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Genetic underpinnings of host-pathogen interactions in the parasitic plantStriga hermonthica,a root parasitic plant that ravages cereals in sub-Saharan Africa, are unclear. We performed a comparative transcriptome study on five genotypes of sorghum exhibiting diverse resistance responses toS. hermonthicausing weighted gene co-expression network analysis (WGCNA). We found thatS. hermonthicaelicits both basal and effector-triggered immunity – like a bona fide pathogen. Resistance response was genotype-specific. Some resistance responses followed the salicylic acid-dependent signaling pathway for systemic acquired resistance characterized by cell wall reinforcements, lignification and callose deposition while in others the WRKY-dependent signaling pathway was activated leading to a hypersensitive response (HR). In some genotypes, both modes of resistance were activated while in others, either mode dominated the resistance response. Cell-wall-based resistance was common to all sorghum genotypes but strongest in IS2814, while HR-based response was specific to N13, IS9830 and IS41724. WGCNA further allowed for pinpointing ofS. hermonthicaresistance causative genes in sorghum. Some highlights include a Glucan synthase-like 10, a pathogenesis-related thaumatin-like family, and a phosphoinositide phosphatase gene. Such candidate genes will form a good basis for subsequent functional validation and possibly future resistance breeding.HighlightParasitic plants of theStrigagenus are major pests to cereals in Africa. We pinpointed genetic causes ofStrigaresistance in sorghum that can be harnessed for future resistance breeding.

Background: DNA methylation, the most common epigenetic modification, is defined as the removal or addition of methyl groups to cytosine bases. Studying DNA methylation provides insight into the regulation of gene expression, transposon mobility, genomic stability, and genomic imprinting. Whole-genome DNA methylation profiling (WGDM) is a powerful tool to find DNA methylation. This technique combines standard whole-genome sequencing methodology (e.g., Illumina high-throughput sequencing) with additional steps where unmethylated cytosine is converted to uracil. However, factors such as low cytosine conversion efficiency and inadequate DNA recovery during sample preparation oftentimes render poor-quality data. It is therefore imperative to benchmark sample preparation protocols to increase sequencing data quality and reduce false positives in methylation detection. Methods: A survey analysis was performed to investigate the efficiency of the following commercially available cytosine conversion kits when coupled with the NEBNext® Ultra™ DNA Library Prep Kit for Illumina (NEB): Zymo Research EZ DNA Methylation™ kit (hereafter known as Zymo Conversion kit), QIAGEN EpiTect Bisulfite kit (hereafter known as QIAGEN Conversion kit), and NEBNext® Enzymatic Methyl-seq Conversion Module(hereafter known as NEB EM-seq kit). Input DNA was derived from soybean (Glycine max [L.] Merrill) leaf tissue. Results: Of those tested, the QIAGEN Conversion kit provided the best sample recovery and the highest number of sequencing reads, whereas the Zymo Conversion kit had the best cytosine conversion efficiency and the least duplication. The sequence library obtained with the NEB EM-seq kit had the highest mapping efficiency (percentage of reads mapped to the genome). The data quality (defined by Phred score) and methylated cytosine call were similar between kits. Conclusions: This study offers the groundwork for selecting an effective DNA methylation detection kit for crop genome research.

Background: DNA methylation, the most common epigenetic modification, is defined as the removal or addition of methyl groups to cytosine bases. Studying DNA methylation provides insight into the regulation of gene expression, transposon mobility, genomic stability, and genomic imprinting. Whole-genome DNA methylation profiling (WGDM) is a powerful tool to find DNA methylation. This technique combines standard whole-genome sequencing methodology (e.g., Illumina high-throughput sequencing) with additional steps where unmethylated cytosine is converted to uracil. However, factors such as low cytosine conversion efficiency and inadequate DNA recovery during sample preparation oftentimes render poor-quality data. It is therefore imperative to benchmark sample preparation protocols to increase sequencing data quality and reduce false positives in methylation detection. Methods: A survey analysis was performed to investigate the efficiency of the following commercially available cytosine conversion kits when coupled with the NEBNext® Ultra™ DNA Library Prep Kit for Illumina (NEB): Zymo Research EZ DNA Methylation™ kit (hereafter known as Zymo Conversion kit), QIAGEN EpiTect Bisulfite kit (hereafter known as QIAGEN Conversion kit), and NEBNext® Enzymatic Methyl-seq Conversion Module (hereafter known as NEB EM-seq kit). Input DNA was derived from soybean (Glycine max [L.] Merrill) leaf tissue. Results: Of those tested, the QIAGEN Conversion kit provided the best sample recovery and the highest number of sequencing reads, whereas the Zymo Conversion kit had the best cytosine conversion efficiency and the least duplication. The sequence library obtained with the NEB EM-seq kit had the highest mapping efficiency (percentage of reads mapped to the genome). The data quality (defined by Phred score) and methylated cytosine call were similar between kits. Conclusions: This study offers the groundwork for selecting an effective DNA methylation detection kit for crop genome research.