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Assessing Microbial Communities for a Metabolic Profile Similar to Activated Sludge

June 14, 2007

By Paixao, S M Saagua, M C; Tenreiro, R; Anselmo, A M

ABSTRACT: To search for reliable testing inocula alternatives to activated sludge cultures, several model microbial consortia were compared with activated sludge populations for their functional diversity. The evaluation of the metabolic potential of these mixed inocula was performed using the Biolog EcoPlates and GN and GP MicroPlates (Biolog, Inc., Hayward, California). The community- level physiological profiles (CLPPs) obtained for model communities and activated sludge samples were analyzed by principal component analysis and hierarchic clustering methods, to evaluate the ability of Biolog plates to distinguish among the different microbial communities. The effect of different inocula preparation methodologies on the community structure was also studied. The CLPPs obtained with EcoPlates and GN MicroPlates showed that EcoPlates are suitable to screen communities with a metabolic profile similar to activated sludge. New, well-defined, standardized, and safe inocula presenting the same metabolic community profile as activated sludge were selected and can be tested as surrogate cultures in activated- sludge-based bioassays. Water Environ. Res., 79, 536 (2007). KEYWORDS: standardized biological reference material, model communities, community level analysis, Biolog (Biolog Inc., Hayward, California), activated sludge.

doi:10.2175/106143006X123148

Introduction

Biological assays are crucial for detection of pollution in the environment and the assessment of toxicity of wastewaters and chemical substances. During the past few years, much effort has been put forth to develop standardized analytical protocols (i.e., Organization for Economic Cooperation and Development [OECD, Paris, France], International Organization for Standardization [ISO, Geneva, Switzerland], and Comite Europeen de Normalisation [CEN, Brussels, Belgium]) that guarantee the safety of industrial products and processes and the protection and control of wastewater treatment plants (CORDIS, 2000). Many of those bioassays use activated sludge (AS) as the biological reference material for the study of biodegradability (Pagga, 1997; Strotmann et al., 1995) and toxicity (Pagga and Strotmann, 1999; Strotmann and Pagga, 1996; Strotmann et al., 1994).

However, besides the distinct abiotic characteristics of such habitats, the microbial population of activated sludge depends on the waste composition and operation mode of the treatment plant. Variations in microbial population affect the reproducibility of bioassays and therefore complicate the comparison of different tests. Thus, a strong need exists for the development of a standard and certified biological material, with the metabolic behavior of activated sludge, that can be used as a calibrant for quality control, improving the standardization of biological assays and providing traceability (CORDIS, 2000).

Previous studies have shown that microbial communities produce habitat-specific and reproducible patterns of carbon-source oxidation (Gamo and Shoji, 1999; Glimm et al., 1997; March et al., 1997; Kaiser et al., 1998; Smalla et al., 1998), and the method used to discern temporal and spatial differences among microbial communities was the substrate profiling by Biolog microtiter plates (Biolog Inc., Hayward, California).

Since the proposal of the community-level Biolog assay for the characterization of the heterotrophic microbial communities in environmental samples (Garland and Mills, 1991), this method has become widely used because of its rapidity and simplicity. The Biolog plates contain multiple sole carbon sources and a control without a carbon source. Each well also contains a minimal growth medium and the redox dye tetrazolium violet, which turns purple in the presence of electron transfer, indicating substrate utilization by the inoculated microbes.

In environmental studies using Biolog plates, gram-negative (GN) microplates are predominantly used, although, in a few studies, gram- positive (GP) plates were also referred (PrestonMafham et al., 2002). Choi and Dobbs (1999) demonstrated that GN MicroPlates and EcoPlates are equally efficient in distinguishing among heterotrophic bacterial communities from a variety of environments. Biolog data are also well-suited for multivariate statistical analyses, such as principal component analysis and cluster analysis, tools which can distinguish among microbial communities from various environments (Choi and Dobbs, 1999; Glimm et al., 1997; Victorio et al., 1996).

