TOXICITY SENSING

Abstract: The toxicity of a sample is determined by contacting the sample with living cells, and monitoring the absorption of electromagnetic radiation by the cells of a diagnostic wavelength in the range of 190 nm et 250 nm.

Description: Title: Toxicity Sensing Field of the Invention The invention concerns toxicity sensing and relates to a method and apparatus for detecting toxicity. Background of the Invention Detecting toxic compounds in general is a difficult problem. There are a very wide range of techniques for detecting specific chemical entities: for example, atomic absorption spectroscopy, X-ray fluorescence, electrochemical methods to detect metal ions, mass spectrometry , gas chromatography, ultraviolet absorption to detect different classes of organic materials, specific enzyme assays to detect a range of compounds (see Garbarino, et al 1985. for a review of many different methods). However these simply detect the presence of one compound or type of compound. If other compounds are present which pose a toxic hazard which are not the target of these specific tests, then they will remain undetected. A route round this is to use a living system as a sensor for toxicity. Then any compound which poisons the system will affect the sensor, and so will be detected. A range of living systems can be used, with a range of detection methods to determine whether the systems have been affected by a toxin. Early systems used live fish, whose swimming or opercular movements or electrical activity were monitored. The survival of Daphnia in. water has been used as a bioassay (eg Dutka and Gorrie, 1989). However these detectors rely on complex, multicellular organisms which are difficult to analyse and almost impossible to store in an inert form prior to use. More recent developments have used single celled organisms such as algae, fungi or bacteria as sensing elements in general toxicity sensors (Walker, 1988). These have the advantage that the cells -are easy to grow, can be stored in inert form through freezing or freeze-drying, and are easy to incorporate into a small, rugged test system for a variety of toxicity monitoring applications. The first bacterially-based toxicity assay system was the bacterial mutagenesis test (Maroa and Ames, 1983) where bacteria are used to detect the presence of compounds capable of generating mutations, and hence also compounds which may be capable of initiating neoplastic transformation.. These assays relied on extensive bacterial growth to produce a macroscopic colony of bacteria: the number of bacterial colonies indicated the number of mutation events which had occurred. More rapid bacterial toxicity sensors have relied on a range of effects of toxins on bacterial, fungal or algal metabolism. These effects are measured in many ways. For example the MicroTox system used luminescent bacteria (Bulich et al., 1990). Light production by these bacteria is sensitive to depletion of the internal supply of metabolic energy. Any interference with the energy production in the bacteria interferes with the light production: thus reduction in light output represents an interference with intermediary metabolism by a potentially toxic material. Other assays which use single-celled organisms involve measuring the rate of oxygen uptake or generation, carbon dioxide generation or pH reduction as the micro-organisms metabolise (eg Karube, 1986, Riedel et al., 1989). Any changes in these parameters suggest the presence of a toxic compound. Isolated mammalian cells have also been used in this context. Some workers have also coupled bacterial cells directly to electrodes, and so measured the rate of electron transport as a measure of general metabolic "fitness": changes in the electrode potential reflect changes in the redox state of the " bacterial cell's membrane, and hence an alteration of the metabolic state of the organism (reviewed by Turner, 1989). Most of these assays have to be optimized for specific bacteria. As specific bacteria have different sensitivities to different toxic materials, this means that the sensitivity range of the assays can be limited for some materials. Some are also technically demanding. It is an aim of the present invention to provide an alternative approach to toxicity sensing. Summary of the Invention In one aspect the invention provides a method of determining toxicity of a sample, comprising contacting the sample with living cells, and monitoring the absorption of electromagnetic radiation by the cells at a wavelength in the range 190nm to 250nm. Single cells are generally transparent to visible light, exceptions arising from pigment laid down in the cell. All cells absorb " ultraviolet light in the region 250nm- 300nm due to the presence of protein and nucleic acid in the cell. Absorption of electromagnetic radiation in the visible region and in the region 250-300nm is therefore not diagnostic of the immediate metabolic state of the