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Acinetobacter isolates from different activated sludge processes: characteristics and neural network identification
FEMS Microbiology Ecology 23 (1997) 217^227
isolates from di¡erent activated sludge processes: characteristics and neural network identi¢cation
Acinetobacter
Michael H. Kim a , Oliver J. Hao a *, Nam S. Wang ;
a b
b
Department of Civil Engineering, University of Maryland, College Park, MD 20742, USA
Department of Chemical Engineering, University of Maryland, College Park, MD 20742, USA
Received 5 March 1997; revised 18 April 1997; accepted 18 April 1997
Abstract
species were isolated from various full-scale activated sludge processes based on their abilities to transform an BD413 trp E27 auxotroph. Approximately half of the Acinetobacter isolates (149 out of 282 isolates) were able to accumulate polyphosphate, and some used L-hydroxybutyrate as a sole carbon and energy source. Additionally, most of the Acinetobacter isolates were unable to reduce nitrate. These characteristics of Acinetobacter species are desirable for microorganisms responsible for enhanced phosphorus removal in different activated sludge processes. The backpropagation neural network technique was further applied to assign the isolates to distinct Acinetobacter genospecies based on their phenotypic characteristics. In particular, Acinetobacter johnsonii was consistently the major genospecies from different samples obtained from the enhanced phosphorus removal processes or the conventional plant without biological phosphorus removal. Acinetobacter
Acinetobacter calcoaceticus
Keywords :
reduction ;
Activated sludge process; Neural network; Enhanced phosphorus removal; Polyphosphate; Poly-L-hydroxybutyrate; Nitrate Acinetobacter
1. Introduction
Most wastewater treatment plants are required to remove phosphorus (P) because of its role in eutrophication. The biological means for P removal provides a cost-e¡ective alternative to chemical precipitation methods. The conventional activated sludge (AS) process can be easily modi¢ed to remove P along with organics when an anaerobic stress is imposed on the process [1]. AS microorganisms exposed to alternating anaerobic/aerobic conditions ex* Corresponding author. Tel.: +1 (301) 405-1961; Fax: +1 (301) 405-2585; E-mail: [email protected]
hibit enhanced P removal capability far greater than the normal microbial growth requirements by storing ortho-P as intracellular polyphosphate (PP) under aerobic conditions [2,3]. The stored PP is then hydrolyzed to yield the energy necessary for formation of carbon reserve polymers, e.g. poly-L-hydroxybutyrate (PHB), under the imposed anaerobic conditions. Among AS bacteria, Acinetobacter species have been believed to be responsible for enhanced biological P removal [4,5]. They are strictly aerobic, Gramnegative, non-motile, catalase-positive, and oxidasenegative; they grow well on short chain fatty acids (e.g. acetate), but not on simple carbohydrates (e.g.
0168-6496 / 97 / $17.00 ß 1997 Federation of European Microbiological Societies. Published by Elsevier Science B.V. PII S 0 1 6 8 - 6 4 9 6 ( 9 7 ) 0 0 0 2 5 - 1
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218
glucose).
Based
on
these
nutritional
Fuhs and Chen [6] ¢rst suggested that
requirements,
quently,
Acinetobacter
phenotypic test results as an input was used to assign
a
neural
Acinetobacter
network
modeling
[20]
using
28
species use incomplete oxidation products, such as
the
acetate and ethanol generated by facultative anae-
tion groups [17]. Thus, the extent of the distribution
robes
of di¡erent
under
anaerobic
conditions,
to
store
PHB.
Subsequently, under carbon-limiting aerobic condi-
Acinetobacter
tions,
isolates to DNA/DNA hybridiza-
Acinetobacter
genospecies in various en-
vironmental conditions could be examined.
cells exhibit a competitive edge
A neural network can correctly capture seemingly
over other aerobic heterotrophs and proliferate in
complex input and output patterns by mimicking the
such
have
shown
learning
Acinetobacter
species
backpropagation neural network that learns and ap-
are present in alternating anaerobic/aerobic AS proc-
proximates input and output patterns by examples
esses [6^10]. However, other PP-accumulating bacte-
has been applied to speech synthesis and recognition,
ria may also play a role in enhanced P removal proc-
visual pattern recognition, analysis of sonar signals,
esses [9^12].
medical diagnosis, and learning in process control
that
systems. a
In
signi¢cant
fact,
Acinetobacter
The
many
number
has
studies
of
also
been
frequently
ability
of
the
brain
[21].
