We present an automated modular high-throughput-method for the identification and characterization of microbial exopolysaccharides in small scale. This method combines a fast preselection to analyze the total amount of secreted polysaccharides with a detailed carbohydrate fingerprint to enable the fast screening of newly isolated bacterial strains or entire strain collections.
Many microorganisms are capable of producing and secreting exopolysaccharides (EPS), which have important implications in medical fields, food applications or in the replacement of petro-based chemicals. We describe an analytical platform to be automated on a liquid handling system that allows the fast and reliable analysis of the type and the amount of EPS produced by microorganisms. It enables the user to identify novel natural microbial exopolysaccharide producers and to analyze the carbohydrate fingerprint of the corresponding polymers within one day in high-throughput (HT). Using this platform, strain collections as well as libraries of strain variants that might be obtained in engineering approaches can be screened. The platform has a modular setup, which allows a separation of the protocol into two major parts. First, there is an automated screening system, which combines different polysaccharide detection modules: a semi-quantitative analysis of viscosity formation via a centrifugation step, an analysis of polymer formation via alcohol precipitation and the determination of the total carbohydrate content via a phenol-sulfuric-acid transformation. Here, it is possible to screen up to 384 strains per run. The second part provides a detailed monosaccharide analysis for all the selected EPS producers identified in the first part by combining two essential modules: the analysis of the complete monomer composition via ultra-high performance liquid chromatography coupled with ultra violet and electrospray ionization ion trap detection (UHPLC-UV-ESI-MS) and the determination of pyruvate as a polymer substituent (presence of pyruvate ketal) via enzymatic oxidation that is coupled to a color formation. All the analytical modules of this screening platform can be combined in different ways and adjusted to individual requirements. Additionally, they can all be handled manually or performed with a liquid handling system. Thereby, the screening platform enables a huge flexibility in order to identify various EPS.
Microbial exopolysaccharides (EPS) are a structurally highly diverse group of polymers that fulfill various biological functions. They usually are built of complex repeat units, which are distinguished by different types of monomers (sugar, sugar derivatives, sugar acids), the bonds between these monomers and their substitutions. The structural diversity of microbial polysaccharides confers rather different characteristics to the members of this molecule class, which allows their application in different fields like food1, cosmetics2,3, construction chemistry4 or water treatment5. To further extend the field of application of these bio-based and such sustainable polymers the identification of novel natural microbial polysaccharides as well as the engineering of structural variants represents promising approaches. Either way, a fast screening method is required to quickly scan a vast number of microbial strains for their polysaccharide formation, and to analyze their products. Therefore, we have recently developed a 96-well HT-screening platform for the analysis of microbial polysaccharide production from natural isolates or engineered strain variants that includes the identification of the monomeric composition6.
Applying this platform for a first screening round of ~500 natural isolates allowed us to identify only about 10 to 20% of the isolated strains as being able to produce EPS (data not shown). This means that 80-90% of the analyzed strains did not produce EPS under the conditions applied, and therefore, a further analysis of the detailed carbohydrate fingerprint was not necessary. As this highly sophisticated identification of the monomeric composition is a time consuming process, especially for data analysis, a fast pre-screening method to identify the strains positive in EPS production, would drastically increase the efficiency. Furthermore, reagents, consumables and measurement time at the UHPLC-UV-ESI-MS would be reduced. Additionally, the different analytical modules, while on one hand make the method highly reliable, are on the other hand complicating the manual handling of more than two 96-well plates in parallel, and as such restrict the full potential of the method. For these reasons, we decided to develop an automated screening platform. Therefore, we combined the modular format of the existing carbohydrate fingerprint technique with a fully automated fast detection method of the total sugar content, based on absorbance measurement.
The phenol-sulfuric-acid method still represents the method of choice for the fast determination of total carbohydrate content of bacterial and plant polysaccharides7,8. This method was first described by Dubois et al.9 and adapted for different applications and sample sizes, even for small scale in 96-well plates10,11. The phenol-sulfuric-acid method measures the total carbohydrate content by one value, summating all monomeric, oligomeric and polymeric carbohydrates of the samples.
Taking these aspects into account, the choice of a suitable cultivation medium is essential to apply this method. Complex media containing oligomeric or polymeric carbohydrate compounds like yeast extract might lead to an altered polymer content and therefore, should strictly be avoided. Furthermore, high amounts of sugars are used as C-source for the cultivation of the strains. High levels of remaining carbohydrates from the cultivation process might negatively interfere with the measurement of the EPS content.