cell. However, cells also display an absorption peak in the region 190nm-250nm (in the mid ultra violet region of the spectrum) which is strongly affected by the metabolic state of the cell. This peak generally occurs at about 195nm, but the exact wavelength of the peak can be affected by a wide range of environmental and metabolic effects, and, as will be shown below, by toxins. For simplicity, the peak is referred to herein as "the peak at 195nm" or "the diagnostic peak", but it is to be understood that these expressions are intended to cover peaks in the 190nm to 250nm range regardless of the actual wavelength. Inhibition of cellular metabolism or rigorous removal of all metabolic substrates and co-factors from the environment of a cell give rise to a substantial reduction in the absorption of ultra-violet light in the region 190-250nm, arising from two effects. Firstly, the peak of absorption observed for these cells at 195nm shifts to longer wavelengths, so that the absorption at any wavelength between 190 and 200nm is reduced. Secondly, the maximum absorption of this peak is reduced. The present invention is based on the fact that toxins affect the metabolism of cells which in turn affects the peak at 195nm, reducing maximum absorption and also increasing the wavelength of the peak. The peak at 195nm is in fact due to a number of overlapping absorption peaks. Some toxins affect all components of this peak, some affect only some of these components. By monitoring changes in the absorption peak, particularly the amount of absorption at the peak, of cells in the presence and absence of a sample, an indication can be obtained of the toxicity of the sample. It is thought the effect is due to interference with energy metabolism in cells resulting in net oxidation of the oxidative phosphorylation pathway. Reduced haeme has a substantial absorbance peak at around 200nm which is absent from oxidised cytochrome haeme, accounting for the observed changes in absorbance at the diagnostic peak. See Tables 1 and 2 and further discussion below. The method can be used qualitatively or quantitatively. The invention can be carried out in various ways, e.g. as batch assays, as continuous flow injection analysis or as biosensor assays. The cells could be eukaryotic cells (e.g. protist, algal, fungal, yeasts, isolated plant or isolated animal cells such as cultured mammalian cells) or prokaryotic cells (e.g. bacterium, mycobacterium, archaebacterium etc.). The cells are typically used in an assay in which isolated cells are added to a dilute aqueous solution containing a sample to be tested, e.g. a suspected toxic material, and the absorption of electromagnetic radiation at a specific wavelength or a number of wavelengths encompassing the range of 190-250nm is measured. The change in the absorption at this wavelength, or the change in the pattern of absorption at a number of wavelengths, of cells which are exposed to the sample as compared with cells which are not exposed to the sample gives a measure of the toxicity effect of the sample. Other wavelengths may also be measured to provide additional information. For example the absorption at 260 or 280nm may be measured to estimate the amount of protein or nucleic acid present, or the scattering of light at longer wavelengths (e.g. 600nm) may be measured to estimate the number of cell particles present. The cells may be exposed directly to a potentially toxic sample, e.g. by simple mixing, or the sample may be initially separate from the cells in, for example, a separate compartment and the potentially toxic substances reach the cells through diffusion or osmotic processes. A detectable response generally occurs less than one minute after mixing the cells with the sample, typically within 15 seconds. The response time does not seem to vary significantly with toxin concentration. The method of the invention is found to be very sensitive to toxins, and generally can detect toxins at concentrations equal to or lower than those that inhibit cell growth in conventional assays. In a further aspect the invention also provides apparatus for determining the toxicity of a sample, comprising a receptacle for receiving a sample to be tested and living cells; and means for monitoring the absorption of electromagnetic radiation by the cells at a wavelength in the range 190nm to 250nm. The cells may be present in any convenient form. For example, the cells may be present as a suspension of cells, or the cells may be immobilised on or in a solid support, for example trapped within a polymeric matrix or bound in a layer to the surface of a support by use of antibodies or other non-covalent binding agents or by covalent cross-linking, such that changes in the absorption spectrum of the cells can still be detected. One convenient embodiment comprises a flow cell with means for passing a sample therethrough and conveniently using bacterial cells immobilized in an agarose membrane. UV absorbance is