community analysis by biolog.pdf

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ELSEVIER Journal of Microbiological Methods 27 (1996) 183-197

JournalMicrobiologicalMethods

Community analysis by Biolog: curve integration for statisticalanalysis of activated sludge microbial habitats

James B. Guckerta*, Gregory J. Carrb, Troy D. Johnsonb, Burt G. Hamm,Daniel H. Davidsona, Yoshiharu Kumagai

Environmental Science Department, Ivorydale Technical Center, The Procter and Gamble Company, Cirtcinnati, OH 45217- 1087, USAbBiostatistics and Medical Surveillance Department, Miami Valley Laboratories. The Procter and Gamble Company, Cincinnati, OH

45253-8707, USAProfessional and Regulatory Services-Asia, Kobe Technical Center, The Procter and Gamble Company, Kobe. Japan

Received 19 August 1996; revised 19 September 1996; accepted 19 September 1996

AbstractBiolog MicroPlates are 96-well plates that contain pre-dried carbon sources and a tetrazolium violet redox dye that turns

purple if added microorganisms utilize the nutrients. We have measured absorbance changes due to tetrazolium dye responsefor microbial communities found in activated sludge and laboratory models of these wastewater communities. To analyze theabsorbance versus time data, we export and organize the data into spreadsheets and use a trapezoidal approximation todetermine the area under the absorbance versus time curve for each well. The Excel-based trapezoidal approximation hasbeen shown to be in agreement with a more complex curve fitting routine that used SAS to fit a log-logistic function to eachcurve, and then determined the area under each curve. This data analysis procedure has the advantage of collapsing theabsorbance versus time curves down to a single value that integrates information from the entire incubation period. Thissingle value incorporates the lag phase, the rate of development and the extent of dye development for each well. The areaunder the curve is then used for statistical testing to compare individual carbon source utilization, or in a multivariate patternanalysis of community metabolism of the Biolog MicroPlate carbon sources. Examples of the use of this analysis are givenfor microbial communities of activated sludge and laboratory models of activated sludge maintained with different types offeedstocks.Keywords: Activated sludge; Biodegradation testing; Biolog; Microbial ecology; Ministry of International Trade andIndustry; Statistics; Wastewater treatment

1. IntroductionBiolog MicroPlates are 96-well plates that contain

pre-dried carbon sources and a tetrazolium violetredox dye that turns purple if the added micro-organisms utilize the carbon source [l]. When iso-*Corresponding author. Tel: + 1 513 6263373; fax: + 1 513

6263522: email: [email protected].

lated strains are evaluated, the pattern of responsesover a Biolog plate can be compared to databases toestablish probable identifications [2]. There are sev-era1 different arrays of carbon sources designed tooptimally identify Gram-negative (GN Biologplates), Gram-positive isolates (GP Biolog plates) oryeasts (YT Biolog plates). In addition, empty plates(MT Biolog plates) are available that contain nocarbon sources, but do contain the tetrazolium dye.

0167-7012/96/$15.00 Copyright 0 1996 Elsevier Science B.V. All rights reservedPIZ SO167-7012(96)00948-7


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184 J.B. Cuckert et al. I Journal of Microbiological Methods 27 (1996) 383-197

Biolog plates were originally designed to be evalu-ated after a 4 or 24 h incubation and compared withestablished databases of pure cultures [3,4].

Recently, microbial ecologists have been usingBiolog plates to investigate carbon-source utilizationpatterns for mixed microbial communities. Thistechnique has been used with soil microbial com-munities [5-S], aquatic samples [5], [D.M. Lee andJ.B. Guckert, unpublished data] and wastewatertreatment communities [9], [and this report]. Inaddition, the MT Biolog plates [lo], which containno added carbon sources, have been used to screenbacterial strains for their ability to biodegrade toxicorganics [l l] and surfactants [12].

Community-level Biolog analysis is accomplishedin four steps: (1) process the sample to create asuspension of microorganisms, (2) inoculate theBiolog plate(s) with aliquots of the suspensions, (3)incubate the plate while monitoring color develop-ment in each well, and (4) analyze the results.Victorio et al. [9] has published a comparison ofsample processing techniques for wastewater acti-vated sludge and has concluded that homogenizationproduced the most representative community withthe best recovery. Inoculum cell density has beenshown to influence color development in Biologplates [5,6,8]. A minimum number of metabolicallyactive cells (about lO*/ml based on [8]) are requiredto produce an observable color change. This hasresulted in the recommendation (e.g. [S]) that allmicrobial suspensions be adjusted to a standardizedcell density prior to inoculation into Biolog plates.This is the approach suggested by Biolog, Inc. [3,4]when the plates are used to identify pure cultures. Analternative that has been proposed [5] is to adapt thedata analysis to account for different inoculumdensities. A standardized reference point, the Aver-age Well Color Development (AWCD), is calculatedas the mean of the absorbance values for all 95response wells per reading time [5]. All well re-sponses are then normalized to the AWCD for eachplate to account for different inoculum densities [6].

Although color development in Biolog plates isoften monitored many times over the course of anincubation period, data analysis has been generallylimited to a few selected time points for microbialcommunity analyses [5-91. The absorbance versusincubation time curves contain additional informa-

tion not available from any single time point analy-sis; such as, lag times, rates of color developmentand maximum absorbance. Several researchers havediscussed the probable utility of an analysis thatcould take into consideration this additional infor-mation [6,8]. In this report, we describe a newapproach to Biolog data analysis that incorporates allof the additional information from the absorbanceversus incubation time curves into a single numberusing a PC-based curve integration analysis. Datasets for several microbial communities (describedbelow) based on this new approach are then analyzedusing both univariate and multivariate analyses. Onthe basis of our results, the use of curve integrationprovides a powerful tool for the analysis of microbialcommunity metabolic patterns.

