Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides
<p dir="ltr">Maize, a crop of global significance, is vulnerable to a variety of biotic stresses resulting in economic losses. Fusarium verticillioides (teleomorph Gibberella moniliformis) is one of the key fungal pathogens of maize, causing ear rots and stalk rots. To better underst...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2015
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| _version_ | 1864513505716076544 |
|---|---|
| author | Mansuck Kim (19570903) |
| author2 | Huan Zhang (220065) Charles Woloshuk (435330) Won-Bo Shim (133931) Byung-Jun Yoon (142313) |
| author2_role | author author author author |
| author_facet | Mansuck Kim (19570903) Huan Zhang (220065) Charles Woloshuk (435330) Won-Bo Shim (133931) Byung-Jun Yoon (142313) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mansuck Kim (19570903) Huan Zhang (220065) Charles Woloshuk (435330) Won-Bo Shim (133931) Byung-Jun Yoon (142313) |
| dc.date.none.fl_str_mv | 2015-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/1471-2105-16-s13-s12 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Computational_identification_of_genetic_subnetwork_modules_associated_with_maize_defense_response_to_Fusarium_verticillioides/26977189 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Crop and pasture production Biological sciences Biochemistry and cell biology Bioinformatics and computational biology Plant biology Maize Fusarium verticillioides host-pathogen interaction network-based analysis subnetwork module identification |
| dc.title.none.fl_str_mv | Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Maize, a crop of global significance, is vulnerable to a variety of biotic stresses resulting in economic losses. Fusarium verticillioides (teleomorph Gibberella moniliformis) is one of the key fungal pathogens of maize, causing ear rots and stalk rots. To better understand the genetic mechanisms involved in maize defense as well as F. verticillioides virulence, a systematic investigation of the host-pathogen interaction is needed. The aim of this study was to computationally identify potential maize subnetwork modules associated with its defense response against F. verticillioides. We obtained time-course RNA-seq data from B73 maize inoculated with wild type F. verticillioides and a loss-of-virulence mutant, and subsequently established a computational pipeline for network-based comparative analysis. Specifically, we first analyzed the RNA-seq data by a cointegration-correlation-expression approach, where maize genes were jointly analyzed with known F. verticillioides virulence genes to find candidate maize genes likely associated with the defense mechanism. We predicted maize co-expression networks around the selected maize candidate genes based on partial correlation, and subsequently searched for subnetwork modules that were differentially activated when inoculated with two different fungal strains. Based on our analysis pipeline, we identified four potential maize defense subnetwork modules. Two were directly associated with maize defense response and were associated with significant GO terms such as GO:0009817 (defense response to fungus) and GO:0009620 (response to fungus). The other two predicted modules were indirectly involved in the defense response, where the most significant GO terms associated with these modules were GO:0046914 (transition metal ion binding) and GO:0046686 (response to cadmium ion). Through our RNA-seq data analysis, we have shown that a network-based approach can enhance our understanding of the complicated host-pathogen interactions between maize and F. verticillioides by interpreting the transcriptome data in a system-oriented manner. We expect that the proposed analytic pipeline can also be adapted for investigating potential functional modules associated with host defense response in diverse plant-pathogen interactions.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Bioinformatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="license" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/1471-2105-16-s13-s12" target="_blank">https://dx.doi.org/10.1186/1471-2105-16-s13-s12</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_234cdcea6aa457bb72a32e9b92c439ce |
| identifier_str_mv | 10.1186/1471-2105-16-s13-s12 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26977189 |
| publishDate | 2015 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioidesMansuck Kim (19570903)Huan Zhang (220065)Charles Woloshuk (435330)Won-Bo Shim (133931)Byung-Jun Yoon (142313)Agricultural, veterinary and food sciencesCrop and pasture productionBiological sciencesBiochemistry and cell biologyBioinformatics and computational biologyPlant biologyMaizeFusarium verticillioideshost-pathogen interactionnetwork-based analysissubnetwork module identification<p dir="ltr">Maize, a crop of global significance, is vulnerable to a variety of biotic stresses resulting in economic losses. Fusarium verticillioides (teleomorph Gibberella moniliformis) is one of the key fungal pathogens of maize, causing ear rots and stalk rots. To better understand the genetic mechanisms involved in maize defense as well as F. verticillioides virulence, a systematic investigation of the host-pathogen interaction is needed. The aim of this study was to computationally identify potential maize subnetwork modules associated with its defense response against F. verticillioides. We obtained time-course RNA-seq data from B73 maize inoculated with wild type F. verticillioides and a loss-of-virulence mutant, and subsequently established a computational pipeline for network-based comparative analysis. Specifically, we first analyzed the RNA-seq data by a cointegration-correlation-expression approach, where maize genes were jointly analyzed with known F. verticillioides virulence genes to find candidate maize genes likely associated with the defense mechanism. We predicted maize co-expression networks around the selected maize candidate genes based on partial correlation, and subsequently searched for subnetwork modules that were differentially activated when inoculated with two different fungal strains. Based on our analysis pipeline, we identified four potential maize defense subnetwork modules. Two were directly associated with maize defense response and were associated with significant GO terms such as GO:0009817 (defense response to fungus) and GO:0009620 (response to fungus). The other two predicted modules were indirectly involved in the defense response, where the most significant GO terms associated with these modules were GO:0046914 (transition metal ion binding) and GO:0046686 (response to cadmium ion). Through our RNA-seq data analysis, we have shown that a network-based approach can enhance our understanding of the complicated host-pathogen interactions between maize and F. verticillioides by interpreting the transcriptome data in a system-oriented manner. We expect that the proposed analytic pipeline can also be adapted for investigating potential functional modules associated with host defense response in diverse plant-pathogen interactions.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Bioinformatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="license" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/1471-2105-16-s13-s12" target="_blank">https://dx.doi.org/10.1186/1471-2105-16-s13-s12</a></p>2015-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/1471-2105-16-s13-s12https://figshare.com/articles/journal_contribution/Computational_identification_of_genetic_subnetwork_modules_associated_with_maize_defense_response_to_Fusarium_verticillioides/26977189CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/269771892015-12-01T00:00:00Z |
| spellingShingle | Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides Mansuck Kim (19570903) Agricultural, veterinary and food sciences Crop and pasture production Biological sciences Biochemistry and cell biology Bioinformatics and computational biology Plant biology Maize Fusarium verticillioides host-pathogen interaction network-based analysis subnetwork module identification |
| status_str | publishedVersion |
| title | Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides |
| title_full | Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides |
| title_fullStr | Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides |
| title_full_unstemmed | Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides |
| title_short | Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides |
| title_sort | Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides |
| topic | Agricultural, veterinary and food sciences Crop and pasture production Biological sciences Biochemistry and cell biology Bioinformatics and computational biology Plant biology Maize Fusarium verticillioides host-pathogen interaction network-based analysis subnetwork module identification |