monitored continuously as samples flow past the membrane. A "dipstick" version is another possibility. The apparatus conveniently comprises two similar receptacles, e.g. spectrophotometer cuvettes, one for receiving the sample and cells and the other for receiving a control solution. The monitoring means conveniently comprises a spectrophotometer: suitable equipment is commercially available . A source of illuminating radiation may be provided if required. It is found that a wide range of toxins that affect the metabolism of cells have effects on the diagnostic peak. The invention thus provides a broad spectrum assay, capable of detecting a wide range of biocides, antibiotics, antimetabolites and other materials. This invention has a number of possible applications. It may be used for monitoring aqueous samples, e.g. water from rivers, soil, drinking water supplies, industrial waste water, such as from mining, large scale metal processing and paper making,- agricultural or domestic waste streams etc. , to detect the presence of any potentially toxic materials, by detecting materials which are toxic to the cells. It may similarly be used to detect the presence of toxic materials in water which has been used to collect those materials from other environments, e.g. from air (by a number of collection methods, e.g. bubbling the air through water), from dry surfaces or solids. The invention may also find application in quality control on input water e.g. in industrial or consumer contexts such as hotels, hospitals, restaurants. The invention may also be used to measure the toxicity of known materials, to screen for biocide action etc., by exposing cells to the material and monitoring the results. The invention could also be used to monitor oxidative phosphorylation processes in living cells. The invention is further described, by way of illustration, in the following Examples which refer to the accompanying Figures, in which: Figure 1A is a graph of absorbance versus wavelength illustrating the effect of different concentrations of the toxin 2-mercaptoethanol on Bacillus subtilis cells; Figure IB is a graph of absorbance versus wavelength, illustrating the effect of washing out micronutrients from B. subtilis cells; Figure 2A is a graph of optical density versus concentration of sodium azide (in ppm) , illustrating absorbance changes on incubating B. subtilis cells with different sodium azide concentrations, with empty circles showing OD at peak and filled circles showing OD at 200nm; Figure 2B is a graph of wavelength of the diagnostic peak (in nm) versus sodium azide concentration (in ppm), illustrating the effect on the wavelength of the optical density (OD) peak between 190nm and 250nm of incubating B. subtilis cells with different concentrations of sodium azide; Figure 2C is a graph of reciprocal doubling time (in hours) versus concentration of sodium azide (in ppm), illustrating the effect of different concentrations of sodium azide on the growth of B. subtilis cells; Figure 3A is a graph of normalised OD (OD 200nm/OD 260nm) versus concentration (in uM) , illustrating changes in OD 200nm of B. subtilis cells exposed to varying concentrations of different chemicals, with empty circles showing results for iodoacetic acid, filled circles showing results for oxa ic acid and triangles showing results for trimethoprim; Figure 3B is a graph similar to Figure 3A, with filled circles showing results for silver nitrate and empty circles showing results for sulphamethazine; Figure 3C is a graph similar to Figure 3A, with circles showing results for glucose and triangles showing results for sodium chloride; Figure 4 is a graph similar to Figure 1, showing the effect of five differing concentrations of sodium azide on S. cerevisiae; Figure 5 is a graph of optical density versus concentration (in ppm) illustrating changes in OD of S. cerevisiae cells exposed to varying concentrations of different toxins, with filled circles showing results for sodium acetate, filled triangles showing results for glucose and empty squares showing results for sodium azide; Figure 6 illustrates a cell of one embodiment of apparatus for performing the invention; Figure 7 illustrates apparatus incorporating cells as shown in Figure 6; and Figure 8 is a graph of OD at 200nm versus time showing results obtained with Tween-20 (Figure 8a) and sodium azide (Figure 8b). Examples Bacterial cells were obtained from NCIMB (Torrey Research, Aberdeen, Scotland) and grown in L-broth (1% yeast extract, 0.5% Bacto tryptone, both from Oxoid Chemicals Ltd., 1% sodium chloride) at 37°C with shaking. 