Enhancing inference of differential gene expression in metatranscriptomes from human microbial communities.

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Metatranscriptomic (MTX) sequencing measures microbial gene expression, assessing functional activity.
• Current MTX analysis lacks standardized practices, relying on simulations for benchmarking.
• Real-world data analysis faces challenges like low abundance, prevalence, and compositional effects.

Purpose of the Study:

• To quantitatively assess current MTX analysis methods using mock communities.
• To evaluate method performance on real MTX datasets and identify robust approaches.
• To develop strategies for accurate differential expression inference in complex microbiomes.

Main Methods:

• Utilized mock microbial communities to benchmark differential expression analysis tools.
• Applied selected MTX analysis methods to datasets from gnotobiotic mice and human clinical studies.
• Integrated metagenome-assembled genomes to refine analysis by excluding low-information samples.

Main Results:

• No single MTX analysis method demonstrated robustness across all tested confounders.
• Simulated data performance did not accurately predict real-world MTX data analysis outcomes.
• A validated method successfully inferred microbial cross-feeding dynamics in vivo.

Conclusions:

• Benchmarking MTX analysis on real datasets is essential for method selection and implementation.
• This approach enables reliable inference and validation of microbial interactions and metabolic strategies.
• Refined analysis strategies improve differential expression inference in complex microbial ecosystems.