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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.

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Supplementary Text and Supplementary Figures: Figures S1 – S34.

Figure S1. Schematics of animal experiment indicating time of HIFM transplantation and time points of samples collection. Pigs were transplanted at 4 days of age and euthanized at 11Â days of age (dotted arrows). Intestinal tissues sampling was performed at PTD7. Abbreviations: HIFM-Human infant fecal microbiota; PTD-Post transplant days. (PDF 36 kb)

Figure S4. Impact of the diet on intestinal microbiota of HIFM+No HRV pigs. The relative abundance between deficient and sufficient diets for a given tissues is shown. Results are represented via a phylogenetic tree (Graphlan), combined with relative abundance data. Labeled in red are the bacteria detected in higher abundance in deficient diet; while in green are the bacteria detected in higher abundance in sufficient diet. The labeling of the taxonomic levels from the outside (phylum) to the inside (genus), while the tree start (root) from the center and goes outside. Nodes are indicated by a circle. Bacteria (node) more abundant in one of the diets is shown in red or green, no change is shown in gold. Bacteria are designated with alphabet in red or green corresponding to the node. (PDF 3708 kb)

Genes making up the turquoise module overrepresented GO categories coated membrane and membrane coat. (DOC 42Â kb)

Hierarchical clustering of co-expressed modules. (DOC 869Â kb)

Environmental variation due to landscape variation. (DOC 22Â kb)

Co-expression module gene counts. (DOC 43Â kb)

Raw, trimmed and mapped read counts for each of the 45 maize landrace RNA-seq libraries. (DOC 89Â kb)

Environmental parameters definitions. (DOC 23Â kb)

‘Hub’ genes of interest other than transcription factors and kinases [67–85]. (DOC 82 kb)

This is the open peer reviewers comments and recommendations regarding the submitted GigaScience article and/or dataset.

Thirty year precipitation (A) and evaporation (B) related environmental parameters of maize landrace origin. (DOC 514Â kb)

GO enrichment analysis for the turquoise (A) and yellow (B) co-expression modules. (DOC 41Â kb)

Figure S3. Impact of the diet on fecal microbiota of HIFM pigs before HRV challenge. The relative abundance between deficient and sufficient diets for a given time point is shown. Results are represented via a phylogenetic tree (Graphlan), combined with relative abundance data. Labeled in red are the bacteria detected in higher abundance in deficient diet; while in green are the bacteria detected in higher abundance in sufficient diet. The labeling of the taxonomic levels from the outside (phylum) to the inside (genus), while the tree start (root) from the center and goes outside. Nodes are indicated by a circle. Bacteria (node) more abundant in one of the diets is shown in red or green, no change is shown in gold. Bacteria are designated with alphabet in red or green corresponding to the node. (PDF 2487 kb)

Figure S5. Impact of the diet on systemic tissue microbiota of HIFM+No HRV pigs. The relative abundance between deficient and sufficient diets for a given tissues is shown. Results are represented via a phylogenetic tree (Graphlan), combined with relative abundance data. Labeled in red are the bacteria detected in higher abundance in deficient diet; while in green are the bacteria detected in higher abundance in sufficient diet. The labeling of the taxonomic levels from the outside (phylum) to the inside (genus), while the tree start (root) from the center and goes outside. Nodes are indicated by a circle. Bacteria (node) more abundant in one of the diets is shown in red or green, no change is shown in gold. Bacteria are designated with alphabet in red or green corresponding to the node. (PDF 1746 kb)

Figure S4. Impact of the diet on intestinal microbiota of HIFM+No HRV pigs. The relative abundance between deficient and sufficient diets for a given tissues is shown. Results are represented via a phylogenetic tree (Graphlan), combined with relative abundance data. Labeled in red are the bacteria detected in higher abundance in deficient diet; while in green are the bacteria detected in higher abundance in sufficient diet. The labeling of the taxonomic levels from the outside (phylum) to the inside (genus), while the tree start (root) from the center and goes outside. Nodes are indicated by a circle. Bacteria (node) more abundant in one of the diets is shown in red or green, no change is shown in gold. Bacteria are designated with alphabet in red or green corresponding to the node. (PDF 3708 kb)

Figure S3. Impact of the diet on fecal microbiota of HIFM pigs before HRV challenge. The relative abundance between deficient and sufficient diets for a given time point is shown. Results are represented via a phylogenetic tree (Graphlan), combined with relative abundance data. Labeled in red are the bacteria detected in higher abundance in deficient diet; while in green are the bacteria detected in higher abundance in sufficient diet. The labeling of the taxonomic levels from the outside (phylum) to the inside (genus), while the tree start (root) from the center and goes outside. Nodes are indicated by a circle. Bacteria (node) more abundant in one of the diets is shown in red or green, no change is shown in gold. Bacteria are designated with alphabet in red or green corresponding to the node. (PDF 2487 kb)

Figure S5. Impact of the diet on systemic tissue microbiota of HIFM+No HRV pigs. The relative abundance between deficient and sufficient diets for a given tissues is shown. Results are represented via a phylogenetic tree (Graphlan), combined with relative abundance data. Labeled in red are the bacteria detected in higher abundance in deficient diet; while in green are the bacteria detected in higher abundance in sufficient diet. The labeling of the taxonomic levels from the outside (phylum) to the inside (genus), while the tree start (root) from the center and goes outside. Nodes are indicated by a circle. Bacteria (node) more abundant in one of the diets is shown in red or green, no change is shown in gold. Bacteria are designated with alphabet in red or green corresponding to the node. (PDF 1746 kb)

Figure S1. Schematics of animal experiment indicating time of HIFM transplantation and time points of samples collection. Pigs were transplanted at 4 days of age and euthanized at 11Â days of age (dotted arrows). Intestinal tissues sampling was performed at PTD7. Abbreviations: HIFM-Human infant fecal microbiota; PTD-Post transplant days. (PDF 36 kb)