The main objective of this study was to compare the communitylevel physiological profiles (CLPPs) of several well- defined microbial consortia with those of activated sludge inocula (reference communities), using EcoPlates and GN and GP MicroPlates from Biolog, Inc., to select those that present a CLPP similar to activated sludge CLPPs. The selected microbial consortia can be further tested as alternative biological reference materials for toxicity and biodegradability assessment. Different inoculum preparation methodologies (inoculum washing, / growth medium, / optical density) were also applied to the microbial consortia to determine their influence on CLPPs.

Materials and Methods

Microbial Inocula. In this study, several microbial inocula were used-activated sludge inocula, as reference communities, and distinct bacterial model communities, as potential standardized biological reference materials. Each model community was designed to fit the following predefined criteria:

(1) To exhibit a similar CLPP to activated sludge CLPP;

(2) To be a mixed microbial consortium, composed by a defined and limited number of known strains, to make feasible its standardization, control, and production;

(3) To consist exclusively of heterotrophic bacteria, which are considered mainly responsible for organic matter degradation and overall metabolic activity;

(4) To be a homogeneous and stable blend of bacterial cultures;

(5) To contain neither pathogenic microorganisms nor nitrifiers;

(6) Not to contain microorganisms acclimatized to particular pollutants; and

(7) To be of easy preservation, preparation, and manipulation.

Activated Sludge Inocula. Activated sludge samples were collected from the aeration tank of two municipal wastewater treatment plants (WTPs), the Beirolas WTP (Lisbon, Portugal) (WTPl), which predominantly treats domestic wastewater, and the S. Joao da Talha WTP (Lisbon, Portugal) (WTP2), which treats urban wastewaters with approximately 60% industrial wastewater. The concentration of activated sludge samples was 2 to 4 g/L (suspended solids), and these samples were used within 36 hours after collection. From each homogenized activated sludge sample, the suspended microorganisms were separated by settling, with the resulting supernatant adjusted to an optical density at 600 nm (OD6Qo) of approximately 0.30 (direct inoculum). Simultaneously, activated-sludge-washed inocula were also prepared by centrifuging (6000 r/min for 10 minutes) and washing (twice) the suspended microorganisms with sterile phosphate buffer (50 mM, pH 7) or sodium chloride 0.85% (w/v), and resuspending them in phosphate buffer to an OD600 of approximately 0.30 (Guckert et al., 1996; Kaiser et al., 1998) (see Table 1).

Bacterial Model Communities. The model communities (MCs) consisted of the following:

(a) Well-defined bacterial mixtures containing 4 to 8 selected bacterial strains, based on their individual metabolic profiles (King and Dutka 1986; Haack et al., 1995), in equal proportions, belonging to nonpathogenic species from strains commonly found in wastewater and/or soil, namely Acinetobacter sp., Enterobacter sp., Xanthomonas sp., Staphylococcus capitis, Pseudomonas sp., Pseudomonas putida, Bacillus subtilis, and Bacillus cereus (strains stored in Cultures Collection of Industrial Microorganisms, Laboratory of Industrial Microbiology at National Institute of Engineering, Technology and Innovation, Lisbon, Portugal), and

(b) Commercial inocula (biological seed standardized by its manufacturer, containing nonpathogenic bacteria), namely BIOLEN M112 (Gamlen Industries S.A., St. Marcel-Vemon, France) and BI-CHEM BOD seed (Novozymes Biologicals Inc., Salem, Virginia).

Fifteen standardized consortia, 2 commercial inocula, and 13 bacterial mixtures were used to prepare 21 model communities (MC5 to MC25) according to different inoculum preparation methodologies (growth medium ^ optical density), as summarized in Table 2.

A master broth culture (MBC = consortium #1), composed of six bacterial strains, was developed to produce three different model communities (MC5 to MC7), according to the MBC growth on different culture media-synthetic medium (International Organization for Standardization, 1986) and tryptone soy broth (TSB). These cultures were incubated at 280C with agitation (150 r/min). After 18 to 20 hours, each inoculum was centrifuged (6000 r/min for 10 minutes) and washed twice with sterile sodium chloride (0.85%) or 50-mM phosphate buffer (pH 7), with the pellet resuspended in the phosphate buffer to an OD600 of approximately 0.30 to 0.50 (see Table 2).