Several researchers have discussed limitations ofthe Biolog method, especially related to the differen-tial growth of microorganisms that occurs in theindividual Biolog wells [6-81. While we acknowl-edge that changes in community structure can occurduring Biolog incubations, we interpret all Biologresults as a function of the original microbial com-munity structure from the sample. The rate andextent of utilization of any particular carbon sourceon a Biolog plate will be related to the originalmicrobial community structure and metabolic capaci-ty for that sample. Since the community will have asingle carbon source to utilize in each well of theBiolog plate, the microbial community will likelychange independently in each of these wells. How-ever, the measured Biolog response is still related tothe functional potential of the original community[5]. Therefore, we believe that a comparison ofmicrobial communities based on the 95-carbonsource array available from a Biolog plate is anappropriate relative measure of the metabolic diversi-ty of these communities. The results discussed in thisreport indicate significant shifts in carbon sourceutilization patterns that are relevant to the researchquestions being addressed for microbial communitiesof fresh activated sludge as compared to laboratorymodels of these communities, as described below.

Wastewater treatment plants (WWTPs) are im-portant microbial habitats that remove wastes by acombination of biological and physical processes. Tofacilitate biological removal, WWTPs provide amanaged habitat for a complex microbial community


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J.B. Guckert et al. / Journal of Microbiological Methods 27 (1996) 183-197 18.5that is maintained to breakdown biodegradablewastes [13]. Although this biodegradation of wastesis of great importance to human society and receiv-ing stream water quality, there has been minimalresearch into the microbial ecology of these habitats[9]. Recently, the microbial ecology of these com-munities has been evaluated with such techniques asgene probes [14], enzyme activity profiles [15], andBiolog [9].

For the purpose of regulatory clearance of newchemicals, such as consumer products ingredients,sludge from a WWTP is used to evaluate thebiodegradability of these new chemicals. It is nor-mally stipulated that the WWTP should treat pre-dominantly domestic sewage, and be free from majorspecific pollution. The sludge used in the Organiza-tion for Economic Cooperation and Development(OECD)-Japanese Ministry of International Tradeand Industry (MITI) biodegradation test (3OlC),however, is different from other OECD ready-biodegradation tests [16]. This test requires that theWWTP sludge microbial community initially col-lected be maintained in a Semi-Continuous ActivatedSludge (SCAS) unit [17] on a glucose and peptonefeed for at least I. month before biodegradationtesting can begin. A.s part of a global, collaborativeprogram evaluating an array of methods for thecommunity-level changes that occur during the MIT1cultivation process, 13iolog was selected as a measureof changes in metabolic diversity. The results shownin this report highlight our new Biolog data analysisprocedure, using the comparisons of carbon-sourceutilization profiles for fresh WWTP sludge withSCAS unit communities maintained on fresh sewageor synthetic feeds (such as the MIT1 glucose andpeptone).

2. Materials and methods2.1. Waste Water T,reatment Plants (WWTPs)sampled

Three activated sludge WWTPs in the GreaterCincinnati area were sampled for activated sludge.These WWTPs discharge into the watershed of theLittle Miami River, OH, USA 1181. In each case avolume of sludge was collected and transported

directly to Procter and Gamble Laboratories foranalysis. The Middle East Fork WWTP is a 7 milliongallons a day secondary treatment plant. Middle EastFork serves a residential population of over 10 000in Clermont County, OH, with about 15% of its flowfrom industries. The Polk Run WWTP is a secondarytreatment plant with a capacity of about 6 milliongallons a day. Polk Run services a rapidly growingarea of northern Cincinnati (Hamilton and Warrencounties) with about 5% of its influent coming fromindustry. The Sycamore WWTP is a secondarytreatment plant with a capacity of about 6 milliongallons a day. Sycamore serves a population of over30 000 in Hamilton County, with industrial contribu-tions accounting for about 1% of its influent [ 181.Sufficient activated sludge was collected to allow1.5 1 of sludge for each laboratory unit (describedbelow) to be set up. Sludge was transported in acontainer with ample headspace to maintain aerobicconditions in the sludge. When possible, aeration wasmaintained during transportation or restored at theearliest opportunity. In all cases, sludge was set upwith aeration in the laboratory units within 24 h ofcollection.

2.2. Semi-Continuous Activated Sludge (SCAS)unit set-up

SCAS units are used as batch models of WWTPmicrobial communities to study WWTP processes,chemical fate and biodegradation potential [171.SCAS units used in this study consisted of Plexiglascylinders with a coned bottom to prevent settling ofsludge solids during aeration. The cylinders wereapproximately 60 cm tall with an inside diameter of8 cm. An air dispersion tube introduced air at a rateof 85 -+5 ml/min. The air was introduced at thelowest possible point in the cone to maximize thedispersion of air bubbles through the sludge (this isaccomplished by aiming the tube down at a 45 angletoward the bottom of the cone). Another tube, usedto drain and/or sample the unit, enters the unithorizontally at the 500 ml mark. This tube was keptclamped shut during normal operation to prevent lossof mixed liquor suspended solids. On 20 April 1995,each SCAS unit received 1500 ml Polk Run WWTPactivated sludge at a total suspended solids level of


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186 J.B. Gudert et al. I Journal of Microbiological Methods 27 (1996) 183-1972000-3000 mg/l. SCAS units were maintained on a23.5 h aeration schedule.2.3. SCAS unit feeding

