Exploiting Catabolite Repression and Stringent Response to Control Delay and Multimodality of Bioluminescence Signal by Metal Whole-Cell Biosensors: Interplay between Metal Bioavailability and Nutritional Medium Conditions

Abstract

The time-dependent response of metal-detecting whole-cell luminescent bacterial sensors is impacted by metal speciation/bioavailability in solution. The comprehensive understanding of such connections requires the consideration of the bacterial energy metabolism at stake and the effects of supplied food on cells’ capability to convert bioaccumulated metals into light. Accordingly, we investigated the time response (48 h assay) of PzntA-luxCDABE Escherichia coli Cd biosensors in media differing with respect to sources of amino acids (tryptone or Lysogeny Broth) and carbon (glucose, xylose and mixtures thereof). We show that the resulting coupling between the stringent cell response and glucose/xylose-mediated catabolite repressions lead to well-defined multimodalities and shapes of the bioluminescence signal over time. Based on a recent theory for the time–response of metal-sensing luminescent bacteria, successful theoretical reconstructions of the bioluminescence signals are reported under all Cd concentrations (0–20 nM) and nutritive conditions examined. This analysis leads to the evaluation of time-dependent cell photoactivity and qualitative information on metal speciation/bioavailability in solution. Biosensor performance and the position, shape, number, and magnitude of detected peaks are discussed in relation to the metabolic pathways operative during the successive light emission modes identified here over time. Altogether, the results clarify the contributions of metal/nutrient bio-availabilities and food quality to cell response typology.

Comparative Analysis of Cell Metabolic Activity Sensing by Escherichia coli rrnB P1-lux and Cd Responsive-Lux Biosensors: Time-Resolved Experiments and Mechanistic Modelling

“…For the sake of illustration, quantifying the concentration of (bioavailable) metals from the signal of a metal-responsive (non-constitutive) luminescent biosensor still remains today a formidable challenge. This is explained by the complexity of the intertwined processes that define, e.g., the partitioning of the relevant metal forms at the biosensor/solution interface [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] and the ensuing time-dependent bioluminescence [ 39 , 40 ]. In turn, the analysis remains currently limited to correlative observations that often involve the recourse to calibration media whose nutritional quality is tacitly supposed equivalent to that of the sample of interest.…”

“…Common strategy further relies on empirical connections between bioluminescence cell output and bulk metal concentrations estimated from equilibrium Biotic Ligand Model (BLM) and, e.g., Visual MINTEQ metal speciation code [ 13 , 29 , 41 , 42 , 43 , 44 , 45 , 46 ]. As extensively commented elsewhere [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ], the BLM-based approach suffers from a number of approximations, e.g., it ignores a priori the possible contribution of labile metal complexes to the flux of bioaccumulated metals [ 30 , 32 , 36 , 37 , 38 ], it misses any rationale of the energetic cell demands required to convert internalized metals into cell signal output [ 23 , 39 , 40 ], it fails to reproduce metal bioaccumulation kinetics captured by Best’s formalism and extensions thereof [ 30 , 31 , 33 , 34 ], and it discards the implications of bulk metal depletion [ 31 , 33 , 34 , 47 ] or passive metal adsorption [ 48 ] on metal bioavailability and biosensor signal. Similarly, theoretical evaluation of medium toxicity from analysis of non-specific biosensors signal is tied to a successful formulation of cell sensitivity to stress and of the resulting cell ability to maintain (or not) light production under unfavourable conditions.…”

“…Similarly, theoretical evaluation of medium toxicity from analysis of non-specific biosensors signal is tied to a successful formulation of cell sensitivity to stress and of the resulting cell ability to maintain (or not) light production under unfavourable conditions. As a result, the analysis of the response of constitutive and non-constitutive biosensors is generally restricted to mere consideration of the only maximal cell signal amplitude [ 41 , 42 , 43 , 44 , 45 , 46 ] without comprehensive evaluation of the information-rich dependence of the signal on time [ 40 ]. The feasibility of current biosensing practice and reliability of corresponding results thus basically rests on the availability of proper controls as any decrease in the overall metabolic activity of a given stress-responsive or chemical-responsive biosensor may produce similar effects to those in the absence of the targeted stress or chemical, which possibly leads to false negative or false positive [ 12 ].…”

“…In their attempt to rationalize and increase predictability of whole-cell biosensors signal, Duval et al derived a formalism for the time-dependent bioluminescence response of metal-detecting lux -based Escherichia coli biosensors under conditions where cell concentration remains constant over time and metal depletion from bulk solution is not operational [ 39 ]. Theoretical results were successfully confronted against measurements of bioluminescence produced by E. coli sensors engineered with pZnt- lux plasmid harbouring a Cd-inducible zntA - luxCDABE gene fusion [ 23 , 39 , 40 ]. The formalism explains how the dependence of bioluminescence on time is governed by rapidly established steady-state metal biouptake flux, by the metal-mediated kinetics of lux -reporter expression, by the ensuing rates of luciferase production/degradation, and the kinetics of photon emission.…”

“…A remarkable result is that, contrary to modelling reports in the literature [ 49 ], bioluminescence is not proportional to the concentration of photoactive cells in solution or, equivalently, it is not related linearly to cell photoactivity. Changes in the latter with time reflect the way cells (re)allocate bioavailable energetic resources to sustain light production, cope with or adapt to toxicity/hormesis effects mediated by contaminants or to limitations in essential nutrients [ 23 , 39 , 40 , 48 ]. In line with this theory, the cell signal may then take the form of multiple, well-defined successive peaks whose defining patterns (amplitude, number, shape, time-positioning) are not only dependent on metal concentration but also on the kinetic settings of the metabolic pathways over the course of time [ 23 , 40 ].…”