Similarly, the commercial inocula, BIOLEN (#2) and BI-CHEM (#3), were used to prepare six model communities-MC8 to MClO and MCIl to MC13, respectively (see Table 2). For each commercial inoculum, a suspension was prepared in isotonic solution (International Organization for Standardization, 1986), aerated for 10 minutes to rehydrate the inoculum and adjusted to an OD600 of approximately 0.30. These suspensions were inoculated on synthetic medium and on TSB and incubated at 280C for 18 to 20 hours, with agitation (150 r/ min). Then, these cultures were centrifuged, washed, and resuspended in phosphate buffer, as described above, for MC5 to MC7. In addition, 12 more standardized microbial consortia (#4 to #15) containing 4 to 8 defined bacterial strains were designed, each corresponding to one model community (MC 14 to MC25). To prepare these model communities (MC 14 to MC25 containing predominantly GN bacteria), the pure cultures previously grown on TSB (28[degrees]C, 20 hours, 150 r/min) were added in equal parts (5 to 10 mL); then, each mixture was centrifuged, washed, and resuspended in phosphate buffer, as described above, for the other MCs (Table 2).

Biolog Assays. Biolog EcoPlates and GN and GP MicroPlates (Biolog Inc.), were used to evaluate the CLPPs. The EcoPlate contains three replicate wells of 31 carbon substrates (Choi and Dobbs, 1999; Insam, 1997) and a control well (Al), with no added carbon substrate. Any color development in Al well presumably indicates the use of carbon sources inherent to the inoculated microbial suspension. The GN and GP MicroPlates consist of 96-well microtiter plates, with 95 different carbon sources (Zak et al., 1994) and a control well, so these plates were used in triplicate for each microbial community analyzed.

For the Biolog assays with the several microbial communities, each suspension of bacterial cells, prepared as described above, was used to inoculate the Biolog EcoPlates and GN and GP microplates. Aliquots of 150 ie were added per well using a multichannel pippetor (Biolog 8 channels, Biolog Inc.), and the Biolog plates were evaluated for optical density changes at 590 nm using a microtiter plate reader (model MRXREVELATION. Dynex Technologies Inc., Chantilly, Virginia). Following an initial reading (A0), the plates were incubated at 28[degrees]C for 72 hours. The plates were read periodically at 17.5, 20, 22.5, 41, 45.5, 48.5, 66, and 72 hours, during the incubation period (Guckert et al., 1996). For each inoculum, the results were expressed as the mean optical density at 590 nm, OD590, (ODrO for the control wells and the ECO, GN, and GP substrates. For each reading time (T1), a relative value corresponding to the optical density ratio (OD-rj/ODro) was then calculated.

Biolog Data Analysis. A quantitative analysis (Paixao, Santos, Baeta-Hall, Tenreiro, and Anselmo, 2003) was performed by plotting the optical density ratio (OD-n/ODro ) versus time for the 32 wells of EcoPlates, the 96 wells of GN plates, and the 96 wells of GP plates. Each substrate curve was blanked (control Al curve subtracted), and then the curve-integration approach was used (Guckert et al., 1996; Hackett and Griffiths, 1997). The CLPPs were primarily expressed as the net area under the curve for each of the 31+95+95 carbon substrates over 3 days of incubation.

The relationships among the CLPPs of the several microbial communities were determined by principal component analysis (PCA), using ANDAD software (CVRM-IST 1989-2000, Centra de Geosistemas do Institute Superior Tecnico, Lisbon, Portugal), and by cluster analysis and Pearson linear correlations, using the NTSYSpc program, version 2.02h for Windows (Exeter Software, New York).

Principal component analysis is an ordination method that projects the original set of data points into new axes or principal components (PCs), so that intrinsic patterns of clustering become apparent. Each principal component extracts a percentage of the variance in the original data, with the greatest variance extracted by the first axis. Principal component analysis also allows identification of the major discriminating variables associated with a given principal component (Victorio et al., 1996). The PCA was done based on the correlation matrix of the variables (carbon sources).

