease, PLOS ONE, 11, 7, 2016. Crossref

  11. Martinelli Federico, Reagan Russell L., Dolan David, Fileccia Veronica, Dandekar Abhaya M., Proteomic analysis highlights the role of detoxification pathways in increased tolerance to Huanglongbing disease, BMC Plant Biology, 16, 1, 2016. Crossref

  12. Balan Bipin, Caruso Tiziano, Martinelli Federico, Gaining Insight into Exclusive and Common Transcriptomic Features Linked with Biotic Stress Responses in Malus, Frontiers in Plant Science, 8, 2017. Crossref

  13. Martinelli Federico, Dandekar Abhaya M., Genetic Mechanisms of the Devious Intruder Candidatus Liberibacter in Citrus, Frontiers in Plant Science, 8, 2017. Crossref

  14. Yadav Sunita, Chhibbar Anju K., Plant–Virus Interactions, in Molecular Aspects of Plant-Pathogen Interaction, 2018. Crossref

  15. Verde Gabriella Lo, Fileccia Veronica, Bue Paolo Lo, Peri Ezio, Colazza Stefano, Martinelli Federico, Members of the WRKY gene family are upregulated in Canary palms attacked by Red Palm Weevil, Arthropod-Plant Interactions, 13, 1, 2019. Crossref

  16. Balan Bipin, Ibáñez Ana M., Dandekar Abhaya M., Caruso Tiziano, Martinelli Federico, Identifying Host Molecular Features Strongly Linked With Responses to Huanglongbing Disease in Citrus Leaves, Frontiers in Plant Science, 9, 2018. Crossref

  17. Balan Bipin, Marra Francesco Paolo, Caruso Tiziano, Martinelli Federico, Transcriptomic responses to biotic stresses in Malus x domestica: a meta-analysis study, Scientific Reports, 8, 1, 2018. Crossref

  18. Martinelli Federico, Marchese Annalisa, Giovino Antonio, Marra Francesco Paolo, Della Noce Isabella, Caruso Tiziano, Dandekar Abhaya M., In-Field and Early Detection of Xylella fastidiosa Infections in Olive Using a Portable Instrument, Frontiers in Plant Science, 9, 2019. Crossref

  19. Martinelli Federico, Perrone Anna, Della Noce Isabella, Colombo Lorenzo, Lo Priore Stefano, Romano Simone, Application of a portable instrument for rapid and reliable detection of SARS‐CoV‐2 infection in any environment, Immunological Reviews, 295, s1, 2020. Crossref

  20. Indrakumari R., Poongodi T., Khaitan Supriya, Sagar Shrddha, Balamurugan B., A review on plant diseases recognition through deep learning, in Handbook of Deep Learning in Biomedical Engineering, 2021. Crossref

  21. Nehela Yasser, Killiny Nabil, Revisiting the Complex Pathosystem of Huanglongbing: Deciphering the Role of Citrus Metabolites in Symptom Development, Metabolites, 10, 10, 2020. Crossref

  22. Aksenov Alexander A., Novillo Ana V. Guaman, Sankaran Sindhuja, Fung Alexander G., Pasamontes Alberto, Martinelli Frederico, Cheung William H. K., Ehsani Reza, Dandekar Abhaya M., Davis Cristina E., Volatile Organic Compounds (VOCs) for Noninvasive Plant Diagnostics, in Pest Management with Natural Products, 1141, 2013. Crossref

  23. Kumar Vinay, Sharma Vinukonda Rakesh, Patel Himani, Dinkar Nisha, An Insight into Current Trends of Pathogen Identification in Plants, in Phytobiomes: Current Insights and Future Vistas, 2020. Crossref

  24. Vergata Chiara, Yousefi Sanaz, Buti Matteo, Vestrucci Federica, Gholami Mansour, Sarikhani Hassan, Salami Seyed Alireza, Martinelli Federico, Roberts Thomas, Meta-analysis of transcriptomic responses to cold stress in plants, Functional Plant Biology, 49, 8, 2022. Crossref

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