In
particular,
a
iso-
systems [20]. However, the use of neural networks
lated from other sources (e.g. soils and water) and
for identi¢cation of microorganisms is rather limited.
shown to be phenotypically and genotypically heter-
For example, only two case studies of identi¢cation
ogeneous [13]. The identi¢cation of the
Acinetobacter
Haemophilus
using a neural network approach for
genus is often based on the results of simple bio-
species [21] and marine bacteria [22] have been re-
chemical tests, e.g. API test kit (4, 5), despite the
ported. In the present study, a non-linear learning
fact that the transformation assay for
Acinetobacter
is well established [14]. The earliest attempts to classify
Acinetobacter
into di¡erent species were based
capability
of
a
neural
bacter
was used to identify
carbon substrates [13,15] that showed two or four
species
broad
bacter
[16,17],
DNA/DNA
rRNA/DNA
hybrid
hybrid
thermal
thermal
stability
stability
and
16S rRNA sequence similarity map [18] have also been used to classify di¡erent Furthermore,
Acinetobacter
chemotaxonomic
methods
species.
based
was
evaluated
for
Acineto-
genospecies, and the trained neural network
on the numerical taxonomy on the use of di¡erent
phena.
network
highly variable phenotypic characteristics of
level.
Acinetobacter
Finally,
di¡erent
isolates at geno-
groups
of
Acineto-
genospecies were characterized for PP accu-
mulation,
denitri¢cation
capability,
and
PHB
utilization for their possible role in enhanced P removal processes.
on
the analysis of a particular biomarker such as respiratory quinone [12], diaminopropane [10], fatty acid
2. Materials and methods
and soluble proteins [19] pro¢les, have been used for identi¢cation Among
and/or
these,
estimation
however,
of
phenotypic
Acinetobacter. tests
2.1. AS samples
provide
the most simple and rapid method for isolated cul-
Samples were obtained from two biological P re-
tures. In particular, the 28 phenotypic characteristics
moval
AS
correlated with DNA/DNA hybridization data [17]
water
treatment
provide a comprehensive classi¢cation scheme and
Both
facilitate identi¢cation of di¡erent
Acinetobacter gen-
Phostrip
at
plant
(Fig.
the in
1a)
Little
Patuxent
Howard
and
2
A O
waste-
County,
MD.
processes
(Fig.
1b) are designed for e¡ective P removal by the imposed anaerobic conditions. However, the Phostrip
ospecies.
Aci-
process does not involve nitri¢cation and denitri¢ca-
genospecies present in the `man-made' AS
tion, whereas nitri¢cation/denitri¢cation is achieved
To better understand the diversity of di¡erent
netobacter
processes
Acinetobacter
2
species were isolated from
in the A O process by recycling the nitri¢ed e¥uent
various biological P removal and conventional AS
through anoxic zones (without oxygen, but with ni-
processes. The identi¢cation at the genus level was
trate). The AS samples were also obtained from a
Acinetobacter
conventional plant (Fig. 1c) without biological P re-
BD413 trp E27 auxotroph [14]. Subse-
moval at the Blue Plains wastewater treatment plant
ecosystem,
based on their abilities to transform an
calcoaceticus
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219
Fig. 1. Source of AS samples from di¡erent AS processes.
in Washington, DC. The locations of samples from
Na2 B4 O7 W10H2 O.
di¡erent zones are shown in Fig. 1.
(ADM ; basal medium supplemented with 2 g of
The
acetate
de¢ned
medium
Na acetate per liter) was used for isolation.
3
1 Sludge samples were initially diluted (10 ) in a 3 M phosphate bu¡er solution (pH = 7.2) and 10
2.2. Isolation procedure
3
claving) contained the following ingredients per liter :
mixed in a high speed blender for 20 min. The 2 8 blended sludge was further diluted (10 ^10 ),
A basal medium (pH adjusted to 7.4 before auto-
3
3
1 g of (NH4 )2 SO4 , 2 g KH2 PO4 , 0.8 g MgSO4 W7H2 O,
plated on ADM agar, and incubated at 20³C for
10 ml of the stock mineral solution, and 1 ml of trace
7 days. Random isolates from plates were picked
metal solution. The stock mineral solution contained
for further tests. Additionally, for each aerobic zone
(per liter) 20 g nitriloacetic acid, 14.6 g KOH, 6.6 g
sample from the three AS processes, 10 ml of each
CaCl2 W2H2 O, 0.0186 g (NH4 )8 Mo7 O24 W4H2 O, and 0.2
blended sludge sample was further ¢ltered through a
g FeSO4 W7H2 O. The trace metal solution contained
Whatman No. 2 ¢lter. The biomass on each ¢lter
(per
g
paper was washed 10 times with 1 ml phosphate buf-
g
fer solution and transferred into a test tube. The bio-
MnSO4 W4H2 O, 0.04 g CuSO4 W5H2 O, and 0.018 g
mass was sonicated (Bransonic Model 350) at a
liter)
0.25
ZnSO4 W7H2 O,
g 0.5
EDTA g
(disodium
FeSO4 W7H2 O,
salt),
1.1
0.154
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Fig. 2. Structure of backpropagation neural network.