Therefore, the use of defined and pure sugars is advised. In our experiments we used glucose for the cultivation of the cells. The remaining glucose after cultivation was reduced via a gel-filtration and determined via a glucose-assay. Finally, the glucose equivalents of the polysaccharides were calculated by subtracting the remaining glucose after the gel-filtration from the total carbohydrate content that had been detected with the phenol-sulfuric-acid method. Gel-filtration and glucose-assay in combination with the phenol-sulfuric-acid method ensure reliable results and are capable of being our first, completely automated detection system.
Two new analytical modules were included in the automated screening platform to increase the amount of information from the EPS-detection system: the precipitation and the observation of an increase in viscosity.
Many different EPS — e.g. succinoglycan, xanthan and colonic acid12 — are reported to be modified with a non-carbohydrate pyruvate ketal on sugar positions C4 and C6. Those pyruvate ketals (just as succinyl half esters and uronic acids) contribute to the polyanionic nature and therefore, to the physical properties of the polymer by interacting via divalent cation bridges13. In order to identify those particular polymers the determination of pyruvate was established as another additional analytical module. This increases the information about the polysaccharide substituents and their potential macroscopic properties.
Combining all the modules enables the identification of different EPS as well as a fast and efficient determination of EPS producers. By that approach the screening platform could be divided into two major parts (Figure 1). Within the automated screening (part I) the workflow occurs fully automated (Table 1) to quickly preselect the EPS producing strains. The carbohydrate fingerprint analysis (part II) quantitatively determines the monomer composition of the EPS produced by the preselected strains. Thereby, the data analysis was minimized in order to optimize the screening of large strain collections. This offers the possibility to analyze 384 strains in one single automated screening run and with two runs that are possible per day of 768 strains per day. Additionally, the carbohydrate fingerprint analysis gives an even more detailed overview of all the identified EPS. This enables the directed analysis and identification of only slightly differing EPS variants or completely new ones compared to the already described chemical structures of EPS.
1. Automated Screening
Note: All liquid handling steps are done with a robotic liquid handling system. The composition of the robot worktable is presented in Figure 2. All consumables are stored in the storage carousel, unless mentioned otherwise. For all the automated screening steps a robotic manipulator (RM) moves the consumables, (deep well plate (DWP); micro titer plate (MTP); polymerase chain reaction plate (PCR-plate) and so on) between the carousel positions (CP) and the worktable positions (WTP). All the pipette steps are performed with a 96-channel-pipette-arm, except if it is mentioned otherwise. All steps are programmed and are performed automatically in 96-well format.
2. Carbohydrate Fingerprint
Note: All steps for the carbohydrate fingerprint are manually performed.
The validation of the phenol-sulfuric-acid method showed good results with a coefficient of determination (r²) of 0.9998 (Table 2). For the 5 g/L concentration the coefficient of variation (CV) and the accuracy showed a good performance with 1.8% and 2.2% error, but lower performance for the 0.25 g/L standard with 5.3% (CV) and 6.1% error (Bias).
The coefficients of determination of both pyruvate-assay calibration curves (with and without matrix) were >0.9999 in a calibration range of 150 µM (Table 3). The coefficients of variation (CV) for the highest and lowest calibration level were <4.6% and the accuracy showed a very good performance over the complete calibration range with less than 3.9% error. Thus, the matrix from the hydrolysis step showed no influence on the enzymatic assay, which is therefore capable to measure pyruvate before and after hydrolysis.
Table 4 shows the detailed results of three exemplary novel strains as successfully identified with the screening platform. The left part of the table displays the results of the automated screening modules concerning viscosity formation, polymer production and the glucose equivalent from the total hydrolysis which were used as evaluation parameters for detailed carbohydrate fingerprint analysis. The carbohydrate fingerprint based on calibrated sugars as well as unknown sugars, dimers and substituents are given in the right part of the table. By use of this information the monomeric composition can be calculated and compared with already known polymer structures. Furthermore, a targeted screening for interesting monomeric compositions and rare carbohydrates can be performed.