1ml of an overnight culture of bacteria was inoculated into lOOmls of L-broth and cultured until the bacteria were at a convenient density, usually giving an optical density (OD) at 600nm of between 0.1 and 0.3. Bacteria were harvested by centrifugation at 6000rpm in a Sorvall RC-5B centrifuge at 4°C, and resuspended in water for use. There was sufficient trace nutrient in this preparation to maintain the bacteria in a "non-toxic" state for several hours at room temperature. Saccharomyces cerevisiae cultures were prepared from commercially available freeze-dried brewers' yeast (Boots pic, UK), cultured in L-broth and treated as described above. Other chemicals were obtained from Sigma Chemical Co. , UK. Two quartz spectrophotometer cuvettes, with a pathlength of 1cm, were prepared each containing 1ml of distilled water. To one of these a volume of bacterial cells was added to give a convenient OD at 200nm, typically in the range of 1 to 2. To both cuvettes a volume of potentially toxic dilute aqueous solution was added: the solution was added to both cuvettes because some of the potentially toxic solutions have a significant absorption in the range 190nm-250nm. The second cuvette, containing no bacterial cells, was a "blank" control cell against which the optical density of the cuvette containing bacteria is measured. The OD between 190 and 350nm was measured in a Phillips SP8-500 UV/visible scanning spectrometer: the OD at 260nm or 280nm was taken as a measure of the total amount of protein present, and the 0D at 350nm was taken as the "baseline" absorption. These last two are control values which are used to normalise other measurements of 0D between 190nm and 250nm if needed, e.g. to compensate for slight variations in the number of cells in different experiments . The results of various different experiments are illustrated in the Figures. Figure 1A shows the effect on the spectrum of adding different amount ' s of 2-Mercaptoethanol, a known protein- denaturing toxic agent, to cells of B. subtilis strain 8054. While absorbance at 260nm due to protein and RNA remains substantially constant, the diagnostic peak at about 195nm alters substantially: the peak wavelength increases and peak height decreases. A similar pattern is obtained with a large number of compounds, although at widely varying concentrations. Figure IB shows the effect of washing all trace nutrients away from the cells , and illustrates that the ef ect of high concentrations of a toxic agent is different in quality from the effect of washing the cells, although low levels of a toxic agent have a similar effect to washing the cells. Figures 2A, 2B and 2C show the results of further experiments using sodium azide, a known broad-spectrum biocidal agent. Figure 2A shows the OD at 200nm and the OD at the top of the 195nm peak (wherever the top of that peak is) plotted against the amount of sodium azide added. Figure 2B shows the wavelength of the peak of absorption between 190nm and 250nm, again plotted against the amount of azide added. Figure 2C shows the biocidal effect of sodium azide on these cells. Cells were grown in the presence of differing amounts of sodium azide, and their OD at 600nm measured: this gives a measure of cell density. Figure 2C shows the plot of the reciprocal of the doubling time of the cells during their exponential growth phase (this is a measure of the rate at which they grow) against different concentrations of azide in their growth medium. A comparison of Figures 2A, 2B and 2C shows that the technique of the invention can detect concentrations of azide similar to those which are detected by a bacterial growth assay, ie that the response of this technique can correlate with demonstrable biological effect of sodium azide. Figures 3A and 3B show that a range of other biocidal agents are also detected by the technique of the invention. Figure 3A shows that the technique detects trimethoprim (a specific antibiotic), and also oxamic acide and iodoacetic acid (which inhibit specific steps in metabolism, and hence act as wide-spectrum biocidal agents). Figure 3B shows that the technique responds to sulphamethazine (another specific antibiotic) and to silver nitrate (a heavy metal salt, which inactivates a number of enzymes in many living systems). Figure 3C shows that, by contrast, the sensor is not affected by high concentrations of glucose or sodium chloride, compounds which are non-toxic to this organism. Thus the technique can be specific - discriminating between toxic and non-toxic materials - as well as sensitive. Ranking of ultraviolet optical effects of metabolites and toxins Tables 1 and 2 summarise the effects of several compounds on the mid-ultra-violet spectrum of B. subtilis. Three EC50 measures are used, corresponding to the effects of the various compounds on the absorbance at 200nm (the most sensitive measure), the absorbance of the absorbance peak falling between 190nm and 260nm, and the wavelength of that peak. (EC50 is that concentration at which the change in a parameter from the "control" value is 50% of the maximum change possible. Thus, if sodium azide causes a drop of OD from 2 to 0, then the EC50 would be that concentration that causes an OD of 1 ) . (Some values are not filled in because the concentration range tested did not extend far enough to estimate