In cluster analysis, applied also to ECO-GN and ECO-GN-GP data, the microbial communities were clustered using the unweighted pair group method of arithmetic averages (UPGMA) and the linkage distance between profiles based on Pearson’s correlation coefficient (1 – Pearson r).

Results and Discussion

Principal Component Analysis of Biolog Data. The PCA applied to CLPPs determined the relationships among the 25 microbial communities, based on differences in patterns of carbon use. The comparisons by using PCA determine samples differences, but do not show specific differences among samples.

Statistical analysis for the microbial communities functional differences was based on principal components of the data set from each of the three microplate types: ECO (Figure 1), GN (Figure 2), and GP (Figure 3) to compare the ability of each Biolog plate to distinguish among the microbial communities. Two principal components (PCl and PC2) were used throughout this data analysis, because approximately 75% of the total variance in the data was explained.

The separation of samples in principal component space can be related to differences in carbon source use by examining all the possible correlations of the original variables to the principal components. The most important carbon sources in differentiating among microbial communities typically give high positive or negative correlations, which are reflected in the ordination plots. Poor correlation of a carbon source does not necessarily mean that it was poorly used by the inocula, but rather that its use was not different among microbial samples and therefore is not useful in differentiating inocula (Victorio et al., 1996).

Principle Component Analysis for ECO Data. The bidimensional plot (PCl X PC2) presented in Figure 1 shows the relationships among 25 microbial communities according to ECO carbon-source-utilization pattern, where PCl accounted for 65% of the total variation observed and PC2 explained 10%. Analysis of the correlation of carbon source utilization on PCl and PC2 (Table 3) indicated that PCl represented the high use of 29 ECO substrates (r = 0.71 to 0.97), while PC2, delineated by negative correlation, was associated to the relatively low use of two ECO substrates, D-glucosaminic acid (r – -0.76) and itaconic acid (r = -0.74).

Positive correlation, as observed for substrates associated to the first principal component, can indicate a greater response for those carbon sources in communities with higher coordinate scores for the axis, while a negative correlation, as observed for the substrates associated to PC2, would indicate a greater use of those substrates in communities with lower scores for the axis (Garland, 1996).

The neighboring microbial communities in the scatterplot were expected to have similar carbon source use, whereas samples with a large distance to each other were expected to be different according to carbon source use. In this context, according to PCl, which is the axis explaining the greater variance of the original data (65%) and has most of the substrates associated (29 ECO sources) with it, the microbial communities were grouped into three major clusters (A, B, and C) (see Figure 1).

The model communities 18, 19, and 21 to 24 (cluster A) were the most responsive to ECO substrates associated with PC 1, as opposed to the model communities 8, 9, 10, and 13 (cluster C), which were the least responsive to these ECO substrates and presented the lower net areas for the substrates used. In relation to the two carboxylic acids associated with PC2, activated sludge communities (1 to 4) and the model communities 11 and 12 were the most responsive. The model communities 14, 15, and 25 showed the lower use of these ECO substrates.

In cluster A, the model community 11 had a CLPP more similar to activated sludge inocula CLPPs. However, the model communities 17, 20, and 25 also presented an identical response to all ECO carbon sources associated with PCl, differing mainly in the use of the two substrates associated with PC2. Model communities 8, 9, 10, and 13 (cluster C) were observed to be the most distinct from activated sludge (see Figure 1).

The CLPPs of activated sludge samples (1, 2, 3, and 4) seemed to be similar; however, in relation to the two samples from WTP2 (3 and 4), they seemed to be influenced by the washing procedure used. The washed inoculum, AS4, presented higher net areas in its metabolic pattern.

Also, an influence from the inoculum preparation procedure was observed among the model communities derived either from the commercial consortia (BIOLEN and BI-CHEM) or from consortium #1 (MBC). Among model communities 8, 9, and 10 (BIOLEN), the MC8 (rehydrated inoculum) was the least responsive because of the lower initial cells number (3 X 106 CFU/mL) compared with MC9 (BIOLEN grown in synthetic medium), which was the most responsive to ECO substrates (Figure 1).