The high performance of the micro scale hydrolysis and the HT-PMP-derivatization were demonstrated in our previous work14. Furthermore, the validation of the gel-filtration and the carbohydrate fingerprint for various genera have been described in another publication6. In sum, the screening platform with its modular structure can easily be modified and adapted to individual requirements of the user. The automated screening of the platform enables an eight times higher throughput and gives reliable results. Novel analytical modules like the pyruvate-assay can be integrated and in combination with the carbohydrate fingerprint analysis they provide very detailed information about the identified EPS. Thereby, the screening platform is essential when searching for both slightly modified and completely novel EPS variants.
Figure 1: Overall scheme of the modular high-throughput exopolysaccharide screening platform. The automated screening includes the first three tasks. After bacteria are cultivated in 96-well plates, cells are removed by centrifugation (task 1) and a 96-well filtration (task 2). Then, the remaining monomeric sugars from the growth media are removed via a 96-well gel filtration (task 3). The EPS containing samples are evaluated in task 4. The carbohydrate fingerprint of the screening platform contains the last three tasks. The remaining filtrate of the positive hits from task 2 provides the basis for the gel-filtrate in task 5. After hydrolyzation in task 6 the carbohydrate fingerprint can be analyzed via the HT-PMP method (high-throughput 1-phenyl-3-methyl-5-pyrazolone, task 7). All tasks are followed by different analytical modules and/or a viscosity control. Please click here to view a larger version of this figure.
Figure 2: Robot worktable setup for the screening platform. Layouts of both liquid handling robot worktables are shown: (A) robotic liquid handling system and (B) liquid handling station. (A) The setup consists of a microplate carrier with two positions (positions 1-1 to 1-2), a carrier for disposable tips with four positions (positions 2-1 to 2-4) and three microplate carriers with four positions each (positions 3-1 to 3-4, 4-1 to 4-4 and 5-1 to 5-4). In addition, there is a storage carousel with five hotel carriers (1 to 5) each for seven deep well plates (DWP) and four hotel carriers (6-9) each for 21 micro-titer-plates. The hardware installed on the liquid handling robot is a 96-channel-pipette-arm for use with disposable tips and a robotic manipulator (RM) that moves plates/equipment between the worktable, the storage carousel, the MTP-reader, the centrifuge and the shaking-incubator. (B) The liquid handling station is equipped with a liquid handling arm and an 8-channel 300 µl pipette, a waste container at position 1, a tip adapter with 300 µl tips (position 2), a height adapter 30 with a 250 ml trough (position 3) and five height adapter 60 for MTPs (position 4 to 8). The numbering of the positions is referred to throughout this protocol. Alternative worktables can also be used if there are equivalent setups available. Please click here to view a larger version of this figure.
Figure 3: Pyruvate content of 16 commercially available polymers determined via pyruvate-assay. After the 1 g/L polymer solutions were hydrolyzed and neutralized, the pyruvate-assay was performed from a 1:10 dilution (n = 3). Please click here to view a larger version of this figure.
Figure 4: Flow chart of the modular high-throughput screening platform. The automated screening system, which combines different polysaccharide detection modules: analysis of viscosity formation, polymer production and determination of the total carbohydrate content. The second part provides a detailed monosaccharide analysis for all the selected EPS producers identified in the first part. All data from the automated screening and the data from the carbohydrate fingerprint via UHPLC-ESI-MS are collected in a database and enable the simple identification of structurally related variants of already known EPS or novel EPS and therefore, a targeted screening. Please click here to view a larger version of this figure.