Once a day, the air to the SCAS units was shut offand the solids were allowed to settle for 30 min. Thedrain line was then opened and the settled superna-tant allowed to drain from the unit down to the 500ml mark. The drain line was then clamped off, andthe units fed. Three different feeds were used. Theywere: (1) MIT1 Feed (2) Ludzack feed, and (3)Sewage feed. MIT1 Feed is based on the prescribedfeed required by the MIT1 Biodegradation Test [16].This synthetic feed consists of a stock solution of 1 geach of peptones, glucose and KH,PO, dissolved in2 1 of deionized water. After settling and draining ofthe supernatant, 1000 ml of the stock solution wasadded to the unit to bring the final volume to 1500ml and aeration resumed. Ludzack is another formu-lation of synthetic sewage ( [19] as cited in [20]).The stock solution consisted of 30 g n-glucose, 20 gnutrient broth, and 13 g K,HPO, dissolved in a finalvolume of 1 1 of deionized water. After settling anddraining of the supernatant, the SCAS unit is fed 20ml of this stock solution plus 980 ml tap water tobring the final volume to 1500 ml and aerationresumed. Sewage Feed was unmodified sewageobtained once a week from the gravitational thic-kener of the Polk Run WWTP. The gravitationalthickener is just downstream of the activated sludgebasins and provides a higher concentration of or-ganics to match the Chemical Oxygen Demandloading of the synthetic feeds (described above)used. After settling and draining, the unit was fed1000 ml of Polk Run WWTP sewage to bring thefinal volume to 1500 ml and aeration resumed. Allstock solutions and fresh sewage were stored at 4Cand warmed prior to use.2.4. Biolog analysis

Biolog is a 96-well MicroPlate technique original-ly designed to use metabolic patterns to identify purecultures of bacteria [l]. In this study, Biolog GN(Gram-negative), GP (Gram-positive), and MT(empty) MicroPlates were used to evaluate com-munity-level metabolic responses. GN [4] and GP

[3] Biolog plates contain different arrays of carbonsources along with different inorganic nutrients. MTplates [lo] do not contain any carbon sources, andhave been used as a negative control to ensure thatresponses noted for GN and GP plates are not due tocarry-over of carbon sources from the activatedsludge matrix. SCAS units were sampled on 8August 1995, 110 days after their set-up. Threeseparate samples were collected and analyzed fromeach SCAS unit. While the units were aerating, a20-50 ml aliquot of sludge was drained from eachSCAS unit. Grab samples of activated sludge werecollected on 18 December 1995 from the Polk Run,Middle East Fork and Sycamore WWTPs. Threeseparate samples were collected and analyzed fromeach WWTP. Sludge samples from SCAS units andWWTPs required additional preparation steps beforemicrobial communities could be inoculated intoBiolog MicroPlates.

Microorganisms are removed from the sludgesolids by homogenization using a Waring Commer-cial Laboratory Blender (model 34BL97, 1 min atspeed setting #2 at room temperature) with a 355 mlstainless steel attachment and stainless steel bladesand lid (Eberbach Corporation). Sequential extrac-tions by homogenization have recovered microbialcommunities with similar Biolog profiles, suggestingthat each extraction removes a representative portionof the intact sludge microbial community [9].Homogenized sludge samples were placed into 50 mlglass, screw-cap, centrifuge tubes. Suspended micro-organisms were then separated from the other materi-al by centrifugation using a Clay Adams Dynacbenchtop clinical centrifuge (7OOXg for 5 min atroom temperature). The resulting supernatant wasfairly turbid. The supernatant was removed bydecanting, or by pipette from below the surface ifsurface grease or oil was observed.

Following the established Biolog procedures[3,4,10], the supernatant was diluted with phosphatebuffer as necessary to get the turbidity to within arange of 0.25 to 0.35 absorbance units at 420 nmusing a Hewlett Packard 8452A Diode Array Spec-trophotometer. The stock solution of phosphatebuffer was 12.36 g Na,HPO,, 1.80 g H,PO, and85.0 g NaCl in 1 1deionized water, filtered (0.2 ,um)and stored at 4C. The working solution for dilutionsused 100 ml of this stock in 1 1 of deionized water.


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J.B. Guckert et al. I Journal of Microbiological Methods 27 (1996) 183-197 187

The suspension was then poured into a reagentreservoir and 150 b~l aliquots were added to eachwell of the Biolog MicroPlates using a multi-channelpipettor (Brinkman Transferpipette-12). Typically,one Biolog GN, GP and MT MicroPlate were set upfor each replicate sample. Biolog plates were evalu-ated for absorbance changes at 590 nm using aMolecular Devices !$PECTRAmax Model 250. Fol-lowing an initial (time 0) reading, Biolog plates wereincubated at 25C. The Biolog plates were read forabsorbance at 590 mn since respiration of the carbonsource in each well will cause the tetrazolium dye toturn purple and abssorb light at 590 nm. Plates areread periodically (usually 9-12 times) over a 3 dayperiod. For example, plates were commonly read at0, 17, 21, 25, 41: 45, 49, 65 and 69 h. Onerepresentative plate could also be read continuously(every 30 min) over the entire incubation time, ifdesired. This kinetic-style analysis could be extendedto all samples with the use of an automated platestacker/reader.2.5. Statistical analysis

Results were automatically saved into data filesusing Soft MAX Pro (version 1.2.0). Separate datafiles were created for each of the 9-12 readingsconducted on each 13iolog plate over the incubationtime. The data from these individual readings wascomplied into a single Excel spreadsheet for eachplate. An automated method to compile these filesalso provided a calc:ulation of area under the curvefor each well of the plates. Details of this analysisprocedure are discussed in Section 3 and AppendixA sections. Results Ibr analysis are expressed as netarea under the curve where the area under the curvefor well Al (water control) is subtracted from eachof the other 95 wells for a plate. In the developmentof this method, data files were first evaluated using acurve-fit routine available in SAS [21], as discussedfurther in Section 3.

