Untangling the transcriptome from fungus-infected plant tissues

Area of Science:

• Mycology
• Plant Science
• Bioinformatics

Background:

• Advancements in sequencing technology generate vast amounts of biological data.
• Analyzing large datasets, such as mixed plant-fungal samples, presents significant challenges.
• Accurate identification of sequence origins is essential for understanding host-pathogen interactions.

Purpose of the Study:

• To develop and validate an effective strategy for distinguishing plant and fungal sequences in mixed samples.
• To accurately determine the origin of transcripts from a mixed poplar and Marssonina brunnea sample.
• To provide a foundation for further transcriptome analysis of mixed biological samples.

Main Methods:

• Combining three distinct methods for sequence origin identification: taxonomic information of homologous sequences, reference genome alignment, and comparative transcriptome analysis.
• Utilizing specific libraries: Library 895 (poplar), Library M6 (M. brunnea), and Library 895-M6 (mixed sample).
• Employing Illumina/Solexa GA IIx sequencing technology.

Main Results:

• Successfully identified the origin of 80,978 contigs (99.5%) in the mixed poplar and Marssonina brunnea sample (Library 895-M6).
• Demonstrated high accuracy in distinguishing between plant (poplar) and fungal (M. brunnea) sequences.
• Validated the integrated approach as a robust method for mixed sample analysis.

Conclusions:

• The integrated three-method strategy is highly effective for determining sequence origins in mixed biological pools.
• This approach facilitates accurate transcriptome analysis of complex host-pathogen interactions.
• The findings support further research into the molecular mechanisms of plant-fungal infections.