Main step / Analytical module | Workflow | Observation / Description |
Cultivation of the strains | 1 ml EPS-mediuma Pre-culture 48 hr, 30 °C, 1,000 rpma Main-culture 48 hr, 30 °C, 1,000 rpma |
Production of EPS |
Cell removal / viscosity | Centrifugation: 30 min at 4,300 x g | No pellet = increased viscosity = positive |
Detection of Polymer: Precipitation | 50 µl supernatant + 150 µl 2-propanolb Shaking 10 min at RT and 900 rpmb |
Visual: Fibers and flakes = positive precipitation of polymer |
Cell removal / high viscosity | 180 µl supernatant of main-culture Centrifugation: 10 min at 3,000 x g 1.0 µm glass fiber membrane |
No filter passing = high viscosity = positive |
Detection of Polymer: Precipitation | 50 µl filtrate + 150 µl 2-propanolb Shaking 10 min at RT and 900 rpmb |
Visual: Fibers and flakes = positive precipitation of polymer |
Glucose consumption: Glucose-assay |
Dilution 1:100: 10 µl filtrate + 990 µl ddH2O 50 µl aliquot + 50 µl reagent-mix Incubation 30 min at 30 °C 150 rpm Measurement 418-480 nm |
Remaining glucose after cultivation |
Gel-filtration | Equilibration: 3 x 150 µl NH4-acetat buffer pH 5.6 2 x 2 min at 2,000 x g 1 x 2 min at 1,000 x g Gel-filtration: 35 µl filtrate, 2 min at 1,000 x g Washing: 3 x 150 µl ddH2O, 2 min at 2,000 x g 75 µl 20% ethanol for storage |
Polymer purification: Removal of salts, pyruvate, glucose and other sugar monomers from cultivation supernatant |
Remaining glucose after gel-filtration Glucose-assay |
Dilution 1:10: 25 µl ddH2O + 20 µl ddH2O and 5 µl filtrate + 50 µl reagent-mix Incubation 30 min at 30 °C, 150 rpm Measurement 418-480 nm |
Subtraction of remaining glucose after gel-filtration from the phenol-sulfuric-acid method |
Glucose equivalent: Phenol-sulfuric-acid methodc |
20 µl gel-filtrate + 180 µl phenol-sulfuric-acid (30 µl 5% (w/v) phenol in ddH2O + 150 µl conc. H2SO4 (ρ = 1.84 g/ml)) Shaking 5 min at 900 rpm Incubation 35 min at 80 °C Measurement at 480 nm |
Glucose equivalent: Δ (phenol-sulfuric-acid value – remaining glucose after gel-filtration) <300 mg/L negative >300 and <700 mg/L putative positive >700 mg/L positive |
a Handled manually under sterile conditions (laminar flow). | ||
b Flammable liquid handled manually under a fume hood. | ||
c Phenol-sulfuric-acid handled with Brand Liquid Handling Station (LHS) under a fume hood. |
Table 1: Complete workflow of the automated prescreening with the robotic liquid handling system and the liquid handling station. Overview of all parameters for the automated analytical modules.
Linearity | LOD | LOQ | ||
r²a | Slopea | Offseta | mg/L | mg/L |
0.9998 | 0.0007 | -0.021 | 50 | 100 |
Standard | Meanb | Precisionb | Accuracyb | |
mg/L | mg/L | CV% | Bias (%error) | |
5,000 | 5,112 | 1.8 | 2.2 | |
250 | 265 | 5.3 | 6.1 | |
a Mean of eight measurements, calibration with six levels glucose from 0.1 to 5 g/L | ||||
b Performed with a Student’s t-test (α = 0.05; n = 8). | ||||
LOD: limit of determination, LOQ: limit of quantification, CV: coefficient of variation. |
Table 2: Validation of the phenol-sulfuric-acid method was carried out with the liquid handling station. The linearity was calculated based on a six point calibration (n = 8). Mean, precision and accuracy of two exemplarily chosen glucose concentrations are given here.
Linearity | LOQ | |||
r²a | Slopea | Offseta | µM | |
without matrix | 0.99999 | 0.0223 | -0.0019 | 1 |
1:10 diluted matrix | 0.99999 | 0.0221 | -0.0011 | 1 |
Standard | Meanb | Precisionb | Accuracyb | |
µM | µM | CV% | Bias (%error) | |
without matrix | 50 | 49.96 | 3.05 | -0.09 |
1 | 1.04 | 2.95 | 3.86 | |
1:10 diluted matrix | 50 | 49.98 | 0.44 | -0.04 |
1 | 1.00 | 4.58 | 0.33 | |
a Mean of three measurements, calibration with six concentrations of pyruvate from 1 to 50 µM. | ||||
b (n = 3) | ||||
LOQ: limit of quantification, CV: coefficient of variation. |
Table 3: Validation of the pyruvate-assay with and without a 1:10 diluted neutralized trifluoroacetic-acid-matrix. Two six point calibrations (n = 3) with and without evaluation of matrix influences were performed. Mean, precision and accuracy of two exemplarily chosen pyruvate concentrations with and without effects of a 1:10 dilution were calculated.
Table 4: Results of three exemplary strains screened with the platform. Data collected from the automated screening and the carbohydrate fingerprint. Please click here to download this table as a Microsoft Excel file.