In relation to the model communities 11, 12, and 13, derived from the BI-CHEM consortium, the least responsive was the MC13 (BI-CHEM grown in TSB), which showed much lower net areas for substrate use than MCIl and MC12. Despite an identical initial cells number presented by MC13 and MC12 (108 CFU/mL), the metabolic behavior of MC12 was more similar to MCIl, which had a lower initial cells number (4 X 106 CFU/mL), because it was a direct suspension of the lyophilized BI-CHEM. Thus, this showed that the profile of consortium grown in the synthetic medium was similar to the nongrowth inoculum.

In this context, the model communities 5, 6, and 7 (consortium #1) presented a similar net area response to ECO substrates, independently of the growth medium used. Despite the optical density range.5), the initial cell number was identical for all (108 CFU/ mL). Principal Component Analysis for GN Data. The bidimensional plot (PCl X PC2) presented in Figure 2 shows the relationships among 25 microbial communities according to GN carbon-source-use pattern, where PCl accounted for 66% of the total variation observed and PC2 accounted for only 7%. Analysis of the correlation of carbon source use on PCl and PC2 (Table 4) indicated that PCl was associated with the high use of 90 GN substrates, with high correlations (r > 0.85) observed for almost all carbon sources associated with PCl, while PC2 was associated mainly with the relatively low use of 5 GN substratesM-cyclodextrin (r = -0.76), itaconic acid (r = -0.53), Dglucosaminic acid and lactulose (r = -0.65), and 2-amino ethanol (r = -0.49).

The microbial communities were grouped into three major clusters (A, B, and C) according to similarity of carbon source use, relative to the 90 substrates associated with PC 1 (Figure 2). These clusters are similar to those delimited in the PCA of ECO data, differing mainly in the inclusion of MC9 and AS4 in cluster B, and including MC12 in cluster A, which contains the most activated sludge communities (1,2, and 3). In addition, a high percentage of agreement (84 to 100%) was observed for the use of the substrates common in both types of plates (25).

The model communities 18, 19, and 21 to 24 (cluster A) were also the most responsive to most of GN substrates associated with PCl, as opposed to the model communities 8, 10, and 13 (cluster C), which were least responsive to the substrates associated with this axis. Relative to the carbon sources associated with PC2, activated sludge communities (1 to 4) and the model communities 11 and 12 were the most responsive, as opposed to the model communities 18, 22, 24, and 25, which showed the lower use of these substrates.

In cluster A, model communities 11 and 12 presented the CLPP more similarly to the major activated sludge communities (1,2, and 3). However, the CLPPs of model communities 17, 20, and 25 differed from activated sludge CLPPs mainly in the use of the 5 GN substrates associated with PC2. Model communities 8, 10, and 13 (cluster C) were observed to be the most different from activated sludge (Figure 2).

As PCA for ECO data, this analysis showed that activated sludge CLPPs were more similar between samples from different sources (1 versus 3) than between samples from the same source, but subjected to a different preparation procedure (washing: 1 versus 2; 3 versus 4). The washing procedure decreased the net areas of the most substrate use curves, probably because of the absence of residual organic matter existing in activated sludge supernatant, which could act as an inducer. The influence of the experimental procedure during the inoculum preparation on its final metabolic profile was also confirmed for the commercial inocula, BIOLEN (8 to 10) and BI- CHEM (11 to 13), where different growth media were used.

Principal Component Analysis for GP Data. The bidimensional plot (PCl X PC2) presented in Figure 3 shows the relationships among the microbial communities according to the GP carbon-source-utilization pattern, which were grouped into three major clusters (A, B, and C). The GP data were obtained for 19 out of 25 microbial communities, selecting the best responsive model community for ECO and GN substrates from the consortia tested using different procedures, namely MC5 (MBC), MC9 (BIOLEN), and MC12 (BI-CHEM).

