Introduction
The heavy metals pollution, as
consequence of the industrial progress and the human activities, is one
of the important issues in the environmental field representing an
appealing research. Heavy metals are involved in many pathologies from
cancer, neurodegenerative and metabolic diseases. One of the most
contaminated natural resource is water, since metals are often spilled
in soils through the industrial and consumer wastes and accumulate in
streams, lakes, rivers and water intended for direct consumption
bringing a series of implications for the human safety (Masindi, Muedi,
2018; Verma, Dwivedi, 2013). The water poisoning by arsenic is one of
the most studied subjects in this field. Arsenic is a natural metalloid
element diffused at different levels, from soil to water and air. Within
this distribution, it can react with oxygen or other molecules producing
various inorganic compounds, among which the toxic arsenite (AsIII)
(ATSDR, 2007). About 140 million people in 50 countries are under the
risk to use drinking, food preparation and irrigation water that is
poisoned by AsIII at levels above the World Health Organization (WHO)
provisional guideline value of 10 μg/L, becoming very dangerous for
human safety (World Health Organization, 2020; Ravenscroft et al.,
2009). When high concentrations of AsIII are present, the accumulation
inside the human organism by ingestion causes different effects from
simple skin lesions to more dangerous systemic disorders. Prolonged
exposures to AsIII, in fact, have been linked to cardiovascular
diseases, diabetes and liver, prostate and bladder cancer, while other
studies suggest a relationship with neurological effects and
reproductive organs (Hong, Song, Chung, 2014). Moreover, it seems to
influence the cognitive development and the incidence of youthful deaths
(Mandal, Suzuki, 2002).
Therefore, considering the arsenic toxicity it is extremely important
the availability of effective sensing technologies able to detect and
quantify AsIII in water. Unfortunately, many of the available detection
systems have several limitations mainly due to the complexity of the
analysis. Spectrometric (CV-AAS, AEF, CV-AFS), chromatographic and
potentiometric technologies even if they are the advantages to be
consolidate methods with low detection limits (LoD) of about 1
µgL-1 or 1 part per billion (ppb) and a wide range of
linearity in the determination of arsenic, however, present some
drawbacks in terms of time/cost consuming, long procedure, expensive
reagents, the need for a sample pre-concentration, lab constraints
referred to the bulky instrumentations and the high trained personnel
required to perform the analysis (United States Environmental Protection
Agency Office of Water, 1999; Yu, Wang, 2013; Bose, Rahman, Alamgir,
2011).
In this context, recently ultrasensitive method for dual-mode detection
of arsenic by colorimetric and surface-enhanced Raman SERS using
glutathione functionalized Au-nanoparticles has been described reaching
LoD of 0.14 ppb (Lia et al., 2020).Colorimetric assay on Paper-based
microfluidic device are has been developed allowing a rapid and low-cost
detection through a direct observation of a colour change induced by the
reaction of arsenic with specific dyes (Morita, Kaneko, 2006; Martinez
et al., 2010). This detection method is performed on paper strips or
microfluidic system so that the reagents are automatically mixed and
give a colorimetric signal. However, the main drawback of the procedure
is the specificity, since other molecules can cross-react with the
sensing dyes (phosphates and silicates, for example, compete to react
with molybdenum blue) giving a false positive and the sensitivity
reaching a LoD value of 10 ppb (Lace et al., 2019; Yogarajah, Tsai,
2015) .
Among the heavy metals pollution, also Hg represent a very dangerous
analyte. Actually, in its divalent inorganic form (HgII), it is
frequently spilled in water as waste of industrial activities and
accumulates over 1 ppb (defined by the WHO as the threshold value for
human safety) becoming toxic and bringing a high risk to contract severe
diseases as neurological disturbances, skin rash and kidney failure.
Therefore, a punctual quantification of mercury traces in water is
necessary and is, usually, performed by combined gas/liquid
chromatographic-spectrometric methods (GC-AFS, HPLC-AAS, HPLC-ICP-OES,
etc.) and surface-enhanced Raman scattering (SERS) techniques
(Kodamatani et al., 2011; Hashemi-Moghaddam, Saber-Tehrani, 2008;
Guerrini et al., 2014). These approaches share a good reliability, high
sensitivity (LoD of 0.14-0.17 ppb) and accuracy discriminating among
multiple mercury species (as methylmercury, ethylmercury, phenylmercury
and the mercury ions) with extreme precision. However, they have some
limitations in terms of lab constrain, referring to the bulky
instruments, and time and cost of analysis, considering the reactants
and procedure of sample preparation required for the mercury speciation,
which make these methods unsuitable for on-site measurements. In this
sense, many attempts have been made towards the miniaturization of the
entire detection system, as for the sensing platform based on optical
(both colorimetric and fluorometric) and electrochemical detection
(Santangelo et al., 2014). Optical sensors for the HgII detection in
water provide an improvement in terms of time and miniaturization of
analysis but are limited by the low sensitivity (most of the
colorimetric sensors report a LoD of 10 ppb) and the risk of
cross-sensitivity towards other metal ions (Chen et al., 2015; Yang et
al., 2018).