Biolog results were first plotted by well number tovisualize trends. Profiles of Biolog results were alsoanalyzed by multivariate analysis with the patternrecognition software package Ein*Sight (version 2.5,Infometrix, Inc.). Principal component analysis(PCA) was conducted on the net area under the curvedata by first autoscaling the data to a mean of 0 and

constant variance. PCA was then used to evaluateseparations of samples based on the 95 carbon sourceutilization pattern. Scores for variable loadings wereevaluated to determine which carbon sources pro-vided the largest discrimination power, and theseindividual carbon sources were evaluated individual-ly. For the purposes of this report, no hypothesistesting (e.g. ANOVA) was conducted due to the levelof replication available in this example data set.Individual SCAS unit feeds were not replicated inseparate SCAS units, so replicates show levels ofvariability from pseudo-replicates rather than truetreatment effects [22].

3. Results and discussion3.1. Biolog absorbance curves

Analysis of microbial communities by Biologproduces a very large data set. Each of the 96-wellsper MicroPlate has 9- 12 absorbance measurementstaken over the 3-day incubation time. Therefore,there are 96 curves that need to be analyzed. Fig. 1uses selected data from one of the MITI-fed SCASunits to show how results from 24,48 and 65 h couldbe very different for the same Biolog plate. Ab-sorbance versus incubation time curves can be usedto develop summary information about the well colordevelopment in each well. The rate of increase inabsorbance might be considered, though it would notaccount for a lag phase. For example, 2-amino-ethanol (H7) and Tween 40 (A5) appear to havesimilar rates of increase, but because of differencesin the length of the lag phase, the overall utilizationof these carbon sources is very different (Fig. 1).Also, a maximum absorbance could be evaluated,though this will not indicate the rate of increase orduration of color development. For instance, 2-aminoethanol (H7) finishes the incubation at a higherabsorbance value than glycerol (H9), but evaluatingthe entire curve clearly shows that glycerol was morerapidly and extensively used over the entire incuba-tion time (Fig. 1).

Garland and Mills [5] introduced the concept ofAverage Well Color Development (AWCD) forcommunity-level Biolog data analysis. A plot ofAWCD over time (Fig. 2) for this data set is similar


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J.B. Guckert et (11. I Journal of Microbiological Methods 27 (1996) 183-197

A5 /-,// MAX. OD/ /-/ 1)

LAGI I ! / I II

0 10 20 30 40 50 60 70 80Incubation time in Hours

Fig. 1. Optical Density versus time curves for four wells of BiologGN MicroPlate inoculated with a MITI-fed SCAS unit microbialcommunity. Optical density is expressed as absorbance units at590 nm. The carbon sources are labeled by well number. Al,water (control); A5, Tween 40; H7, 2-aminoethanol; H9, glycerol.These carbon sources were selected to show that picking any onetime point to evaluate the Biolog plate results can give differentresults for the same plate. At 24 h, A5>H9>AI >H7. At 48 h,A5>H9>H7>Al. At 65 h, A5>H7>H9>>Al. Also, the threekey factors that make up the optical density versus time curves aremarked, the lag phase, the maximum rate of increase and themaximum optical density. The analysis for any one curve needs totake into consideration all of these factors.

to one shown by Garland (see Fig. 1 in [6]), althoughit appears that activated sludge communities have amuch shorter lag time than the soil communitiesevaluated by Garland [6]. AWCD development wasconsistent and linear over the entire 3-day incubationperiod. The total number of positive wells, describedas having a net (Al subtracted) value BO.25 ab-sorbance units, is also shown in Fig. 2, and is againsimilar to the results shown in [6]. The number ofpositive wells over time increased rapidly for the first24 h. After 24 h, the number declined slightly,possibly due to an increase in the control well (Al)absorbance value (see Fig. 1). The number ofpositive wells then increased until about 50 h ofincubation when it began to plateau. These resultsindicate that, at the end of a 3-day incubation period,

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Ec 1. 5zs 1. 0ElP$ 0. 59

0. 0I I I i I I I

0 10 20 30 40 50 60 70 80Incubation time in Hours

Fig. 2. Average Well Color Development (AWCD) is the mean ofthe blanked (At subtracted) absorbance values for all 95 responsewells per reading time (see [4] for details). This AWCD versustime curve is for a GN plate inoculated with a MITI-fed SCASunit microbial community. This curve shows that this activecommunity had, on average, very little lag time in the develop-ment of dye within the Biolog plate wells. Development wasconsistent and linear over the entire 3-day incubation period. Thenumber of positive wells (defined as blanked absorbance values>0.25) over time increased rapidly for the first 24 h. At this time,the number declined slightly, possibly due to an increase in thecontrol well (Al) absorbance value (Fig. 1). The number ofpositive wells then increased until about 50 h of incubation whenit began to plateau.

no new carbon sources were being utilized, but that,on average, the microbial community activity con-tinued to consume the carbon sources and increasecolor development of the Biolog dye.

The AWCD method for Biolog analysis wasdeveloped to account for differences in inoculadensities. However, this method still requires dataanalysis at various incubation times. Garland [6]proposes to define the analysis point based on thetime required to achieve a certain AWCD absorbancevalue, such as 0.75. Garland [6] also showed howmultivariate classifications could be influenced bydifferences in AWCD set points (e.g., 0.25, 0.5, 0.75,1.00). Our integrative approach (described below)requires an inoculum density adjustment, but pro-vides a method of analysis more independent ofincubation time.


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J.B. Guckert et al. / Journal of Microbiological Methods 27 (1996) 183-197 1893.2. Net area under the curve approach

Our approach to 13iolog data analysis has been tocalculate area under the absorbance versus timecurve. We feel that this number best summarizes thecolor development information because its value willreflect differences in lag phases, rates of increase,maximum optical densities, etc. We initially used akinetic Biolog data set in which we obtained 98 datapoints collected at approximately every 30 min overa 3-day period. The 98 data points for each of the 96wells was used as input for a curve-fitting routine inSAS [21]. SAS was successful at fitting log-logisticcurves to each of the 96 sets of data. We then usedthe same data set and truncated all but 12 of theseobservations. These 12 observations were selected sothey reflected actual times we would plan to observethe Biolog plates over a 3-day incubation period. Nosignificant differences were found between thecurves, indicating that we could simplify the pro-cedure by taking fewer data points to obtain the sameresult (G. Carr, unpublished data).