power output of approximately 26 J/s for 100 s to break up the sludge £ocs [23]. The sonicated samples were further diluted, and plated onto ADM plates with or without 2U1034 M DCCD (N,NP-dicyclohexylcarbodiimide, an ATPase inhibitor). The additional treatments to aerobic sludge samples were performed to enhance the isolation of Acinetobacter from sludge £ocs and to evaluate any alternative ATP generation from the stored PP. Initially, the colonies were examined microscopically on a wet mount for motility; colonies showing £agellum motility were discarded. Cells were further characterized by Gram-staining, and only Gram-negative isolates were transferred to ADM plates for further puri¢cation. After 2 days of incubation at 30³C, purity was con¢rmed microscopically.
Na3 citrateW2H2 O, and heating at 65³C for 2 h. A small amount of auxotroph, cultured overnight in the ADM broth supplemented with 100 mg/l of Ltryptophan, was mixed with approximately 0.1 ml of each crude DNA solution in a spot (ca. 1 cm diameter) on an ADM agar plate supplemented with 50 mg/l of L-tryptophan. The non-DNA added auxotroph and the DNA solution alone (without cells) were also spread on the same plate as control. After incubation at 30³C for 2 days, colonies from DNA and non-DNA treated sample were streaked onto ADM agar plate without L-tryptophan supplement and incubated at 30³C for 2 days. Only transformants exhibiting growth on ADM without L-tryptophan supplement were positively identi¢ed as Acinetobacter.
2.3. DNA transformation assay
2.4. Biochemical characterization
The colony transformation method using the competent strain BD413 trp E27 obtained from Dr. R. Bayly (Monash University, Victoria, Australia) was performed as described by Juni [14]. A crude DNA was extracted by adding a loopful culture of each isolate in a solution consisting of 0.05% sodium dodecyl sulfate, 0.15 M NaCl, and 0.015 M
For each Acinetobacter isolate, the 28 physiological, biochemical and nutritional tests (Table 1) were performed as described by Bouvet and Grimont [17]. 2.5. PP accumulation and nitrate reduction
The accumulation of PP in each isolate was tested
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with the ADM broth in which the concentrations of the original P and carbon source were increased to 100 mg/l P and 5.67 g/l NaAc, respectively [24]. Each isolate was stained with a modi¢ed Neisser method [25]. The capability of isolates to reduce nitrate (to nitrite) and denitrify (to N2 ) was tested with tryptocasein soy broth, supplemented with 0.17% agar and 0.1 g KNO3 /l [26]. The concentration of nitrite was monitored spectrometrically at 540 nm. For those isolates with only nitrate reduction capabilities, nitrite accumulated in the medium. However, isolates that could denitrify accumulated nitrite initially; nitrite then disappeared due to further reduction to N2 . 2.6. PHB metabolism
The key enzyme, L-hydroxybutyrate (L-HB) dehydrogenase, is common to the pathways of both L-HB utilization and PHB synthesis in all organisms capable of PHB metabolism [27]. Therefore, the result of L-HB utilization tests was used as an indicator for PHB metabolism for those isolates exhibiting PP accumulation. 2.7. Backpropagation neural network
The backpropagation neural network consisted of input (i neurons), hidden (j neurons), and output (k neurons) layers with a bias at the input and hidden layers (Fig. 2). Adjacent layers are interconnected by weighted branches, i.e. weight matrices ji and kj for input to hidden and hidden to output layers, respectively, where subscripts denote weights associated with ith neuron of the input layer to jth neuron of the hidden layer and jth neuron of the hidden layer to kth neuron of the output layer. In the forward propagation mode, a set of input data ( vector) is presented to neurons in the input layer which simply transmit the input values to the hidden layer, and, subsequently, each neuron in the hidden and output layers except the bias unit calculates its activation (c) as a function of the weighted sum (z) of its input as follows: 1 1 c z 1 e3z V
