PT  - JOURNAL ARTICLE
AU  - Aqib Hasnain
AU  - Shara Balakrishnan
AU  - Dennis M. Joshy
AU  - Jen Smith
AU  - Steven B. Haase
AU  - Enoch Yeung
TI  - Learning perturbation-inducible cell states of novel compounds from observability analysis of transcriptome dynamics
AID  - 10.1101/2022.05.27.493781
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.05.27.493781
4099  - http://biorxiv.org/content/early/2022/11/17/2022.05.27.493781.short
4100  - http://biorxiv.org/content/early/2022/11/17/2022.05.27.493781.full
AB  - 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 library of living malathion sensors 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.Competing Interest StatementThe authors have declared no competing interest.