The above reported limitations can be overcome by using quantitative
transduction technologies based on electrical signal, as those based on
the Anodic Stripping Voltammetry or the Screen-Printed Electrodes, where
the measurement of the analyte concentration is not influenced by the
size of the sample used (Renedo, Alonso-Lomillo, Martínez, 2007). In
this context, the continuous development of electrochemical
silicon-based technologies together with miniaturization of
biotechnologies can give an opportunity to develop portable and
easy-to-use sensing devices able to solve the lab constraints above
described particularly those related to the need of specialized
personnel. The effectiveness of this approach has been already
demonstrated in several areas of medical field such us nucleic acids
analysis (Petralia, Sosentino, Sinatra et al., 2017; Petralia, Sciuto
and Conoci, 2017; Petralia, Sciuto, Di Pietro, et al., 2017) glucose and
biomolecules sensing (Petralia et al., 2018). Electrochemical systems
share the advantages of a high miniaturization degree, especially for
those improved with silicon technology (Petralia, Sciuto, di Pietro et
al et al., 2017), and a rapid and high-sensitivity analysis. Actually,
it has been found in case of AsIII detection reaching a LoD
>1 ppb that is well below the WHO value. However, the
sample preparation requires a pre-dilution of real samples before
analysis that can introduce additional variability into the measurement
and interferences (Toor, Sharma, Bansod, 2015). However, electrochemical
systems often require an expensive fabrication, considering the
functionalization and electrodes modification, and imply weak sensing
elements, as for the enzyme-based sensors (Pujol et al., 2014; Fu et
al., 2011).
To overpass these limitations, electrochemical sensors based on Noble
Metal nanoparticle-modified electrodes (Pd, Pt, Ag etc.) have been
developed for a wide series of applications (Petralia, et al, 2012;
Majid et al., 2006). Although they show excellent performances in terms
of sensitivity, however few data for specificity are reported (Kumar et
al., 2017; Babar et al., 2019). In this context, sensing strategies
based on specific enzyme are very appealing since they can offer
selective detection. In the case of arsenic direct enzymatic recognition
by arsenite oxidase Male et al., 2007) or indirect recognition trough
β-galactosidase have been reported (Stocker et al., 2003), reducing the
risk of cross-reactivity and increasing both the sensitivity and
selectivity. However, both these technological approaches present some
issues related to the specificity of the nanostructures-based sensor and
to the costs production and stability of enzyme-based device which
degenerate after a long usage inhibiting the recognition activity.
More recently, whole-cell based biosensors received a widespread
attention for their properties. Most of them are based on
microorganisms, especially bacteria, that have been genetically modified
in order to be sensitive towards the specific analyte of interest
(Sciuto et al., 2019; Gu, Mitchell, Kim, 2004). These biosensors use
engineered bacterium (generally an Escherichia coli ) as sensing
element that, each time it interacts and internalises the analyte to
detect, produces a reporter protein that express a detection signal
(optical, electrical and/or electrochemical). The genetic modification
of the sensing element makes the whole-cell-based biosensor extremely
customizable. Moreover, thanks to its intrinsic properties, the sensing
bacterium is physiologically more robust than enzymes, in case of
prolonged usage allowing a high degree of miniaturization and
portability, due to the small size and the survival at different
environmental conditions (Gilchrist et al., 2005). Finally, the genetic
recognition of arsenic, based on the modified expression of thears operon sequences, β-galactosidase or luciferase gene,
guarantees a strong selectivity excluding the risk of cross-reactivity
(Gui et al., 2017). However, the biotechnology has the main limitation
of the sample management since the sensing bacteria are strictly
dependant on the nutrients availability and the environmental parameters
(pH, temperature, or ionic strength) and, usually, require a long time
period to complete the genetic recognition of target, from the arsenic
internalization to the reporter protein transcription and translation.
In this work we propose an innovative miniaturized electrochemical
biosensing platform for the specific and high-sensitive quantification
of metal ions in water sample. The sensor system exploits the synergy
between two interfaced sensing modules: (a) a whole-cell-based module
using an engineered Escherichia coli as whole-cell sensing
element; (b) an electrochemical module based on a silicon chip,
integrating electrochemical cells (EC-cell) composed by three planar
microelectrodes, and a portable EC-reader, performing a cyclic
voltammetry (CV) analysis. The whole-cell sensing element has been
genetically modified to produce a redox active 4-aminophenol, as
mediator, each time it interacts with the metal target in a highly
specific manner. Thanks to the electrochemical detection of the
mediator, the metal is indirectly detected and quantified. Sensitivity,
robustness and selectivity of the sensing system have been fully studied
for AsIII and preliminary investigated for HgII proving the fully
versatility of the system towards multiplex heavy metals detection.