Even with the fewer data points per Biolog plate,the SAS curve-fitting and area integration routinewas very time consuming in both real time andcomputer CPU time. We then explored an additionalsimplification in which an estimate of area under thecurve was developed by summing the trapezoidsformed when drop lnes were drawn from each datapoint to the X axis. Details of the calculation used forthis approximation are provided in the Appendix A.A comparison for all of the SCAS Biolog results(discussed below) was made. Area was calculated forall 1728 curves (96 wells on each of 18 MicroPlates)using the SAS curve-fitting routine along with anarea integration as compared to an Excel-basedcalculation of the ttapezoidal approximation to areaunder the curve, shown in the Appendix A. Areavalues greater than 1 unit by both methods were thenanalyzed. A brief sample of results is shown in Table1, with summary stiatistics for the 1608 wells witharea >l unit. The average absolute value differencein area under the curve was 0.80 units, with amaximum difference of 7.3 1 (Table 1). The averagepercent difference between the two approaches was3.19%. The summary of data (Table 1) shows thatthe average area under the curve was about 41 areaunits for both, so an average difference of 0.80 units

is minor (


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Table 1J.B. Guckert et al. I Journal qf Microbiological Methods 27 (1996) 183-197

Comparison of integration methods for Biolog data setsWell Area under SAS Trapezoidal estimate of

best-fit curve area under the curveAbsolute valuedifference

Percentdifference

17 89.2465 91.951 2.70 2.9918 68.4507 73.3128 4.86 6.8619 68.4111 69.2353 0.82 1.2020 72.7837 70.4263 2.36 3.2921 19.5848 18.4186 1.17 6.1422 81.0536 79.3904 1.66 2.0723 80.6474 85.301 4.65 5.6124 84.3316 85.4424 1.11 1.3125 81.2219 79.155 2.07 2.5826 58.7679 60.3558 1.59 2.6727 72.0657 67.2839 4.78 6.8628 80.9 75.2803 5.62 7.2029 74.7898 71.264 3.53 4.8330 63.9544 59.3226 4.63 7.5 131 88.8478 90.2037 1.36 1.5132 97.2798 97.6841 0.40 0.4133 27.1123 22.4638 4.65 18.7534 12.8642 9.0734 3.79 34.5635 58.1416 57.6789 0.46 0.8036 33.7667 33.9934 0.23 0.6737 18.7633 15.9254 2.84 16.3638 73.1211 71.5239 1.60 2.2139 55.53 16 49.8672 5.66 10.7541 48.0974 46.1945 1.90 4.0442 55.0856 48.593 6.49 12.5243 56.9821 55.0411 1.94 3.4744 45.2828 42.5561 2.73 6.2145 60.5233 63.0733 2.55 4.1346 1.516 3.1708 1.65 70.6247 59.0321 57.0506 1.98 3.4148 53.7607 52.0241 1.74 3.2849 85.3295 88.7305 3.40 3.9150 50.5604 47.3626 3.20 6.5351 7.1777 3.5246 3.65 68.2752 53.103 50.2051 2.90 5.6154 39.4426 38.7184 0.72 1.85Average 40.73 40.59 0.80 3.19S.D. 20.40 20.17 0.85 5.95Max 98.40 98.78 7.31 70.62Min 1.07 1.06 0.00 0.00Median 41.78 41.57 0.57 1.57n= 1608 1608 1608 1608Brief data summary showing data from a GN plate inoculated with MITI-fed SCAS community. Summary statistics are for results acrossGN, GP and MT plates for all SCAS unit communities where area under the curve values were greater than 1 unit,

GN versus MT results (Fig. 3) suggests that even forthis worst-case example, the MT results do notsignificantly overlap with the higher net area re-sponses seen on the GN plates. No further subtrac-

tion of MT results was used for any sample since theresponse noted in the MT plates does not appear tobe due to any carbon source carry-over for thesesamples. In addition, this subtraction may not be


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0 10 20 30 40 50 60 70 60 90 100Well Number

Fig. 3. Profile plot of Biolog results expressed as net area underthe curve (with control well Al subtracted) for each of the 95response wells (e.g.. well number 2=A2, well number 96=H12)over a 3-day incubation, Results are shown for the MITI-fedSCAS unit microbial community in the Biolog GN (0) and MT(W) MicroPlates. A line is drawn through the data points as an aidin observing the response patterns. Values are given as mean areaunits irl S.D. for three separate Biolog plates run from threeseparate samples collected from the same SCAS unit, and thenprocessed individually.

warranted to normalize for well-to-well differencesas the MT plates are manufactured differently fromGN plates.3.5. Results for Bioiog GP MicroPlates

Biolog GP plates were also analyzed for the SCASunits. In Fig. 4, the GN and GP net area under thecurve results for glucose are compared for the MITI-fed, Sewage-fed and Ludzack-fed SCAS units. TheGN results suggest Ithat these SCAS unit microbialcommunities are about the same in their ability toutilize glucose. Even though the MIT1 and Ludzackcommunities have bleen cultivated with glucose, itmakes some sense that the capacity to utilize thiscarbon source would be retained in the sewage-fedcommunities. The results for the GP Biolog plates,however, are very different. These results suggestthat the glucose-fed microbial communities of theMIT1 and Ludzack-.fed SCAS units had a lower

x& 120t

MlTld 1 SEWAGE t3ln - LUDZACKE

100

f 60

O 60

5 40I?33 202Y 0i GN GP

Fig. 4. Results for cr-o-glucose utilization in the Biolog GN (wellB6) and GP (well Bl 1) for the three SCAS units maintained withdifferent feeds. Values are mean area units (3-day incubation) 2 1S.D. for three separate Biolog plates run from three separatesamples collected from the same SCAS unit, and then processedindividually. In theory, these values between the GN and GPshould be identical, and for the Sewage-fed SCAS unit there isgood agreement. However, the MITI- and Ludzack-fed SCASunits had lower values for glucose on the GP plate.