Carbohydrate | Absorption max [nm] | Absorption at 480 nm mean±SD | Absorbance relative to glucose [%] | |
Diutan gum | 470 | 0.342 | ±0.010 | 187 |
Gellan gum | 472 | 0.334 | ±0.002 | 183 |
Guar gum | 478 | 0.387 | ±0.017 | 212 |
Gummi arabic | 476 | 0.393 | ±0.034 | 215 |
Hyaluronic acid | 484 | 0.231 | ±0.011 | 126 |
Karaya gum | 478 | 0.455 | ±0.023 | 249 |
Konjac gum | 480 | 0.297 | ±0.009 | 163 |
Larch gum | 480 | 0.337 | ±0.032 | 185 |
Locust bean gum | 478 | 0.354 | ±0.033 | 194 |
Scleroglucan | 484 | 0.168 | ±0,010 | 92 |
Succinoglycan | 482 | 0.168 | ±0.005 | 92 |
Tara gum | 480 | 0.318 | ±0.016 | 174 |
Tragacanth | 478 | 0.513 | ±0.003 | 281 |
Welan gum | 472 | 0.226 | ±0.016 | 124 |
Xylan | 472 | 0.567 | ±0.007 | 311 |
Xanthan gum | 482 | 0.245 | ±0.021 | 134 |
Glucose | 484 | 0.191 | ±0.014 | 100 |
SD: standard deviation |
Table 5: Results as obtained by the phenol-sulfuric-acid method for 16 commercially available polymers and glucose. The absorption maximum and absorption at 480 nm of 16 commercially available polymers (1 g/L) as well as glucose (1 g/L) were measured applying the phenol-sulfuric-acid method. The absorbance relative to glucose of all the polymers was calculated.
Standard | Meana | Precisiona | Accuracya | |
mg/L | mg/L | CV% | Bias (%error) | |
1:10 dilution | 450 | 460 | 1.01 | 2.14 |
45 | 44.7 | 1.41 | -0.70 | |
1:100 dilution | 4,500 | 5,026 | 1.19 | 11.6 |
450 | 471 | 1.16 | 4.55 | |
b Performed with a Student’s t-test (α = 0.05; n = 8). | ||||
CV: coefficient of variation. |
Table 6: Validation of the automated dilution for the glucose-assay. The dilution for the glucose-assay after cultivation (1:100) and after gel-filtration (1:10) were validated. Two glucose concentrations (n = 8) were diluted via the liquid handling system and evaluated. Mean, precision and accuracy were calculated.
Theoretical glucose value | Covered with silicone cap mat | Test of evaporation (uncovered) | |||||
Meana | Precisiona | Accuracya | Meana | Precisiona | Accuracya | Evaporation | |
mg/L | mg/L | CV % | Bias (%error) | mg/L | CV % | Bias (%error) | %error |
45.0 | 45.2 | 0.69 | 0.44 | 46.0 | 0.66 | 2.05 | 1.60 |
18.0 | 17.7 | 0.80 | -1.68 | 18.0 | 0.72 | -0.01 | 1.69 |
9.0 | 8.74 | 1.20 | -2.98 | 8.92 | 0.81 | -0.95 | 2.09 |
4.5 | 4.50 | 1.26 | -0.04 | 4.58 | 1.57 | 1.76 | 1.80 |
1.8 | 1.85 | 0.74 | 2.90 | 2.01 | 2.82 | 11.6 | 8.48 |
0.9 | 1.03 | 1.43 | 14.1 | 1.16 | 3.52 | 28.3 | 12.4 |
a (n = 4) | |||||||
CV: coefficient of variation. |
Table 7: Evaluation of the evaporation effect of covered and uncovered MTP. Six different glucose standards (n = 4) were stored in the carousel for 3.5 hr at room temperature. The effect of the evaporation was evaluated by using uncovered as well as covered (silicon mat) standard samples. Mean, precision, accuracy and the evaporation in % error were calculated.