W
x
zj
X 0 X
2a
Wji xi
Vkj cj
i
zk
0
2b
j
In a learning mode, sets of training examples consisting of n input and output vector pairs ( and ) are repeatedly presented to the network. Each neuron in the output layer predicts a single real number, based on the input ( ) and current weight values of ji and kj . The objective is to iteratively search for sets of weights ( ji and kj ) that minimize the sum of squared errors (J) between the predictions ( ) and the desired outputs ( ) over all n training examples: n 2 yp 3dp 3 minw v J x
d
x
V
W
V
W
;
X
y
d
1
p
In the backpropagation mode, the weights ji and are updated after presentation of each sample in proportion to the gradient of the error between the prediction and actual values at the rth iteration step as: 4 vr Vkj R drk 3yrk y2rk 13yrk and 2 vr Wji R 13drk drk Nrk Vkj 5 W
Vkj
X k
where Nrk is further de¢ned as 6 Nrk drk 3yrk yrk 13yrk In Eqs. 4 and 5, R is a learning parameter that has a value less than 1 which decreases as the training proceeds. Detailed information about neural network fundamentals as well as implementation can be found in Baughman and Liu [28]. 3. Results
3.1. Training of a neural network
Genospecies group 1^12 that had been genotypically and phenotypically characterized by Bouvet
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Table 1 Phenotypic characteristics of Acinetobacter genospecies [17] Characteristic Positive strains of each genospeciesa (%) 1 2 3 4 5 6 7 8/9 10 11 12 this studyb (8) (121) (15) (23) (17) (3) (23) (34) (4) (4) (3) (162) Growth at 44³C 3 + 3 3 3 3 3 3 3 3 3 3 Growth at 41³C 3 + + 3 90 3 3 3 3 3 3 4 Growth at 37³C + + + + + + 3 + + + + 20 Gelatin hydrolysis 3 3 3 96 3 + 3 3 3 3 3 7 Hemolysis 3 3 3 + 3 + 3 3 3 3 3 8 Q-Glutamyltransferase + 99 + 4 3 66 3 3 3 3 3 10 Citrate (Simmons) + + + 91 82 + + 3 + + 3 39 Glucose oxidation + 95 + 52 3 66 3 6 + 3 33 2 L-Xylosidase 3 95 + 52 3 66 3 6 3 3 3 2 Utilization of: DL-Lactate + + + 3 + 3 + + + + + 44 Glutarate + + + 3 3 3 3 3 + + + 15 L-Phenylalanine + 87 66 3 3 3 3 3 3 3 + 6 Phenylacetate + 87 66 3 3 3 3 94 25 50 + 3 Malonate + 98 87 3 3 3 13 3 3 3 + 5 L-Histidine + 98 87 96 + + 3 3 + + 3 25 Azelate + 90 + 3 3 3 3 + 50 25 + 8 D-Malate 3 98 + 96 + 66 22 76 + + 3 4 L-Aspartate + + + 64 40 66 61 3 + 75 3 37 L-Leucine 38 97 94 96 11 + 3 3 3 3 + 10 Histamine 3 3 3 3 3 3 3 3 + 75 3 7 L-Tyrosine + + + 5 60 66 70 3 + 75 + 23 L-Alanine + 95 94 3 3 3 3 3 + + 3 19 Ethanol + + + 96 + + + 97 + + + 99 2,3-Butanediol + + + 3 3 3 35 3 + + + 26 trans-Aconitate + 99 + 52 3 3 3 3 3 3 3 4 L-Arginine + 98 + 96 95 + 35 3 3 3 + 28 L-Ornithine + 93 + 3 3 3 4 2 3 3 3 10 DL-4-Aminobutyrate + + + + 88 3 35 40 + + + 23 a + = 100% positive; 3 = 100% negative. The number in parentheses indicates the number of each respective genospecies tested. b For 162 isolates identi¢ed as genospecies 7 in this study.
and Grimont [17] were used for training of the neural network. Additional groups of Acinetobacter genospecies, described by Tjernberg and Ursing [29], were not consolidated for the neural network identi¢cation, since only a few phenotypic properties of these additional genotypes were investigated. Among genospecies 1^12, species 8 and 9 have been grouped together in this study since they could not be distinguished from their phenotypic characters [17]. Additionally, because genospecies 1^3 share similar phenotypes [19,30], these genospecies have also been grouped as one to yield a total of nine groups out of 12 genospecies for training of the network.