capacity to utilize glucose than the sewage-fedcommunities (Fig. 4). A more likely explanation isthat differences between GP and GN plate inorganicnutrients may have influenced this glucose result.Assuming that glucose-fed communities are notlikely to lose the capacity to utilize glucose, theseresults suggest that the MIT1 and Ludzack-fed SCAScommunities appear to have been influenced, andlikely inhibited, by inorganics found in the GPplates. Our original intent in using both GN and GPBiolog plates was to have an evaluation of 128unique carbon sources across the two plates, ratherthan the 95 available from either one separately. Inaddition, the carbon sources in common between theGN and GP plates were initially thought of asinternal replicates within the assay. The glucoseresults (Fig. 4) discussed above, indicate that thecarbon sources contained in both the GN and GPBiolog plates do not necessarily provide this level ofreplication, In addition, from the analysis below it isalso apparent that 95 separate carbon sources provideenough discrimination power for our research ques-


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192 J.B. Guckert et al. I Journal of Microbiological Methods 27 (19961 183-197

tions, and analysis of both the GN and GP isunnecessary. Therefore, our future Biolog work willonly use GN Biolog plates (with MT plates ascontrols for comparison but not subtraction) as theyappear to provide the most complete and reproduc-ible analysis for these communities.3.6. Biolog results for WWTPs

WWTP samples were analyzed with the GN andMT Biolog plates. The average values for each wellon the GN plate are shown for the Polk Run WWTP(Fig. 5). The pattern of carbon source utilization forthis fresh WWTP sludge appears to be different fromthe MITI-fed SCAS unit results shown in Fig. 3. ThePolk Run results (Fig. 5) also suggest that Biologmay be a method to quickly differentiate chemicalspecies that are consistently more highly degraded(=large area under the curve) by WWTP com-munities from those chemical species that are con-sistently not as well degraded (=small area under the

g 100t;E

90s 80?I 70g 60; 503 40t 3005 20g 102 0hz 0 10 20 30 40 50 60 70 80 90 100

Well NumberFig. 5. Profile plots of Biolog results expressed as net area underthe curve (with control well Al subtracted) for each of the 95response wells (well number 2 = A2, well number 96 = H12) overa 3-day incubation. Results are shown for the Polk Run Waste-water Treatment Plant microbial community in the Biolog GNMicroPlates. A line is drawn through the data points as an aid inobserving the response patterns. Values are given as mean? 1S.D.for three separate Biolog plates run from three separate samplescollected from the three separate locations within the WWTP, andthen processed individually.

curve). This information could be particularly usefulin the design and development of new, biodegra-dable, consumer product ingredients.3.7. Analysis of community-level Biolog results

As an example of how the net area under the curveapproach can be used, a comparison is made ofBiolog profiles for the three WWTPs and the threedifferent SCAS communities that were maintainedwith different feeds. Two methods of analysis arepresented. One is a multivariate analysis of the entireBiolog profile followed by comparisons of severalkey individual carbon source responses. The secondis a scatterplot of the entire Biolog data set tographically evaluate trends in the data set.3.8. Multivariate analysis

When a PCA was conducted for these carbonutilization patterns, the replicate values for eachWWTP were very similar (Fig. 6). These first twoprincipal components accounted for 64% of the totalvariability in the data set. The Middle East Fork andPolk Run WWTPs had similar Biolog metabolicprofiles, and these were similar to the profile of theSewage-fed SCAS unit. The largest separation in thisanalysis was between the SCAS units fed syntheticfeeds (Ludzack and MITI) and the rest of theSewage-fed SCAS and WWTP microbial com-munities (Fig. 6).

The PCA plot shown in Fig. 6 provides a methodto visualize the data and explore its structure, but itis only a first step in the data analysis for theseprofiles. On the basis of the loadings scores fromthe PCA, four representative carbon sources (suc-rose, L-glutamic acid, xylitol, Tween 40) wereselected to more directly compare the SCAS unitsand the WWTP metabolic patterns.

Sucrose appears to be equally utilized by all threeWWTPs and the Sewage- and Ludzack-fed SCASunits (Table 2) while the MITI-fed SCAS unitresponse appears to be enhanced. The MITI-fedSCAS unit also has an enhanced metabolism of theamino acids, such as L-glutamic acid (Table 2). Theresults with xylitol and Tween 40, however, suggestthat the MITI-fed SCAS units lost some key meta-bolic capacity (Table 2) from the fresh sludge


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al

SCAS

-15 - 10 -5 0 5 10 15 20Principal Component 1

Fig. 6. Muhivariate Principal Component Analysis for Biolog GNprofiles for microbial communities from three WWTPs (V,Middle East Fork; 6, Polk Run; filled hexagon, Sycamore) andthree SCAS units maintained with different feeds (0, MITI-feed;A, Ludzack feed; 0, Sewage feed; see text for details). PrincipalComponent 1 (48% of total variability) separates the MITI-fedSCAS unit from the other communities. The addition of thesecond principal component accounted for 64% of the totalvariability in the data set and the Ludzack- and MITI-fed SCASunit communities were clearly separated from the WWTP com-munities. The SCAS unit that had been fed fresh sewage for 110days had a metabolic profde more similar to the WWTP microbialcommunities than the other SCAS units.