Before gel-filtration | After gel-filtration | Remaining glucose after gel-filtration | |||
Meana | SDa | Meana | SDa | ||
mg/L | mg/L | mg/L | mg/L | % | |
1 | 8,647 | 110 | 259 | 121 | 3.00 |
2 | 5,108 | 56 | 116 | 37 | 2.27 |
3 | 2,014 | 12 | 50.8 | 14 | 2.52 |
4 | 1,015 | 12 | 25.1 | 8.1 | 2.47 |
5 | 510 | 4.9 | 12.8 | 4.3 | 2.51 |
6 | 223 | 8.6 | 6.6 | 1.5 | 2.94 |
7 | 122 | 5.6 | 4.3 | 0.9 | 3.48 |
8 | 75 | 6.0 | 3.1 | 0.3 | 4.18 |
a (n=8) | |||||
SD: standard deviation |
Table 8: Results of the gel-filtration efficiency. Eight different glucose standards were determined before and after gel-filtration to evaluate the efficiency of the gel-filtration. Mean, standard deviation and remaining glucose after gel-filtration in % were calculated.
Polysaccharide detection with the phenol-sulfuric-acid method: Different monosaccharides show different absorption maxima and molar extinction coefficients by use of this method9. This results in different absorption maxima of polymers, which contain several sugars in different amounts. The different wavelengths of absorption maxima for 16 different commercially available polymers are given in Table 5. The polymers were dissolved (1 g/L) in ddH2O, stirred (150 rpm) overnight and measured with the phenol-sulfuric-acid-method. Diutan gum showed the lowest absorption maxima at 470 nm and scleroglucan and hyaluronic acid the highest at 484 nm. Based on these results 480 nm was chosen for this screening platform. The relative absorbance of the polymers was calculated based on the absorbance obtained with 1 g/L glucose (set as 100%). The lowest results were obtained with scleroglucan and succinoglycan, both with 92%. This was expected because scleroglucan only contains glucose and succinoglycan contains glucose and galactose in a ratio of 7:1. Both commercial polymers have different losses of drying and different ash contents, this is the reason why the theoretical value of ~110% was not reached. Xylan showed the highest relative absorbance with 311%. The reason for this is the high molar extinction coefficient achieved from xylose due to the more dominant furanose form. At a level of 0.1 g/L glucose the quantification limit was reached, as well as the detection limit at a concentration <0.05 g/L. However, the detection limit for positive strains in the screening is higher than 0.7 g/L and therefore, the assay showed a good performance. In order to get reliable results, the remaining glucose after gel-filtration was determined with a glucose-assay and this value was subtracted from the value from the phenol-sulfuric-acid method.
Automated glucose-assay dilution: The performance of the glucose determination after cultivation (dilution 1:100) was investigated. For this, 10 µl of supernatant were transferred to 990 µl of ddH2O in a deep well plate and mixed via ten times aspirating and dispensing 180 µl out of this dilution. The second critical step was the correct pipetting of only 5 µl aliquot for the 1:10 dilution from the glucose-assay after gel-filtration. In order to generate the dilution 25 µl of ddH2O were transferred with a 50 µl-tip first, afterwards 20 µl of ddH2O and 5 µl gel-filtrate were aspirated together. This ensures a better removal of the 5 µl aliquot out of the tip. Both dilution steps were verified with various glucose standards via a glucose-assay. The results for two exemplary concentrations are given in Table 6. The 1:100 dilution for the determination of the glucose content after cultivation showed high precision for both standards with a CV <1.2%. At the same time, the accuracy for the higher standard was up to 11.6 (% error). However, this is negligible as the glucose determination represents only the remaining glucose content after cultivation and therefore, is not important for the polymer detection. The 1:10 dilution for the remaining glucose after gel-filtration showed very reliable results with a CV <1.4% and an accuracy <2.1% error.
Consideration of evaporation: The screening requires 3.5 hr from the first step to the first glucose-assay. In order to find out, whether this time frame has an influence on uncapped MTP storage, 50 µl of glucose-assay calibration standards were stored with and without cover for 3.5 hr in the robot carousel. In the calibration range (45 to 4.5 mg/L) the sample concentration hardly increased. An increase — caused by evaporation — was below 2.1% and only for the two lowest concentrations (1.8 and 0.9 mg/L) it reached up to 12.4% (Table 7).
Gel-filtration: High amounts of non-metabolized glucose disturb the quantitative detection of glucose from the hydrolyzed polymer. Therefore, a gel-filtration step was required to remove the remaining glucose after cultivation. Additionally, the gel-filtration purifies the polymer containing supernatant from salts and monomeric carbohydrate compounds, others than glucose, to minimize the analytical background in the monomer analysis. At the gel-filtration step 35 µl of filtrate were placed in the center of the well. For validation of the robustness of gel-filtration in the automated system, eight calibration standards from 0.045 up to 9 g/L glucose were filtrated (n = 8). The glucose of every concentration was always reduced by more than 95% of the initial value (Table 8). In doing so, the gel-filtration showed very good results for various concentrations of glucose. Additionally, the remaining glucose after gel-filtration was also determined with a glucose-assay and subtracted from the phenol-sulfuric-acid determination to receive the correct amount of glucose equivalent for the hydrolyzed polymer.