that value (usually because of solubility limit effects)). Table 1 shows these effects for a range of metabolites, ordered in .decreasing EC50 values. Notably, this also orders the compounds according to their degree of oxidation: the relative ratio of carbon+hydrogen to oxygen is shown in column 6 of this table. The table suggests that reduced metabolites, particularly sugars, have very little effect on the ultraviolet absorbance of B. subtilis. More oxidized metabolites have an increasingly dramatic effect. This suggests a link between the metabolic fate of the compounds and their effect on the ultraviolet absorbance. Table 2 lists the EC50 values for several metabolic inhibitors. Several inhibitors of central metabolism also cause a drop in absorbance around 200nm, and show this effect at much smaller concentrations than the concentrations of metabolites needed to show the same effect, concentrations similar to the concentrations known to be necessary to inhibit cellular metabolism and growth. The one substantial exception is sodium cyanide. A comparison of the OD200 of cells exposed to differing concentration of sodium cyanide with the reciprocal growth rate of cells exposed to those concentrations of cyanide shows that the change in mid-ultra-violet absorbance occurs at much higher concentrations of this metabolic inhibitor than does the effects on cell growth. Table 3A summarises the effect of a number of antibacterial agents on the bioassay of the invention. The assay is a sensitive detector of organic compounds that inhibit bacterial growth or metabolism by a number of routes. By contrast its response to heavy metal intoxicants occurs at higher concentrations. Table 3B shows the equivalent measure ' s on solutions of non-toxic salts, and shows that this is a specific effect of toxins on the cells and not a general effect of charged species or of the metal counterions on the cell. An exception is sodium bromide (potassium bromide behaves similarly - not listed), which has significant spectroscopic effects at around ImM. Spectral effects of compounds on other types of cell Because the spectral changes observed are large, they must be ocurring to one of the prominent chromophoric components of the B. subtilis cell. This could be an element of the cell that is specific to B. subtilis , or could be a component common to many types of organism. To see how general the effects of compound on the mid-ultra¬ violet absorbance was, two other organisms were analysed. Both E. coli and S. cerevisiae show similar mid-ultra¬ violet absorbance changes when exposed to materials which are likely to alter their primary metabolism. The optical effects on the Saccharomyces cells are shown in Figures 4 and 5. The spectrum of yeast cells in water differs from that of bacterial cells, and in particular, if the concentration of the different cell suspensions is adjusted until their absorbance at 260nm is the same, the scattering of light by the yeast cells is substantially greater than the scattering by bacterial cells and the relative height of the absorbance peak at 195nm is greater for yeast cells than for bacterial cells. However these cells show the characteristic changes seen in B. subtilis cells when they are exposed to metabolites or toxins. Figure 4 shows the spectral changes resulting from incubating Saccharomyces cerevisiae with sodium azide. Increasing concentrations of azide cause a decrease in the height of the absorbance peak between 190nm and 260nm, and increase in the peak wavelength of that peak. Figure 5 shows the effect of two typical metabolites on these cells, and shows that the effects on the mid ultra¬ violet spectrum is very similar to the effect of similar compounds on Bacillus subtilis cells. While the effects of sodium acetate occur at similar concentrations for these organisms and for B. subtilis (compare with Table 1), the yeast cells show less responsiveness to glucose that B. subtilis. This is probably because this yeast strain has been selected for brewing, and hence is unusually osmotolerant of the high concentrations being used here: possibly, then, the effects of glucose on B. subtilis and E. coli recorded here represent gross cellular damage through osmotic effects. Generally similar results (not shown) were obtained in experiments using E. coli cells and mammalian cells in culture. Figures 6 and 7 illustrate one embodiment of apparatus suitable for carrying out the method of the invention in the form of a flow cell. Figure 6 shows the basic cell configuration. A quartz cuvette 10 has supported therein by means of clip 12 a low gelling temperature agarose membrane 14 with bacterial test cells immobilized therein in conventional manner. A silicone bung 16 is located in the top of the cuvette and has respective tubes 18, 20 passing therethrough for liquid flow into and out of the cuvette, so that a test sample can flow over the membrance in the direction of arrow 22. Figure 7 shows the flow