In this analysis, PCl and PC2 accounted only for 54 and 12%, respectively, of the total variation observed. Analysis of the correlation of carbon source use on PCl and PC2 (Table 5) indicated that PCl was associated with the high use of most GP substrates, with high correlations (r > 0.85) observed for almost all carbon sources associated with this axis, while PC2 was mainly associated with the high use of 13 GP substrates (r = 0.47 to 0.77) and with the relatively low use of a carboxylic acid, the a-keto valeric acid (r = -0.58).

In Figure 3, model communities 18, 19, 23, and 24 (cluster A) were the most responsive to the majority of GP substrates associated with the first axis, as opposed to activated sludge inocula (1,3, and 4) and MC5 (cluster C), which were the least responsive to these substrates, presenting the lowest net areas under curves. This behavior, observed for activated sludge inocula and MC5, contrast with PCA for ECO and GN data and can be explained essentially by the high net area values obtained for control wells that were used to determine the blanked net areas for the correspondent substrates.

Considering the substrates associated with PC2, MC12 was the community most responsive, relatively, to the use of the substrates associated with this axis, presenting positive correlation, as opposed to MC5, which was the least responsive for these substrates, but presented the highest response relative to the substrate with negative correlation (ot-keto valeric acid).

As in the previous PCAs, this analysis demonstrated the influence of the washing procedure used for the preparation of the activated sludge inocula in terms of the final CLPP exhibited. Once again, the activated sludge CLPPs were more similar between samples from different sources (1 versus 3) than between samples from the same source, but subjected to a different preparation procedure (1 versus 2; 3 versus 4).

Cluster Analysis of Biolog Data. The dendrogram, obtained by cluster analysis of grouped ECO-GN data sets (126 substrates), is presented in Figure 4. Seven groups could be defined in this dendrogram, with an overall similarity level of less than 4%. Group I contains the activated sludge samples (ASl to AS4), with 61% similarity and MCI 1 being less than 53% similar to the rest of the group. Group [Eth] contains MC5, MC9, MClO, and MC12, with 68% similarity. Group [Eth]E contains the model communities 14 to 25, with 71% similarity. These three groups were 53% similar. Groups IV to VD correspond to MC6, MC7, MC8, and MC13, respectively, which are linked with the first three groups at similarity levels between 36 and 4%.

The results of cluster analysis of grouped ECO-GN-GP data sets (221 substrates) are presented in the dendrogram of Figure 5. This dendrogram, which excludes six communities (MC6, MC7, MC8, MClO, MCl 1, and MC13) not tested with GP MicroPlates, shows a different group distribution, accounting for the divergence of results obtained with GP data, mainly for activated sludge samples. Five groups were defined, with an overall similarity level of approximately 25%. Group I contains ASl, AS3, and AS4, with 68% similarity. Group II corresponds to AS2, which was only 25% similar to the other activated sludge samples included in group I. Group [Eth]E contains the model communities 14 to 25, with 72% similarity. Group IV corresponds to MC9 and MC12, with 76% similarity and which were less than 60% similar to group III. The communities of groups E[Eth] and IV were less than 53% similar to AS2. Group V corresponds to mixture 5, which was 48% similar to groups II to IV.

In this data analysis (Figure 5), group I, involving the major activated sludge samples, was only 25% similar to the other group communities, compared to the 53% similarity of all activated sludge samples with the communities of groups [Eth] and [Eth]E, observed with ECO-GN data (Figure 4).

Correlation Analysis. The relationships among the microbial communities’ CLPPs based on ECO, GN, GP, and ECO-GN or ECO-GN-GP data were also analyzed by Pearson linear correlation. The correlation matrices obtained permitted a comparative study between each model community and the activated sludge inocula. This comparative study was carried out in relation to activated sludge from the WTPl treating predominantly domestic waste water (i.e., ISO’s reference inoculum).

Therefore, Table 6 summarizes the correlations obtained for model communities more similar to activated sludge communities (ASd = mean value relative to AS 1 and AS2), in terms of their metabolic behavior, distinguishing those that have higher relative correlations in each data set (ECO, GN, GP, ECO-GN, and ECOGN-GP).