Pyruvate-assay: First of all, it was investigated whether the neutralized and diluted (1:10) TFA-matrix from the hydrolysis step interferes with the enzymatic reaction. Therefore, the complete assay was performed twice, one time with and one time without matrix and showed reliable results. Finally, the pyruvate content of 16 commercially available polymers was successfully measured and is depicted in Figure 3. It is generally known that out of those 16 polymers only succinoglycan and xanthan naturally contain pyruvate. With our pyruvate-assay both of these polymers were correctly identified. In scleroglucan, welan gum and xylan pyruvate was also detected in significant amounts. Overall, the capability of the approach was validated and the pyruvate-assay showed a high performance. It proved to be able to detect pyruvate in different polymers after hydrolysis.
Carbohydrate fingerprint: After performing all analytical modules in the automated screening, potential EPS producers were selected for the carbohydrate fingerprint analysis. For this, several criteria were applied: 1) Positive observation of viscosity after centrifugation and/or after filtration. 2) Precipitation before and after filtration. Observed fibers and flakes were evaluated as positive. 3) Glucose equivalent value from the phenol-sulfuric-acid method. Values >700 mg/L were rated as positive and values between 300 and 700 mg/L were rated as putative EPS producers. When two or three criteria were evaluated as positive, the strains were selected for further carbohydrate fingerprint analysis. The criteria can be customized towards the individual purpose of the EPS screening (e.g. low viscosity EPS). Our approach aimed at finding efficient EPS producers. When searching for strains that only produce small amounts of EPS the evaluation limit of the glucose equivalent should be reduced.
Technical benefit and future applications: One interesting feature of this protocol is the modular character of the steps and the different analytical modules. They can be combined in different ways, adjusted to individual requirements and novel modules can easily be implemented. Furthermore, the analytical modules can be used separately, e.g. the hydrolysis module in combination with the HT-PMP-derivatization module is able to perform a monomeric composition analysis from different polymer solutions (1 g/L) in 96-well format. For laboratories without having access to a liquid handling system the complete screening can be handled manually without any changes in the pipetting scheme. However, using a liquid handling system increases the throughput to up to 768 strains (instead of 192 strains if screened manually) per day. The protocol that is described here is capable of a screening for different genera and therefore, for the screening of large strain collections to identify novel EPS producers and analyze their carbohydrate fingerprint in one approach (Figure 4). Furthermore, a targeted screening for polysaccharides containing rare sugars like fucose, uronic acids or even unknown sugars can be performed via the detailed monosaccharide analysis. Also, different sugar combinations in defined ratios can be detected. This enables the simple identification of structurally related variants of already known EPS or novel EPS.
The authors have nothing to disclose.
We sincerely thank Thomas Howe and Jörg Carsten for the programming and technical support with the liquid handling systems.
