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

Aqib Hasnain (), Shara Balakrishnan, Dennis M. Joshy, Jen Smith, Steven B. Haase and Enoch Yeung
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Aqib Hasnain: University of California Santa Barbara
Shara Balakrishnan: University of California Santa Barbara
Dennis M. Joshy: University of California Santa Barbara
Jen Smith: University of California Santa Barbara
Steven B. Haase: Duke University
Enoch Yeung: University of California Santa Barbara

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-37897-9

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