Global Analysis. Methods that use activated sludge as an inoculum for biological tests, to measure toxicity and ready or inherent biodegradability of chemicals, are among the most widely used, because chemicals that ultimately enter the environment are often discharged through wastewater or WTPs.

Toxicity and biodegradability screening tests are standardized to ensure repeatability. However, given the enormous variability in the structure and function of microbial communities developed at different times or in different places, it is quite reasonable to question the repeatability of tests using these communities. In the case of the activated-sludge-based tests, the microbial communities found in activated sludge are complex assemblages of microorganisms that maintain a dynamic equilibrium by responding to changes in environmental conditions, either by shifts in the structure of the community (species richness or rank abundance) or by quantitative or qualitative changes in community function (Forney et al., 2001).

In accordance, some authors (CORDIS, 2000; Forney et al., 2001) indicate that differences in the composition of influent wastewater, in plant operation mode, or manipulations done after the collection of sludge can lead to inconsistent biodegradation or toxicity data, confounding its interpretation.

The inoculum quality (composition and cell density) is therefore variable and difficult to standardize. In spite of some recommended pretreatment for the activated sludge inoculum used (i.e., filtration, decantation, aeration, and washing), the inoculum shows huge variability and constitutes the main cause of poor reproducibility of standardized tests (Struijs et al., 1995; Thouand et al., 1995, 1996; van squez-Rodriguez et al., 1999, 2000). In fact, the lack of microbial inoculum standardization makes it almost impossible to carry out controlled assays, reproduce test results, or compare different test results, indicating an obvious need to have standard biological reference material for toxicity and biodegradability standardized tests. Models are the most basic of scientific tools. Models are not exact reproductions of the systems being studied, as they incorporate only the parts needed. In this ambit, this work focused on the selection of model microbial communities, composed of a defined and limited number of bacteria commonly found in wastewater and/or soil, presenting a similar CLPP to activated sludge. A similar metabolic behavior to activated sludge was a prerequisite imposed because an activated sludge surrogate culture was developed. In fact, the use of standardized inocula (i.e., controlled mixtures of microbial strains with a specific or broad-range sensitivity) permits a more homogeneous response, absence of sampling variability, absence of pathogenic microorganisms, and benefits from the selected strain’s ecological relevance.

Haack et al. (1995), using model communities consisting of 4 or 6 isolates, as in this study, demonstrated that their substrate use profiles were repeatable and unique. Thus, the use of representative model communities with a reduced number of heterotrophic bacteria (4 to 8) make feasible their standardization, control, and production.

Statistical analysis (PCA, cluster analysis, and Pearson correlation analysis) applied to the CLPPs, based on the use pattern of 221 carbon sources (ECO + GN + GP), permitted the following:

(1) Assessment and comparison of the functional diversity (metabolic potential) of several microbial communities to select activated sludge surrogate cultures, and

(2) Evaluation of the influence of the use of different inoculum preparation procedures on its final CLPP to choose the most effective methodology for the inoculum preparation.

Relative to ISO’s reference inocula (activated sludge), two different sources (WTPl versus WTP2) were tested, to assess inoculum variability and establish pattern CLPPs. In this study, the activated sludge inocula from the two sources chosen presented a quite similar functional diversity because of a similar microbial composition and an identical cell density. In addition, the authors also evaluated the influence of an usual recommended pretreatment for activated sludge in bioassays-the washing of the inoculum-on each activated sludge CLPP (International Organization for Standardization, 1986, 1999). In general, the washing procedure applied to activated sludge samples led to a decrease of net areas under the curves of the substrates and a slight decrease in the number of substrates used compared with samples that were not washed. The CLPPs of washed samples (AS2 versus AS4) differed more between them than the CLPPs of samples that were not washed (AS 1 versus AS3), despite the different source of activated sludge. The manipulation of each activated sludge inoculum (washing) introduced a higher variability to inoculum quality, which was demonstrated by the change observed in the corresponding CLPP (ASl versus AS2; AS3 versus AS4). Thus, it is important to standardize the inoculum, including its manipulation procedure, to guarantee uniformity and maintenance of the inoculum characteristics and, ultimately, its performance as a biological material for bioassays.

