» Articles » PMID: 24454689

The Genome Sequence of the Fungal Pathogen Fusarium Virguliforme That Causes Sudden Death Syndrome in Soybean

Overview
Journal PLoS One
Date 2014 Jan 24
PMID 24454689
Citations 23
Authors
Affiliations
Soon will be listed here.
Abstract

Unlabelled: Fusarium virguliforme causes sudden death syndrome (SDS) of soybean, a disease of serious concern throughout most of the soybean producing regions of the world. Despite the global importance, little is known about the pathogenesis mechanisms of F. virguliforme. Thus, we applied Next-Generation DNA Sequencing to reveal the draft F. virguliforme genome sequence and identified putative pathogenicity genes to facilitate discovering the mechanisms used by the pathogen to cause this disease.

Methodology/principal Findings: We have generated the draft genome sequence of F. virguliforme by conducting whole-genome shotgun sequencing on a 454 GS-FLX Titanium sequencer. Initially, single-end reads of a 400-bp shotgun library were assembled using the PCAP program. Paired end sequences from 3 and 20 Kb DNA fragments and approximately 100 Kb inserts of 1,400 BAC clones were used to generate the assembled genome. The assembled genome sequence was 51 Mb. The N50 scaffold number was 11 with an N50 Scaffold length of 1,263 Kb. The AUGUSTUS gene prediction program predicted 14,845 putative genes, which were annotated with Pfam and GO databases. Gene distributions were uniform in all but one of the major scaffolds. Phylogenic analyses revealed that F. virguliforme was closely related to the pea pathogen, Nectria haematococca. Of the 14,845 F. virguliforme genes, 11,043 were conserved among five Fusarium species: F. virguliforme, F. graminearum, F. verticillioides, F. oxysporum and N. haematococca; and 1,332 F. virguliforme-specific genes, which may include pathogenicity genes. Additionally, searches for candidate F. virguliforme pathogenicity genes using gene sequences of the pathogen-host interaction database identified 358 genes.

Conclusions: The F. virguliforme genome sequence and putative pathogenicity genes presented here will facilitate identification of pathogenicity mechanisms involved in SDS development. Together, these resources will expedite our efforts towards discovering pathogenicity mechanisms in F. virguliforme. This will ultimately lead to improvement of SDS resistance in soybean.

Citing Articles

Genomic Differences Between Two Formae Speciales Causing Root Rot in Cucumber.

Komissarov E, Diabankana R, Abdeeva I, Afordoanyi D, Gudkov S, Dvorianinova E J Fungi (Basel). 2025; 11(2).

PMID: 39997434 PMC: 11856433. DOI: 10.3390/jof11020140.


Soybean genomics research community strategic plan: A vision for 2024-2028.

Stupar R, Locke A, Allen D, Stacey M, Ma J, Weiss J Plant Genome. 2024; 17(4):e20516.

PMID: 39572930 PMC: 11628913. DOI: 10.1002/tpg2.20516.


Identification and molecular detection of the pathogen of leaf yellowing through genome analysis.

Tsao W, Li Y, Tu Y, Nai Y, Lin T, Wang C Front Microbiol. 2024; 15:1431813.

PMID: 39403082 PMC: 11472846. DOI: 10.3389/fmicb.2024.1431813.


Chromosomal scale assembly reveals localized structural variants in avian caecal coccidian parasite Eimeria tenella.

Srivastava S, Parker C, OBrien C, Tucker M, Thompson P, Rosenthal B Sci Rep. 2023; 13(1):22802.

PMID: 38129566 PMC: 10739835. DOI: 10.1038/s41598-023-50117-0.


Potato dry rot disease: current status, pathogenomics and management.

Tiwari R, Kumar R, Sharma S, Sagar V, Aggarwal R, Naga K 3 Biotech. 2020; 10(11):503.

PMID: 33163322 PMC: 7609731. DOI: 10.1007/s13205-020-02496-8.


References
1.
Galagan J, Calvo S, Cuomo C, Ma L, Wortman J, Batzoglou S . Sequencing of Aspergillus nidulans and comparative analysis with A. fumigatus and A. oryzae. Nature. 2005; 438(7071):1105-15. DOI: 10.1038/nature04341. View

2.
Kurtz S, Phillippy A, Delcher A, Smoot M, Shumway M, Antonescu C . Versatile and open software for comparing large genomes. Genome Biol. 2004; 5(2):R12. PMC: 395750. DOI: 10.1186/gb-2004-5-2-r12. View

3.
Bendtsen J, Nielsen H, von Heijne G, Brunak S . Improved prediction of signal peptides: SignalP 3.0. J Mol Biol. 2004; 340(4):783-95. DOI: 10.1016/j.jmb.2004.05.028. View

4.
Huang X, Ye L, Chou H, Yang I, Chao K . Efficient combination of multiple word models for improved sequence comparison. Bioinformatics. 2004; 20(16):2529-33. DOI: 10.1093/bioinformatics/bth279. View

5.
Stanke M, Waack S . Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003; 19 Suppl 2:ii215-25. DOI: 10.1093/bioinformatics/btg1080. View