Optimizing mouse metatranscriptome profiling by selective removal of redundant nucleic acid sequences

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Metatranscriptome (MetaT) sequencing profiles microbiome metabolic functions using gene expression data.
• High rRNA abundance (up to 99%) in microbiome samples impedes accurate mRNA analysis.
• Existing rRNA depletion probe designs, often taxonomy-based, can be costly and technically challenging.

Purpose of the Study:

• To develop and validate an efficient and consistent rRNA depletion strategy for mouse cecal samples for MetaT analysis.
• To refine a taxonomically neutral probe design method for improved rRNA removal.
• To reduce the cost and potential bias associated with rRNA depletion in microbiome research.

Main Methods:

• Design of rRNA depletion probes based on sequence abundance, agnostic of specific microbial species.
• Adaptation and refinement of probe design specifically for mouse cecal content.
• Evaluation of probe efficiency and consistency in rRNA removal for MetaT analysis.

Main Results:

• Human-specific rRNA depletion probes showed limited effectiveness in mouse cecal samples.
• Customized rRNA depletion probes for mouse cecal content demonstrated improved efficiency and consistency.
• The refined method successfully increased the yield of mRNA-rich sequencing reads.

Conclusions:

• Taxonomically neutral rRNA depletion probe design, tailored to specific sample types like mouse cecum, is crucial for effective MetaT analysis.
• This approach enhances the reliability and cost-effectiveness of microbiome functional profiling.
• The developed method provides a significant improvement for studying microbiome gene expression in mouse models.