96 well deep well plate 2.0 mL (DWP) | Greiner Bio-One | 780271 | Main-culture (CP 1-1 to 1-4), equilibration plates for gel-filtration (CP 1-6, 1-7, 2-6 and 2-7), 1:100 dilution for the glucose assay (CP 4-1 to 4-4) containing 990µl of ddH2O |
Breathable Sealing Film | Axygen | BF-400-S | Incubation film for pre- and main-culture DWP |
Aluminum Sealing Film | Axygen | PCR-AS-200 | -80 °C storage film for glycerol-stock plates |
MCA96 Nested Disposable Tips 200 µl | TECAN | 30038619 | Worktable position (WTP 2-1 to 2-4) |
A/B glass-filter-plate 1µm | Pall Corporation | PN 8031 | Stored on collector plate (CP 2-1 to 2-4) |
96-well micro titer plate V-Bottom | Greiner Bio-One | 651201 | Collector plate for filtration plate (CP 2-1 to 2-4) |
96-well SpinColumn G-25 | Harvard Apparatus | 74-5612 | Stored on washing DWP (CP 1-6, 1-7, 2-6 and 2-7) |
96-well micro titer plate V-Bottom | Nunc | 249944 | Collector plate for gel-filtration plate (CP 3-1 to 3-4) |
Nested Disposable Tips SBS 50 µl tips | TECAN | 30038609 | Carousel position (CP 4-6, 4-7 and 5-1 to 5-6) |
Trough 250 ml | Axygen | Res-SW96-HP | Water WTP 1-1, Glucose assay reagent-mix WPT 1-2, ammonium-acetat buffer pH 5.6 (CP 5-7) |
96-well micro titer plate F-Bottom (MTP) | Greiner Bio-One | 655101 | precipitation 1 (CP 6-1 to 6-4), pH-value (CP 7-1 to 7-4), precipitation 2 (CP 8-1 to 8-4), phenol-sulfuric-acid method (CP 8-5 to 8-8), glucose-assay (CP 9-1 to 9-9), dummy plates (WTP 3-1, 3-2) |
96-well silicon cap mat | Whatmann | 7704-0105 | Cover mat for MTP |
200 µl pipette tips | Sarstedt | 70.760.002 | For manually handling |
1000 µl pipette tips | Sarstedt | 70.762 | For manually handling |
96-well-PCR micro titer plate | Brand | 781350 | Hydrolysis, PMP-derivatisation |
TPE (thermoplastic elastomer) cap mat | Brand | 781405 | Hydrolysis, PMP-derivatisation |
Filter plate 0.2 µm Supor, | Pall Corporation | PN 8019 | Filtration of samples for UHPLC-ESI-MS analysis with a MTP collector plate |
Pipette Tips LHS 5-300 µl | Brand | 732150 | Brand LHS system |
ABTS (2.2-azino‑bis-(3‑ethylbenzthiazoline)-6-sulfonic acid) | Sigma-Aldrich | A1888 | Glucose-assay |
Glucose oxidase | Sigma-Aldrich | G2133 | Glucose-assay |
Horseradish peroxidase | Sigma-Aldrich | P6782 | Glucose-assay, pyruvat-assay |
DA-64 (N-(Carboxymethylaminocarbonyl)-4.4'-bis(dimethylamino)-diphenylamine sodium salt) | Wako Chemicals GmbH | 043-22351 | Pyruvat-assay |
Pyruvate oxidase | Sigma-Aldrich | P4591 | Pyruvat-assay |
Potassium phosphate dibasic | Carl-Roth | P749.3 | Pyruvat- and glucose-assay |
Potassium phosphate monobasic | Carl-Roth | 3904.3 | Pyruvat- and glucose-assay |
Thiamine pyrophosphate | Sigma-Aldrich | C8754 | Pyruvat-assay |
Magnesium chloride hexahydrate | Sigma-Aldrich | 31413 | Pyruvat-assay |
2-Propanol | VWR | 20922.466 | Precipitation |
Phenol | VWR | 20599.231 | Phenol-sulfuric-acid method |
Sulfuric acid | Carl-Roth | 4623.4 | Phenol-sulfuric-acid method |
Trifluoroacetic acid | Sigma-Aldrich | T6508 | Hydrolysis |
Ammonium solution | Carl-Roth | P093.1 | Hydrolysis, PMP-derivatization |
Ethanol absolut | VWR | 20821.321 | Hydrolysis, PMP-derivatization |
Phenol red | Alfa Aesar | B21710 | Hydrolysis, PMP-derivatization |
1-Phenyl-3-methyl-5-pyrazolone | Sigma-Aldrich | M70800 | PMP-derivatization |
Methanol LC-MS | VWR | 83638.320 | PMP-derivatization |
Acetonitril LC-MS | VWR | 83040.320 | PMP-derivatization |
Acetic acid | Sigma-Aldrich | 338826 | PMP-derivatization, |
Ethanol absolut | VWR | 20821.321 | PMP-derivatization |
Methyl red | Alfa Aesar | 36667 | pH-value |
Robotic liquid handling system | Tecan | Freedom EVO | Worktable setup in Figure 2 |
Liquid handling station LHS | Brand | 709400 | Worktable setup in Figure 2 |
Tip-Adapter | Brand | 709434 | Worktable setup in Figure 2 |
Liquid Ends MC 20-300µL | Brand | 709416 | Worktable setup in Figure 2 |
Adapter 60mm | Brand | 709430 | Worktable setup in Figure 2 |