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Dynamic network inference and association computation discover gene modules regulating virulence, mycotoxin and sexual reproduction in Fusarium graminearum
BACKGROUND: The filamentous fungus Fusarium graminearum causes devastating crop diseases and produces harmful mycotoxins worldwide. Understanding the complex F. graminearum transcriptional regulatory networks (TRNs) is vital for effective disease management. Reconstructing F. graminearum dynamic TRN...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7041293/ https://www.ncbi.nlm.nih.gov/pubmed/32093656 http://dx.doi.org/10.1186/s12864-020-6596-y |
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author | Guo, Li Ji, Mengjie Ye, Kai |
author_facet | Guo, Li Ji, Mengjie Ye, Kai |
author_sort | Guo, Li |
collection | PubMed |
description | BACKGROUND: The filamentous fungus Fusarium graminearum causes devastating crop diseases and produces harmful mycotoxins worldwide. Understanding the complex F. graminearum transcriptional regulatory networks (TRNs) is vital for effective disease management. Reconstructing F. graminearum dynamic TRNs, an NP (non-deterministic polynomial) -hard problem, remains unsolved using commonly adopted reductionist or co-expression based approaches. Multi-omic data such as fungal genomic, transcriptomic data and phenomic data are vital to but so far have been largely isolated and untapped for unraveling phenotype-specific TRNs. RESULTS: Here for the first time, we harnessed these resources to infer global TRNs for F. graminearum using a Bayesian network based algorithm called “Module Networks”. The inferred TRNs contain 49 regulatory modules that show condition-specific gene regulation. Through a thorough validation based on prior biological knowledge including functional annotations and TF binding site enrichment, our network prediction displayed high accuracy and concordance with existing knowledge. One regulatory module was partially validated using network perturbations caused by Tri6 and Tri10 gene disruptions, as well as using Tri6 Chip-seq data. We then developed a novel computational method to calculate the associations between modules and phenotypes, and identified major module groups regulating different phenotypes. As a result, we identified TRN subnetworks responsible for F. graminearum virulence, sexual reproduction and mycotoxin production, pinpointing phenotype-associated modules and key regulators. Finally, we found a clear compartmentalization of TRN modules in core and lineage-specific genomic regions in F. graminearum, reflecting the evolution of the TRNs in fungal speciation. CONCLUSIONS: This system-level reconstruction of filamentous fungal TRNs provides novel insights into the intricate networks of gene regulation that underlie key processes in F. graminearum pathobiology and offers promise for the development of improved disease control strategies. |
format | Online Article Text |
id | pubmed-7041293 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70412932020-03-03 Dynamic network inference and association computation discover gene modules regulating virulence, mycotoxin and sexual reproduction in Fusarium graminearum Guo, Li Ji, Mengjie Ye, Kai BMC Genomics Research Article BACKGROUND: The filamentous fungus Fusarium graminearum causes devastating crop diseases and produces harmful mycotoxins worldwide. Understanding the complex F. graminearum transcriptional regulatory networks (TRNs) is vital for effective disease management. Reconstructing F. graminearum dynamic TRNs, an NP (non-deterministic polynomial) -hard problem, remains unsolved using commonly adopted reductionist or co-expression based approaches. Multi-omic data such as fungal genomic, transcriptomic data and phenomic data are vital to but so far have been largely isolated and untapped for unraveling phenotype-specific TRNs. RESULTS: Here for the first time, we harnessed these resources to infer global TRNs for F. graminearum using a Bayesian network based algorithm called “Module Networks”. The inferred TRNs contain 49 regulatory modules that show condition-specific gene regulation. Through a thorough validation based on prior biological knowledge including functional annotations and TF binding site enrichment, our network prediction displayed high accuracy and concordance with existing knowledge. One regulatory module was partially validated using network perturbations caused by Tri6 and Tri10 gene disruptions, as well as using Tri6 Chip-seq data. We then developed a novel computational method to calculate the associations between modules and phenotypes, and identified major module groups regulating different phenotypes. As a result, we identified TRN subnetworks responsible for F. graminearum virulence, sexual reproduction and mycotoxin production, pinpointing phenotype-associated modules and key regulators. Finally, we found a clear compartmentalization of TRN modules in core and lineage-specific genomic regions in F. graminearum, reflecting the evolution of the TRNs in fungal speciation. CONCLUSIONS: This system-level reconstruction of filamentous fungal TRNs provides novel insights into the intricate networks of gene regulation that underlie key processes in F. graminearum pathobiology and offers promise for the development of improved disease control strategies. BioMed Central 2020-02-24 /pmc/articles/PMC7041293/ /pubmed/32093656 http://dx.doi.org/10.1186/s12864-020-6596-y Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Guo, Li Ji, Mengjie Ye, Kai Dynamic network inference and association computation discover gene modules regulating virulence, mycotoxin and sexual reproduction in Fusarium graminearum |
title | Dynamic network inference and association computation discover gene modules regulating virulence, mycotoxin and sexual reproduction in Fusarium graminearum |
title_full | Dynamic network inference and association computation discover gene modules regulating virulence, mycotoxin and sexual reproduction in Fusarium graminearum |
title_fullStr | Dynamic network inference and association computation discover gene modules regulating virulence, mycotoxin and sexual reproduction in Fusarium graminearum |
title_full_unstemmed | Dynamic network inference and association computation discover gene modules regulating virulence, mycotoxin and sexual reproduction in Fusarium graminearum |
title_short | Dynamic network inference and association computation discover gene modules regulating virulence, mycotoxin and sexual reproduction in Fusarium graminearum |
title_sort | dynamic network inference and association computation discover gene modules regulating virulence, mycotoxin and sexual reproduction in fusarium graminearum |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7041293/ https://www.ncbi.nlm.nih.gov/pubmed/32093656 http://dx.doi.org/10.1186/s12864-020-6596-y |
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