cell assembly. Two cells of the configuration shown in Figure 6 are mounted in a suitable spectrophotometer (not shown), e.g. a Phillips SP8-500 UV/visible spectrophotometer. Test cell 24 includes a membrane with bacterial cells, while control cell 26 includes a membrane with no bacterial cells. In use, a sample to be tested is pumped by pump 28 from a receptacle 30 via respective tubes 32, 34 to the inlet ports of the test cell and control cell, to flow over the membranes and out of the cells to a reservoir 36. The spectrophotometer compares the absorbance of the 2 cells at 200nm as the sample is passed over the membranes. Figure 8 shows typical traces from experiments using the apparatus of Figure 7, during exposure to 0.01% Tween 20 (Figure 8a) and lOppm sodium azide (Figure 8b), with OD at 200nm plotted against time. These figures show that the sensor responds to toxin in about 10 seconds and recovers from sub-lethal intoxication in 1-2 minutes. If the sensor is not exposed to extreme intoxication, it has an operating life of at least 11 hours at room temperature (the longest an experiment has been continued so far). Discussion The examples above show that the ultraviolet absorbance characteristics of whole B. subtilis, E. coli and S . cerevisiae cells are affected markedly by metabolites added to those cells. The range of cells tested shows that this must be an effect of the compounds on a fundamental metabolic process common to prokaryotes and eukaryotes. The changes detected by mid-ultra-violet spectroscopy affect the cell within 15 seconds of addition of the compound, and affect prokaryotic and eurkaryotic cells grown under aerobic conditions. Thus they must be effects which the compounds have on central metabolism (rather than on slower processes such as DNA replication or cell division) , and on aspects of that metabolism that are common to prokaryotes and eukaryotes. The effect of surface-active compounds such as Tween-20 and of materials which interfere with ion-transport functions such as sodium iodide argue that the effects being detected as changes in mid-ultraviolet spectra are changes in aspects of metabolism closely associated with membrane proteins, membrane ion distributions or both. It is suggested that the results presented above are consistent with the detection by mid-ultraviolet spectroscopy of the redox state of some of the enzymes of oxidative phosphorylation (OP) in these cells. These enzymes are expected to have very large mid—ultraviolet absorption bands caused by charge-transfer absorption bands of the iron atoms in their haeme moieties. It is suggested that the high-absorbance, 'normal' state is equivalent to reduced haeme, the low-absorbance, 'poisoned' state is equivalent to oxidized haeme. In cells which are actively growing, the OP chain will primarily be in a reduced state. If central metabolism is blocked so that the supply of reducing equivalents to the OP chain is disrupted, then continued activity of the enzymes of the chain will result in the rapid oxidation of all members of the OP chain, and a fall in mid-ultraviolet absorbance. Similarly, supplying the cell with a metabolite which is already partly oxidized as a sole carbon source will divert reducing equivalents away from oxidative energy metabolism and into the assimilation of that compound. The more oxidized the compound is, the more reducing equivalent it will require. Relatively reduced sugars such as glucose will instead generate reducing equivalents during their metabolism, and so will 'feed' the OP chain with reducing equivalents. In this scheme, the anomalous effect of sodium cyanide on the cells is to be expected. Cyanide, alone among the metabolic inhibitors listed in Table 2, inhibits cytochrome C oxidase, the terminal enzyme of the OP chain. Although this effectively inhibits cell growth, sodium cyanide has almost no effect on the mid-ultraviolet absorbance of the cells. If the mid-ultraviolet absorbance is characteristic of the redox state of the OP chain, then this is to be expected: inhibiting cytochrome C oxidase will inhibit the oxidation of any of the enzymes of the OP chain, and hence will not cause the oxidation of the OP chain enzymes. This work shows that the ultraviolet absorption spectrum of bacterial and yeast cells may be manipulated by addition of a variety of metabolites and metabolic inhibitors. It is plausible to suggest that the absorption characteristics being monitored are due to absorption by the haeme groups in the oxidative phosphorylation pathway. Whether this mechanism is correct or not, the phenomenon may be useful for classifying how bacteria respond to a number of metabolites and metabolic inhibitors. If the mechanism is correct, then the observation may also provide a simple analytical tool for observing the redox state of the oxidative phosphorylation enzymes in living microorganisms with simple, standard laboratory equipment. TABLE 1. Effect of metabolites on mid-ultraviolet