microbial community. Xylitol, a hydroxylated pen-tane derivative (C,II,,O,), had similar Biolog re-sults for the WWTP and the Sewage-fed SCAS unit.The Ludzack and MITI-fed SCAS units, however,had a Biolog result about half of the initial value.The WWTP Biolog response for the non-ionicsurfactant Tween 40 is similar to that found in theSewage-fed SCAS. However, both the Ludzack andMITI-fed SCAS units appear to have much lowervalues, indicating that these microbial communitieshave lost some cap,scity to degrade this biodegra-dable, non-ionic surfactant (Table 2).3.9. Scatterplot analysis

A second approach to community-level Biologdata analysis is graphical using a scatterplot ofresults compared to {the profile obtained for the Polk

Run WWTP (Fig. 7). Results close to the referenceline indicate a carbon source response very similar tothe Polk Run profile. Results above the reference lineindicate that biodegradation activity has been en-hanced from the original sludge, results below theline indicate metabolic activity for that carbon sourcehas declined compared to the fresh sludge.

When the net areas under the curve for the MiddleEast Fork and Sycamore WWTPs are evaluated, theresults tend to cluster around the reference line (Fig.7). There does appear to be a trend in whichSycamore WWTP results are below the referenceline, but over the range of metabolic activity, theresults for all WWTPs appear to be very similar.

When the MITI-fed SCAS unit profile is comparedto the fresh sludge of Polk Run WWTP, there aremany differences (Fig. 7). MITI-fed SCAS profilessuggest an enhanced ability to biodegrade sugars andamino acids. This enhancement is observed for somecarbon sources previously at low activity (e.g., areaunder the curve 50 area units) in thefresh sludge of Polk Run WWTP (Fig. 7). It is likelythat this metabolic change is the result of mainte-nance of the community on the MIT1 feed, whichcontains sugar and amino acids (in peptone). Inaddition to an enhancement of sugar and amino acidmetabolism, there appears to be a decline in thebiodegradation of some more complex carbonsources (Fig. 7). The anionic surfactants (Tween 40and Tween 80), xylitol and glycogen are severalexamples of carbon sources that were biodegradedmuch more readily by the fresh WWTP microbialcommunity. These results indicate that the MITI-feedcultivation process has resulted in a decline in somespecific biodegradative capacity.

4. SummaryWe feel that the analysis of net area under the

curve provides a better method for the analysis ofBiolog data for microbial community investigations.The area under the curve approach can be automatedusing PC-based software. The area under the curvemeasure is sensitive to the differential effects of thelag phase, the rate of increase and the maximumabsorbance obtained during the incubation time.


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L-asparagine0 D-trehalose /L-aspartic acid 0L-glutamic acid # 6 sucrose

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0 20 40 60 80 100Polk Run WWTP Sludge Biolog Response by Area

Fig. 7. A scatterplot of Eliolog GN responses (average net area under the curve over a 3-day incubation) comparing the Middle East ForkWWTP (A), the Sycamore WWTP (Cl) and the MITI-fed SCAS unit (0) community metabolic profile to that obtained for the Polk RunWWTP (plotted on the Xaxis).Values close to the reference line are in good agreement with the Polk Run values.Values above the referenceline have an enhanced aoility to utilize that carbon source, values below have an inhibited ability, as compared to the fresh WWTP sludgefrom the Polk Run WWTP. The other WWTPs have values very close to the reference line, indicating similar metabolic profiles for allWWTPs. The MU-fed :SCAS community appears to be enhanced in the utilization of many sugars and amino acids and inhibited in theutilization of more complex carbon sources. such as Tween 40 and xylitol.

Since the integration is conducted over the course ofthe entire incubation time, this data analysis ap-proach is not dependent on set points developedarbitrarily by the researcher. Biolog profiles fromWWTP and SCAS unit microbial communities pro-vided several exam-ples of how these data sets canthen be analyzed. The MT MicroPlate results fromthese examples highlight the need to minimizeevaporation in the incubating Biolog plates. Thecomparison of GN and GP results in these examplessuggests that inhibitory effects may be complicatingthe interpretation of results from the GP plates. Onthe basis of these results, we suggest that the use of

GN Biolog MicroPlates (with MT plates as controls)provides the reproducibility and resolution needed toevaluate changes in metabolic diversity of mixedmicrobial communities. The preliminary results forthe WWTP and SCAS unit comparison also show apractical use of this data analysis technique toaddress important questions in microbial ecology. Inthis case, the microbial communities of laboratorySCAS units were maintained with a similar metabol-ic diversity to the WWTP activated sludge com-munities when the SCAS units were fed real sewage.When SCAS units were maintained on syntheticfeeds of simple carbon sources, the microbial com-


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196 J.B. Guckert et al. i Jourtd of Microbiolo+d Methods 27 (1996) 183-197munitys ability to degrade some simple carbonsources increased, but the ability to utilize morecomplex carbon sources declined, based on the areaunder the curve analysis of the community-levelBiolog method.

AcknowledgmentsComments and suggestions for the Biolog analysis

procedure were provided by Dr. L. Forney (Centerfor Microbial Ecology, Michigan State University),Dr. T. Nishihara (Osaka University, Japan) and Dr.M. Nasu (Osaka University, Japan). R.J. Larson(Procter and Gamble Environmental Science Depart-ment) provided key suggestions to experimentaldesigns and this report. This work has also benefitedfrom SCAS unit discussions with S.K. Kaiser, M.A.Hansmann, and E.A. Bookland; and Biolog dataanalysis discussions with D.M. Lee; all of the Procterand Gamble Environmental Science Department.

