Guerra Giraldez, D. | Search bioRxiv

bioRxiv2025

Development and characterisation of pNarsenic: a naringenin-inducible biosensor for arsenic in Escherichia coli

Whole-cell biosensors detecting the heavy metal arsenic have been widely studied for their potential in environmental monitoring. And while inducible biosensors have been shown to be an effective tool to tune the operational range, a thoroughly characterised inducible biosensor is currently lacking. Here, we present an Escherichia coli biosensor for arsenic in which the transcription factor gene arsR is inducible by naringenin, a plant-derived secondary metabolite. Increasing naringenin concentrations reduced the basal output while increasing both the dynamic range and sensing threshold of the biosensor dose-response curves, but the operational ranges appeared constrained by a fixed upper limit. Comparison with a previously published phenomenological model revealed good overall agreement between experimental data and model predictions, except for the behaviour of the maximum output and threshold. This work expands the biosensor toolbox with a profoundly characterised arsenic biosensor and raises a potential practical limit to dose-response curve engineering by tuning transcription factor expression alone. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/678503v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@c15051org.highwire.dtl.DTLVardef@180214forg.highwire.dtl.DTLVardef@10afa84org.highwire.dtl.DTLVardef@1c51bba_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2023

Dose-Response Profiling in Heterologous Gene Expression: Insights from Proteome Fraction Analysis

Many studies characterize transcription factors and other regulatory elements to control the expression of heterologous genes in recombinant systems. However, most lack a formal approach to analyse the parameters and context-specific variations of these regulatory components. This study addresses this gap by establishing formal and convenient methods for characterising regulatory circuits. We model the bacterial cell as a collection of a small number of proteome fractions. Then, we derive the proteome fraction over time and obtain a general theorem describing its change as a function of its expression fraction, which represents a specific portion of the total biosynthesis flux of the cell. Formal deduction reveals that when the proteome fraction reaches a maximum, it becomes equivalent to its expression fraction. This equation enables the reliable measurement of the expression fraction through direct protein quantification. In addition, experimental data demonstrate a linear correlation between protein production rate and specific growth rate over a significant time period. This suggests a constant expression fraction within this window. The expression fractions estimated from the slopes of these intervals and those obtained from maximum protein amount points can both be independently fitted to a Hill function. In the case of an IPTG biosensor, in five cellular contexts, expression fractions determined by the maximum method and the slope method produced similar dose-response parameters. Additionally, by analysing two more biosensors, for mercury and cumate detection, we demonstrate that the slope method can be effectively applied to various systems, generating reliable Hill function parameters.

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