Genomic Science Program
U.S. Department of Energy | Office of Science | Biological and Environmental Research Program

2024 Abstracts

Unveiling Molecular Interactions and Metabolic Contributions in Sorghum Anthracnose Defense: Towards the Integration of Fungal Pathogen and Host Sorghum Models


Janaka N. Edirisinghe1* ([email protected]), Claudia Lerma-Ortiz1, Clint Magill2, Sam Seaver1, Sara El Alaoui1, Filipe Liu1, José P. Faria1, Qun Liu3, Christopher Henry1


1Argonne National Laboratory; 2Texas A&M University–College Station; 3Brookhaven National Laboratory


To gain deeper insights into the virulence mechanisms of the fungus Colletotrichum sublineola in Anthracnose, it is essential to comprehend the pivotal metabolic interactions occurring between C. sublineola and its host organism, sorghum. Through the development of mechanistic models, researchers can delve into these crucial metabolic interactions, shedding light on the roles of both the fungus and the host sorghum in the process.


Anthracnose, a devastating disease in sorghum, is caused by the hemibiotrophic fungal pathogen Colletotrichum sublineola (Crouch and Tomaso-Peterson 2012). Sorghum’s nucleotide-binding leucine-rich repeat proteins (NLRs) play a pivotal role in recognizing virulence effectors of pathogens like C. sublineola (Cs), triggering effective immune responses vital for plant defense (Rausher 2001).  Nonetheless, the intricate molecular interactions governing Cs pathogenicity, anthracnose resistance, and susceptibility in sorghum remain poorly elucidated. Further, Cs metabolism during its appressoria and haustoria invasion stages is not well understood. To address these knowledge gaps, the overall goal within the wider context of the BRaVE project is to build a metabolic model of the interactions between sorghum and Cs. To this end, the team sequenced, assembled, and annotated a newly isolated Cs pathogen, followed by the reconstruction of a genome-scale metabolic model (GEM) around a well-curated core of central carbon metabolism, fermentation, electron transport chains, and energy biosynthesis. A comparative study was also performed between the new Cs genome and many existing nearby fungal strains.

Our GEM reconstruction builds upon extensive curation efforts to develop an improved fungal modeling template for KBase by consolidating and reconciling data from thirteen diverse published fungal models (Edirisinghe et al 2023). This integration involved harmonizing the biochemistry of each published model with the ModelSEED biochemistry database, effectively minimizing redundancy and inconsistency in biochemical pathways and the underlying protein annotations.

This approach is further supported by protein family data computed across the thirteen fungal species and a set of additional well-sequenced fungal strains mapped to pertinent biochemistry data. This system systematically captures the unique biochemistry of each model, ensuring consistency in annotation mapping with the relevant biochemical context.

The curated and reconciled pathways refined and consistently annotated protein families and enhanced fungal template model are bundled together within the readily accessible user-friendly Build Fungal Model application on the DOE Systems Biology Knowledgebase (KBase) platform ( The work also resulted in significant improvements to all the published fungal models that were reconciled in the efforts to build the fungal modeling tool. Additionally, researchers present a KBase narrative workflow illustrating the construction of the central carbon model for Cs, utilizing data from the newly sequenced Cs genome and other closely related Cs genomes.

Concurrently, for the plant side of the eventual host-pathogen mechanistic model, researchers applied the PlantSEED (Seaver et al 2018) approach to generate a high-quality metabolic model of sorghum using the latest set of gene models. Previously, the conservative approach of predicting orthologs using OrthoFinder meant researchers had a high rate of false negatives. Researchers adopted a new approach that takes into account the distribution of pairwise sequence identity for each protein family, allowing both orthologs and in-paralogs to be predicted as carrying the functions of plant primary metabolism, enabling researchers to assign sorghum enzymes to an additional 10% of plant primary metabolism.

We are actively applying the fungal and plant models independently to integrate and mechanistically interpret multi-omics datasets produced by other project team members, while the ultimate goal is to merge these models into a predictive dynamic host-pathogen interaction model.


Crouch, J. A., and Tomaso-Peterson, M. 2012. “Anthracnose Disease of Centipedegrass Turf Caused by Colletotrichum eremochloae, a New Fungal Species Closely Related to Colletotrichum sublineola,” Mycologia 104(5), 1085–96.

Edirisinghe, J. N., et al. 2023. “Consolidating Published Fungal Models: Strategies and Challenges in the Integration of Diverse Fungal Biochemistry for Multi-Omics Analyses,” Metabolic Pathway Analysis (MPA) International Conference. Seoul, Republic of Korea.

Rausher, M. D., 2001. “Co-Evolution and Plant Resistance to Natural Enemies,” Nature 411(6839), 857–64.

Seaver, S. M. D., et al. 2018. “PlantSEED Enables Automated Annotation and Reconstruction of Plant Primary Metabolism with Improved Compartmentalization and Comparative Consistency,” Plant Journal 95(6), 1102–13.

Funding Information

This work is supported as part of the BER BRAVE project and by KBase. KBase and BRAVE are funded by the U.S. DOE, Office of Science, BER Program under Award Numbers DE-AC02-06CH11357, and DE-AC02-98CH10886.