Appendix A: Excel formula for calculatingtrapezoidal approximation of area under curve

The Excel formula below calculates a trapezoidalapproximation of the area under the curve [23]. Itassumes the D column is used for elapsed time andthat D3 is the baseline or time of first measurement.Cell D2 will always be 0. Setting D2 to 0 assures theformulas will work correctly even if the first mea-surement time (D3) is not 0. The 96 columns(usually F - CW) are used for storing the observedvalues for each of the 96 wells at the times indicatedin column D. The formula should be stored in thecell immediately below the last observation in eachof the 96 columns (wells). The formula is written tobe self adjusting. It automatically determines thenumber of measurements and automatically updatescalculations when data are added. The text of theformula is the same in each of the 96 columns.Formula:=SUMPRODUCT($D$3:INDIRECT(ADDRESS(ROW()-1,4)),INDIRECT(ADDRESS(3,COLUMN())):INDIRECT(ADDRESS(ROW()-ICOLUMN())))/2+SUMPRODUCT($D$3:INDIRECT(AD-

DRESS(ROW()-1,4)),INDIRECT(ADDRESS(2,COLUMN())):INDIRECT(ADDRESS(ROW()-2,COLUMN( ),)) /2-SUMPRODUCT( $D$2:INDIRECT(ADDRESS(ROW()-2,4)),INDIRECT(ADDRESS(3,COLUMN())):INDIRECT(AD-DRESS(ROW()-l,COLUMN())))/2-SUM-PRODUCT($D$2:INDIRECT(ADDRESS(ROW()-2,4)),INDIRECT(ADDRESS(2,COLUMN())):IN-DIRECT(ADDRESS(ROW( )-2,COLUMN( ))))/2

EXAMPLE:i ti (time) v, (value)0 0 01 0 32 1 63 2 94 6 10

The trapezoidal area would be:C((q + y_,)l2)*(t; + Ll) = 5oi =

The Excel formula above uses a less straightforwardmethod that is mathematically equivalent:

ReferencesCl1121

[31[41PI

[61

Bochner, B.R. (1989) Sleuthing out bacterial identities.Nature 339. 157-158.Klinger, J.M., Stowe, R.P., Obenhuber, D.C., Groves, T.O.,Mishra SK. and Pierson, D.L. (1992) Evaluation of theBiolog automated microbial identification system. Appl.Environ. Microbial. 58, 2089-2092.Biolog. (1993) GP MicroPlate Instructions for Use. Biolog,Inc.Biolog. (1993) GN MicroPlate Instructions for Use. Biolog,Inc.Garland, J.L. and Mills, A.L. (1991) Classification andcharacterization of heterotrophic microbial communities onthe basis of patterns of community-level sole-carbon-sourceutilization. Appl. Environ. Microbial. 57, 2351-2359.Garland, J.L. (1996) Analytical approaches to the characteri-zation of samples of microbial communities using patterns ofpotential C source utilization. Soil Biol. Biochem. 28, 213-221.


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J.B. Guckert et al. / Journal of Microbiological Methods 27 (1996) 183-197 197[7] Zak, J.C., Willig, M.R., Moorhead, D.L. and Wildman, H.G.

(1994) Functional diversity of microbial communities: aquantitative approach. Soil Biol. Biochem. 26, 1101-1108.

[8] Haack, SK., Garchow, H., Klug, M.J. and Forney, L.J.(1995) Analysis of factors affecting the accuracy, repro-ducibility and interpretation of microbial community carbonsource utilization patterns. Appl. Environ. Microbial. 61,1458-1468.

[9] Victoria, L., Gilbride, K.A.. Allen, D.G. and Liss, S.N.(1996) Phenotypic fingerprinting of microbial communitiesin wastewater treatment systems. Water Res. 30, 1077-1086.

[IO] Biolog. (1993) MT MicroPlate Instructions for Use. Biolog,Inc.[ 1 ] Gorden, R.W., Hazen, T.C. and Fliermans, C.B. (1993) Rapidscreening for bacteria capable of degrading toxic organiccompounds. .I. Microbial. Methods 18, 339-347.

[12] Lee, C., Russell, N.J. and White, G.F. (1995) Rapid screen-ing for bacterial phenotypes capable of biodegrading anionicsurfactants: development and validation of a microtitre platemethod. Microbiology 141, 2801-2810.

[13] la Riviere, J.W.M. (1977) Microbial ecology of liquid wastetreatment. Adv. Microb. Ecol. 1, 215.

[14] Wagner, M., Amann, R., Lemmer. H. and Schleifer, K.-H.(1993) Probing activated sludge with oligonucleotides spe-cific for Proteobacteria: inadequacy of culture-dependentmethods for describing microbial community structure. Appl.Environ. Microbial. 59, 1520-1525.

[15] Boczar, B.A., Begley. W.M. and Larson, R.J. (1992) Charac-

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[16] OECD. (1993) 301C Modified MIT1 Test (I). In: OECDGuidelines for Testing ed.), pp. 25/62-31/62, 1. Organiza-tion for Economic Cooperation and Development, Paris,France.[171 Subcommittee on Biodegradation Test Methods of The Soapand Detergent Association. (1965) A procedure and stan-dards for the determination of the biodegradability of alkylbenzene sulfonate and linear alkylate sulfonate. J. Am. OilChemists Sot. 42, 16.[181 OEPA. (1995) Biological and Water Quality Study of theLittle Miami River and Selected Tributaries: Clark, Greene,Montgomery, Warren, Clermont and Hamiliton Counties(Ohio). DSW MAS / 1994-12-l 1. OEPA Technical Report.State of Ohio Environmental Protection Agency.

[19] Ludzack, F.J. (1965) Modified SDA feed for pH control.Communication to SDA Biodegradation Subcommittee.

[20] Swisher, R.D. (1987) Surfactant Biodegradation. MarcelDekker, Inc., New York. pp. 496.

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[23] Grossman, S.I. (1981) Calculus, Academic Press, New York.pp. 470-475.

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