spectrum of B. subtilis cells. Column 1: Metabolite tested. Columns 2-4: EC50 values for this metabolite, using different spectral parameters to estimate the effect of the metabolite on the cells. Parameters are: column 2: OD at 200nm. column 3: OD at absorbance peak between 190nm and 260nm. Column 4: wavelength of absorbance peak between 190nm and 260nm. Column 5: degree of oxidation of the metabolite R. If C=number of carbon atoms, H=number of hydrogen atoms, 0=number of oxygen atoms, R=(4*C+H)/0. * Note that, although oxamic acid is an inhibitor of isolated tricarboxylic acid cycle enzymes in vitro, it does not inhibit the growth of B. subtilis cells significantly (results not shown), and so is considered here as a metabolite. Compound OD OD Peak 'Oxidation' 200 eak wavelength Glucose Fructose Succinic acid Sodium acetate Citric acid Malic acid Oxamic acid* TABLE 2. Effect of metabolic toxins on mid-ultraviolet spectrum of B. subtilis cells. Column 1: Metabolic toxin. Columns 2-4: EC50 values for this metabolite, using different spectral parameters to estimate the effect of the metabolite on the cells. Parameters are: column 2: OD at 200nm. Column 3: OD at absorbance peak between 190nm and 260nm. Column 4: wavelength of absorbance peak between 190nm and 260nm. * The sensitivity of the cells to silver nitrate depends critically on the amount of culture medium left in the cell preparation, as residual chloride in the medium precipitates the silver into relatively inactive forms. Compound 0D 200 ~~" a k Peak wavelength Sodium azide 15 25 20 Silver nitrate* 10 2 15 Iodoacetic acid 42 50 50 Chloramphenicol 50 120 80 Sulphamethazine 16 20 20 Trimethoprim 27 50 50 TABLE 3A avelength peak 1.90 0.24 0.10 ND 0.24 0.78 0.20 0.34 0.40 0.172 0.085 0.61 0.31 0.96 28 0.20 TABLE 3B Material 0D 2- n 00 . . 0D peaik Waveleng °th p r eak Caesium chloride Over 100 Over 100 Fructose 47 Over 100 Glucose Over 160 Over 160 Inositol 50 65 N-acetyl 25 25 glucosamine Potassium chloride Over 30 Over 30 Sodium bromide 0.5 1 Sodium fluoride 20 20 Trizma 20 25 (tris hydroxymethyl) aminomethane Tween-20 10 15 15 LEGEND TO TABLE 3 Concentrations of compounds (mM) which caused half-maximal changes in spectroscopic characteristics in B. subtilis cells. Column 1: compound. Columns 2-4: concentration causing half-maximal change. References Bulich, A A, Tung, K-K and Scheiber, G (1990). Journal of Bioluminescence and Chemiluminescence jj_, 71-77. Dutka, B J and Gorrie, J F (1989). Environmental Pollution 5J7_, 1-7. Garbarino, J R, Steinheimer, T R and Taylor, H E (1985). Analytical Chemistry 57_, 46R-88R. Karube, I (1986). "Fundamentals and Applications of Chemical Sensors", pp330-348. Maron, D M and Ames, B N (1983). Mutation Research 113, 173-215. Reidel, K, Renneberg, R, Wollenberger, U, Kaiser, G and Scheller, F W (1989). Journal of Chemical Technology and Biotechnology 4_4_, 85-106. Turner, A P F (1989). Sensors and Actuators 1_7_, 433-450. Walker, J D (1988). Journal WPCF 0_, 1106-1121.

Claims: Claims 1. A method of determining toxicity of a sample, comprising contacting the sample with living cells, and monitoring the absorption of electromagnetic radiation by the cells at a wavelength in the range 190nm to 250nm. 2. A method according to claim 1, comprising monitoring the amount of absorption at a wavelength in the range 190nm to 250nm. 3. A method according to claim 1 or 2, wherin the cells comprise bacterial cells, yeast cells or cultured mammalian or plant cells. 4. A method according to claim 1, 2 or 3, further comprising monitoring absorption at other wavelengths. 5. A method according to any one of the preceding claims, wherein the cells are present as a suspension of cells. 6. A method according to any one of claims 1 to 4, wherein the cells are immobilised on or in a solid support. 7. Apparatus for determining the toxicity of a sample, comprising a receptacle for receiving a sample to be tested and living cells; and means for monitoring the absorption of electromagnetic radiation by the cells at a wavelength in the range 190nm to 250nm. 8. Apparatus according to claim 7, comprising two similar receptacles, one for receiving the sample and cells and the other for receiving a control solution. 9. Apparatus according to claim 7 or 8, comprising a flow cell with means for passing a sample therethrough. 10. Apparatus according to claim 7, 8 or 9, wherein the cells are present as a suspension of cells. 11. Apparatus according to claim 7, 8 or 9, wherein the cells are immobilized in or on a solid support. 12. Apparatus according to any one of claims 7 to 11, wherein the monitoring means comprises a spectrophotometer. 13. Apparatus according to any one of claims 7 to 12, further comprises a source of illuminating radiation.

Inventors: BAINS WILLIAM ARTHUR, BAINS, WILLIAM, ARTHUR
Applicants: PA CONSULTING SERVICES, PA CONSULTING SERVICES LIMITED
Application year: 1992
Main classification: C12Q1
IPC codes: C12M1, C12Q1, G01N33