A Kitova, A Reshetilov, O Ponamoreva, T Leathers
A Kitova, A Reshetilov, O Ponamoreva, T Leathers. Microbial Biosensors for Selective Detection of Disaccharides. The Internet Journal of Microbiology. 2009 Volume 8 Number 2.
Seven microbial strains were screened for their ability to detect disaccharides as components of Clark-type oxygen biosensors. Sensors responded to varying degrees to maltose, cellobiose, sucrose, and melibiose, but none responded strongly to lactose. Although microbial sensors are relatively nonspecific, it is possible to obtain differential measurements of specific substrates using multiple sensors with different relative specificities. For example,
Whole cell microbial biosensors offer advantages for the real-time quantitative measurement of analytes (D’Souza, 2001). They are simple and inexpensive to construct, offer sensitivity and stability, and are rugged and durable under field conditions. However, microbial biosensors often suffer from a lack of specificity towards related substrates. This limitation may be overcome by the use of multiple sensors with complementary specificities. For example, we previously demonstrated that a nonspecific microbial sensor could be used for the specific detection of ethanol in a two-component sensor system (Reshetilov et al., 1998). Sophisticated techniques of chemometrics and artificial neural networks greatly enhance the processing of data from a microbial sensor array (Lobanov et al., 2001).
Relatively few works have focused on microbial sensors for the detection of disaccharides. Riedel et al. (1990) describe the use of
Direct quantitation of disaccharides would be useful in numerous commercial food processes. Mixtures of sucrose and maltose are used for the enzymatic production of glucan oligosaccharides, useful as prebiotics and low glycemic index sweeteners (Monsan and Paul, 1995; Carlson and Woo, 2004; Cote and Holt, 2007; Grysman et al., 2008). Since the concentration of sucrose and maltose affects the type of oligosaccharides produced (Reh et al., 1990), it would be useful to have a convenient method to monitor and control these disaccharides in real time.
In this work we survey microorganisms for their ability to detect disaccharides as components of microbial biosensors, and identify two strains that show promise for the development of a two-component biosensor for sucrose and maltose.
I. Microbial strains and culture media
II. Biosensor fabrication and operation
Cells were separated by centrifugation at 10,000 x g for 5 min and twice washed with potassium-phosphate buffer (30 mM, pH 7.5). For
For the fabrication of biosensors, a bioreceptor of 3 × 3 mm2 was fixed to the measuring surface of a Clark oxygen-type electrode by a capron net and a fitting ring. Measurements were performed in an open cuvette, and the sensor signal was recorded by an IPC2L amperometric potentiostat connected to a desktop computer. An analyte sample (5-100 l) was introduced into a 2-ml cuvette, and measurements were performed under constant stirring. Biosensor responses were recorded as the maximal rate of change in signal (nA/s).
Results and Discussion
Responses of microbial sensors to saccharides
Table 1 shows the responses of microbial sensors to 1 mM glucose and 1 mM disaccharides. Glucose serves as a positive control, and as expected, all strains responded most strongly to this sugar. Most of the strains responded to maltose and cellobiose. Both
Calibration dependences of microbial sensors
Fig. 1 shows the calibration dependence of a microbial sensor based on
By contrast, Fig. 2 shows the calibration dependence of a microbial sensor based on
Additivity of microbial sensor signals
The additivity of microbial sensor signals was assessed within the initial part of the determined response ranges. Multi-component analysis requires that the sensor signal corresponding to one substance changes upon the addition of the second substance to the sample. The simplest case is complete (linear) additivity, in which the sensor response to a sample containing two substances is equal to the sum of sensor responses to each component separately. Nonlinearity usually requires a thorough study of weighted coefficients or the introduction of approximating dependences. For example, in our previous study on the selective detection of ethanol, we used the classical model of multi-component analysis (cluster analysis) suggesting the linear additivity of sensor responses: , where
For the current study, additivity was tested by measuring the responses to 0.025 mM sucrose, 0.25 mM maltose, and a mixture of these two disaccharides at the same final concentration. As shown in Table 2, the sensor signals were linearly additive for selected samples.
These findings suggest that a system based on the bacteria
This work was conducted under Specific Cooperative Agreement 58-3620-5-F131 between the Institute of Biochemistry and Physiology of Microorganisms, Pushchino, Moscow Region, Russia and the Agricultural Research Service of the U.S. Department of Agriculture. This study was supported partially by the Federal Task Program “Scientific Brainpower and Research and Educational Personnel in Innovative Russia” for the period of 2009-2013 (Project NK-37(4), Agreement P258).