Identification of novel soybean genes involved in host-pathogen interaction between soybean and SCN
Principal Investigator: Bahram Samanfar
Research Institution: Agriculture and Agri-Food Canada (AAFC)
Timeline: April 2019 – March 2022
- To investigate host-pathogen interaction between soybean and soybean cyst nematode (SCN) by running the Protein-protein Interaction Prediction Engine (PIPE) software to obtain a list of soybean-SCN interacting protein pairs using a computational approach which investigates all soybean proteins against all SCN proteins.
- To validate predicted protein interactions using molecular biology related approaches.
- To investigate sequence databases (SNP, re-sequencing) for nucleotide variations (allelic variations) for the candidate genes predicted by PIPE in soybean. Variations at these target genes might have effects on the protein interactome (to be re-investigated by PIPE) and may identify new SCN resistance genes.
- To perform greenhouse SCN resistance screening of candidate soybean lines having genetic variations at the target genes identified by PIPE.
- To develop allele-specific markers for the candidate genes as potential sources of resistance to SCN which will allow for marker assisted selection in breeding programs.
- There is a gap of knowledge in understanding the interaction between soybean and SCN. This computational approach will shed light into this complicated pathway and will identify new interactions between soybean and SCN.
- For any identified gene in soybean interacting with SCN, we will investigate the variation for that specific gene and use it as a potential new resistance source, which could have application in Ontario adapted SCN-resistant line development.
- For a given gene in soybean that interacts with virulence genes from SCN, another option could be the application of gene knock-out (i.e., CRISPR) or knock-down (i.e., RNAi) to generate a new SCN resistance source.
- Results from this proposal will be used by soybean breeders for marker-assisted selection to generate Ontario-adapted SCN-resistant lines which will allow Ontario farmers to perform farming activities more economically.
Soybean cyst nematode, SCN (Heterodera glycines), was first found in Ontario in 1988 (southwestern regions) and continues to spread to shorter season areas. The problem is once SCN is present in a field, eradication is impossible. Management options are resistant lines, crop rotation and field management. Yield loss could vary between 5-80%. There are limited sources of resistant genes used in soybean and in some areas with long exposure to SCN we have started to see resistance breakdown.
To identify more SCN resistance genes, we proposed to develop and use a bioinformatics tool to investigate host-pathogen interactions between soybean and SCN using a protein-protein interaction approach. The Protein-protein Interaction Prediction Engine (PIPE) is a computational tool developed by our collaborators at Carleton University and is used to predict protein-protein interactions (PPI). PIPE has been used to produce proteome-wide, all-to-all predicted interactomes in a variety of organisms including yeast, human, soybean, Arabidopsis, and others. In this way, the investigation of global PPI of SCN will give us a better understanding regarding functional biology of this nematode; and more importantly, we will have the opportunity to investigate host-pathogen interactions between soybean and SCN in a comprehensive manner. This will lead us to the identification of novel target genes either in soybean or SCN which will provide a better understanding of the virulence process of SCN and will provide novel candidates for new SCN resistance genes.
We incorporated a bioinformatics pipeline to finish the major step of genome-wide analysis. Within the last year, different -omics approaches such as genomics (single nucleotide polymorphism (SNP), gene annotations, loss of function (LOF) mutations, genome-wide protein-protein interaction, PIPE) were used to create a short-list of novel genes along with their allelic variations (SNP, InDel, LOF) which are most likely involved in host-pathogen interactions (i.e., soybean-SCN interactions). The first part of the project required developing a pipeline, tested first with about ~250 genes, which was then used for the larger part of the project, the genome wide approach (over 50,000 genes). The pipeline development was completed in early 2021 allowing us to proceed into the genome-wide part of the computational analysis throughout the rest of the year. The genome-wide project was expected to be very time consuming and labor intensive to complete due to the large amounts of files required to analyse independently (over ~350,000 files). This was, however, fast-tracked when the coding program Python was utilized, and a script was developed to analyze the -omics data. This led to the completion of the raw genome wide runs (of over ~350,000 files) in months instead of the previously expected period. Currently, the raw genome-wide runs are being organized into different priority lists. Organizing the genome-wide files into lists of the most likely gene candidates to the least likely candidates based on our pipeline will allow us to select ideal candidates with higher efficiency. The changes in the protein interactome atlas of SCN-soybean, considering the allelic forms of the selected candidate genes, have been investigated (PIPE) and taken into consideration during the genome-wide analysis. A number of lines have been investigated for their resistance to SCN, potentially carrying the variation for the candidate genes identified by our multi-omics approach (i.e., PI89772, PI437654, PI567295, PI603588, PI341241A, PI549019, PI561310, …). At this point, there are nine candidates which have passed all criteria for being high confidence candidate genes potentially involved in host-pathogen interactions (as well as sixteen additional candidate genes with supporting data indicating their potential involvement in host-pathogen interactions). Allele-specific markers have been developed for these nine candidates; however, further investigations are required to mechanistically characterize the involvement of these genes in response to SCN.
To further expand our analytical approaches and to further collaborate with soybean breeders, we adopted four different KASP (Kompetitive Allele Specific PCR) markers for Rhg1 and Rhg4 (allelic variations and copy numbers). As of today, more than ~200 lines and cultivars (different than those genotyped in 2020-2021) have been genotyped for Rhg1 and Rhg4. We also developed and tested an alternative marker for another source of resistance coming from a cultivar called “Peking”. Finally, we finished genotyping six soybean populations (X6504-X6509, 30 lines per population) developed by AAFC soybean breeder, Dr. Cober, using our KASP marks for Rhg1 and Rhg4 (speed breeding for SCN resistant lines) that we started 2020-2021.
External Funding Partners:
This project is funded in part by the Canadian Agricultural Partnership, a five-year, $3 billion investment by federal-provincial and territorial governments.
Project Related Publications:
Nissan N, Mimee B, Cober ER, GolshaniA, Smith M, and Samanfar B. 2022. A Broad Review of Soybean Research on the Ongoing Race to Overcome Soybean Cyst Nematode. Biology. 11(2):211.
Dick K, Samanfar B, Barnes B, Cober E, Mimee B, Tan LT, Molnar SJ, Biggar K, Golshani A, Dehne F, and Green JR. 2020. PIPE4: Ultra-Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes. Scientific Report. 10(1):1390.