Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics
Abstract
A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library’s ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 31, 2023
- Source ID
- 10.1038/s41467-023-37897-9
Entities
People
- Aqib Hasnain
- Dennis M. Joshy
- Enoch Yeung
- Jen Smith
- Shara Balakrishnan
- Steve Haase
Organizations
- Army Research Office
- Office of Biological and Environmental Research
- United States Department of Defense