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Even with a single data point, we were able to identify both candidate host defense genes and pathogen virulence genes that are highly expressed during infection. We applied this pipeline to metatranscriptomic sequencing data of sweet basil (Ocimum basilicum) and its obligate downy mildew parasite Peronospora belbahrii, both lacking a sequenced genome. This pipeline facilitates rapid gene discovery and expression analysis of a plant host and its obligate parasite simultaneously by next generation sequencing of mixed host and pathogen RNA (i.e.

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Here we have developed a data analysis pipeline integrating several bioinformatic software programs.

These obligate parasites are typically unculturable in the laboratory, posing technical challenges to characterize them at the genetic and genomic level. Our work indicates that the metabolic environment of the microbiome plays a role in the response of this community to antibiotics.Ĭurrent and emerging plant diseases caused by obligate parasitic microbes such as rusts, downy mildews, and powdery mildews threaten worldwide crop production and food safety. thetaiotaomicron, as well as the extent of microbiome disruption with amoxicillin. Accordingly, we observed that dietary composition affected the abundance and expansion of B. In vitro, we found that the sensitivity of this bacterium to amoxicillin was elevated by glucose and reduced by polysaccharides. Notably, Bacteroides thetaiotaomicron, which blooms in response to amoxicillin, upregulated polysaccharide utilization. We found that antibiotics significantly alter the expression of key metabolic pathways at the whole-community and single-species levels. We applied a metagenomic and metatranscriptomic approach to link antibiotic-induced taxonomic and transcriptional responses within the murine microbiome. Bacterial metabolism is an important regulator of susceptibility in vitro and likely plays a large role within the host. All bioinformatic tools used to build this protocol are publically available, and we strove to make them accessible to researchers with limited computational skills and applicable to metatranscriptomic data analysis in a wide range of plant–fungal interactions.Īlthough antibiotics disturb the structure of the gut microbiota, factors that modulate these perturbations are poorly understood. In summary, this protocol captures the fungal-plant interactions at the transcriptional level and facilitates rapid gene discovery and expression analysis using next-generation sequencing data of mixed host and pathogen transcripts (i.e., metatranscriptomics).

We have also presented visualization methods to generate high quality figures as well as data mining strategies for identifying candidate genes/pathways involved in host immunity and pathogen virulence. We have included two transcriptome analysis approaches, one reference-guided and the other de novo assembly-based, and discuss pros and cons for each method. This chapter describes a computational protocol to manage, analyze and interpret RNA-Seq data. RNA sequencing (or RNA-Seq) technology enables a global characterization and quantification of all transcripts of organisms from which RNA can be obtained, and it is particularly useful in identifying pathogen virulence factors involved in disease development and host immunity involved in the development of resistance. Understanding the mechanism of host–pathogen interaction is essential for the development of novel diagnostic methods and disease management strategies. Plant diseases cause significant losses to agricultural production and pose serious threats to food security worldwide.

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