In this study, a group of model communities was selected as potential activated sludge surrogate cultures-MC5, MC9, MC12, MC17, MC18, MC19, and MC21. Figure 6 presents the CLPPs of these communities compared with activated sludge CLPPs, grouping the use pattern into six substrate sets, according to Preston-Mafham et al. (2002).

The model communities MC5, MC9, and MC12 were selected as the best model communities derived from consortia #1 (MBC), #2 (BIOLEN), and #3 (BICHEM), respectively (see Table 2). The use of synthetic medium to grow each of these three consortia was the best methodology, because it enhanced the microbial metabolic responses for substrate use-higher net areas and higher number of substrates used (i.e., increased the metabolic potential). These three model communities, with CLPPs similar to activated sludge CLPPs, have already been tested as a biological reference material in toxicity and biodegradability tests, in which their potential to be used as alternative inocula to activated sludge was demonstrated (Paixao et al., 2000,2006; Paixao, Santos, Baeta-Hall, Tenreiro, and Anselmo, 2003; Paixao, Santos, Tenreiro, and Anselmo, 2003).

In fact, the use of suitable representative standardized microbial inocula as a surrogate culture may act as a complement and important supplementary tool to existing activated sludge tests (OECD, ISO). The seed homogeneity and stability, with its easy and safe manipulation, are important properties of biological reference materials, and this can be accomplished easier using harmless standardized inocula rather than activated sludge samples collected from domestic wastewater treatment facilities.

To validate the selected model communities as alternative biological reference materials, toxicity and biodegradability tests on specific chemicals and effluents must be carried out to evaluate their performance and sensitivity compared with activated sludge results.

Conclusions

* The CLPP approach gives information about the composition of each microbial community according to a functional diversity. The CLPP is reproducible and shows discriminative power to differentiate microbial communities.

* EcoPlates (31 substrates) were suitable to compare the similarities and differences among the 25 CLPPs, demonstrating an equivalent capacity to discriminate among the heterotrophic communities as GN MicroPlates (95 substrates). The presence of three replicate sets of substrates in the same plate makes EcoPlates an useful choice as a screening method to distinguish among communities’ functional diversity.

* From 21 model communities studied, the following 7 were selected as potential activated sludge surrogate cultures: MC5, MC9, MC12, MC17, MC18, MC19, and MC21. These standardized inocula accomplished the predefined criteria required for a potential biological reference material, as follows:

(1) Exhibit a similar CLPP to activated sludge CLPP;

(2) Consist of a mixed microbial consortium composed of a defined and limited number of heterotrophic bacterial strains, which allow its standardization, control, and production to be feasible;

(3) Be a homogeneous and stable blend of bacterial cultures;

(4) Contain neither pathogenic microorganisms nor nitrifiers;

(5) Not to contain microorganisms that are acclimatized to particular pollutants; and

(6) Be of easy preservation, preparation, and manipulation.

Submitted for publication January 5, 2005; revised manuscript submitted March 17,2006; accepted for publication June 20, 2006.

The deadline to submit Discussions of this paper is August 15, 2007.

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S. M. Paixao1*, M. C. Saagua1, R. Tenreiro2, A. M. Anselme1

1 Environmental Microbiologist, Unit of Monitoring and Ecotoxicity (UME) at Biotechnology Department of INETI, Lisboa, Portugal (S. M. Paixao and M. C. Saagua are Ph.D. holders in Microbiology, and A. M. Anselme is the Head of the UME, with a Ph.D. in Biotechnology).

2 Rogerio Tenreiro has a Ph.D. in Microbiology and is a lecturer in the Faculty of Sciences at Lisbon University, Lisboa, Portugal.

* Susana Paixao, UME-DB-INETI, Estrada do Paco do Lumiar 22, 1649038 Lisboa, Portugal; e-mail: susana.alves@ineti.pt.

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