Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics

Area of Science:

• Biotechnology and Synthetic Biology
• Environmental Science
• Genomics and Bioinformatics

Background:

• Identifying reliable biomarkers for specific perturbations and metabolites is a significant hurdle in biotechnology and biomanufacturing.
• Transcriptome-wide analysis of gene expression dynamics is crucial for understanding cellular responses to external stimuli.

Purpose of the Study:

• To develop a data-driven method for discovering analyte-responsive promoters using transcriptome-wide time-series RNA sequencing data.
• To create a living biosensor for detecting the organophosphate malathion in environmental samples.
• To establish a machine learning framework applicable to various host organisms for discovering perturbation-inducible gene expression systems.

Main Methods:

• A transcriptome-wide approach was employed to rank perturbation-inducible genes from time-series RNA sequencing data.
• Low-dimensional models of gene expression dynamics were constructed, and genes were ranked using observability analysis to identify cell-state-capturing biomarkers.
• Synthetic genetic reporters were developed from 15 identified malathion-responsive promoters in Pseudomonas fluorescens SBW25.

Main Results:

• The study successfully identified 15 analyte-responsive promoters specific to malathion in Pseudomonas fluorescens SBW25.
• Synthetic genetic reporters demonstrated measurable responses to malathion, enabling its detection.
• A synthetic consortium approach enhanced malathion reporting, and the system was validated for environmental detection outside the laboratory.

Conclusions:

• The developed machine learning tool and identified promoters enable the creation of a living sensor for malathion detection.
• The engineered Pseudomonas fluorescens SBW25 serves as a promising platform for environmental diagnostics.
• The methodology is adaptable for discovering gene expression systems in diverse host organisms for various applications.