One thousand training data points, equally distributed among the regrouped nine genospecies, were randomly generated based on the percent positive reaction values (Table 1); e.g. each genospecies 7 training strain independently has 61% probability of being positive for L-aspartate, and so on. Each of 28 unit characters (numerical value of 1 for positive and 0 for negative) was used as the input to the network. Initially, 10^100 hidden neurons were investigated and the optimum number of hidden neurons yielding the lowest overall error (J) of the training set was determined to be 15. Approximately 30 000 iterations with 15 hidden neurons were subsequently used to train the network. The number of
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the output layer consisted of nine neurons, representing each one of the nine genospecies. 3.2. Neural network performance evaluation
Phenotypic data of Acinetobacter genospecies from Baumann et al. [13] and Morton and Barrett [31], as discussed and assigned to genospecies 1^12 by Bouvet and Grimont [17], were used to test the trained network. Independent testing data are necessary to validate the prediction by the trained network, prior to identi¢cation of the isolates to the genospecies level. However, only 21 or 18 out of 28 tests employed by Bouvet and Grimont [17] were characterized by Baumann et al. [13] or Morton and Barrett [31], respectively. Therefore, the remaining input data were obtained from the Bouvet and Grimont percent positive reaction values (Table 1), similar to procedures used in the training of the network. Ninety-nine strains out of a total of 104 were correctly predicted by the trained network with an average activation value above 80% (Table 2). The prediction was based on the highest neuron activation level at the output layer; if any particular testing set exhibited its highest activation level below 0.5, the isolate was designated as non-identi¢ed. Each of the nine genospecies groups with the exception of species 10 and 12 was represented in the test strains and correctly predicted by the trained network. Hence, the backpropagation network is clearly able to assign the strains into di¡erent DNA/ DNA hybridization groups based on their phenotypes.
Table 3 Distribution of Acinetobacter genospecies isolateda Genospecies 1^3 4 5 6 7 8/9 10 11 12 Non-identi¢ed 2 1 15 4 PAER PDCCD 1 4 3 12 2 PFS 5 15 1 PANA 3 7 10 1 1 AAER 3 1 15 1 AFS 6 2 ADCCD 2 7 12 2 1 AANA 4 1 19 1 1 2 1 AANO 2 1 24 1 3 1 6 1 2 17 9 1 2 BAER BFS 4 2 1 4 5 2 1 1 BDCCD 1 5 12 3 3 Total 20 13 16 24 162 24 3 9 3 8 a P = Phostrip process; A = A2 O process; B = Blue Plains conventional activated sludge process. Subscripts denote: AER = aerobic, ANA =anaerobic, ANO = anoxic, F+S = ¢ltered and sonicated aerobic sample, DCCD = F+S sample with 2U1034 M of DCCD. 3.3. Acinetobacter species
Two di¡erent types of activated sludges exhibiting biological P removal (Phostrip and A2 O) and the conventional AS without biological P removal were evaluated for the presence of Acinetobacter species. Despite di¡erent types of samples and locations, 83^ 95% of randomly picked colonies (22^40 colonies per plate) were typically identi¢ed as Acinetobacter species through DNA transformation of BD413 trp E27 auxotroph. The dilution plates used ranged from 1033 to 1036 , due to di¡erent concentrations of vol-
Table 2 Evaluation results of the trained networksa Genospecies Number of Number of correctly Averaged activation for the tested strains predicted strains correctly predicted strains 1^3 54 54 0.96 4 7 7 0.99 5 3 3 0.88 6 3 3 0.81 7 19 16 0.88 8/9 13 12 0.80 11 5 4 0.95 Total 104 99 a The data used for evaluation are from Baumann et al. [13] and Morton and Barrett [31].
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Number of misidenti¢ed strains 0 0 0 0 2 1 1 4
Number of nonidenti¢ed strains 0 0 0 0 1 0 0 1
224
M.H. Kim et al. / FEMS Microbiology Ecology 23 (1997) 217^227
atile suspended solids (980^3350 mg/l of biomass concentration). No direct comparison could be made about the number of Acinetobacter species isolated from ¢ltered sludge £oc and their respective unsonicated aerobic samples, nor from di¡erent sources. Overall, the percentage of Acinetobacter species in each sample was comparable to other studies, ranging between 40 and 90% [7,8,11]. A total of 282 isolates of Acinetobacter species were assigned to distinct genospecies based on their 28 phenotypic test results through the prediction of the trained network (Table 3). At least one of each nine genospecies groups was isolated from AS samples. However, a signi¢cant portion of samples including those of conventional AS samples (57%, or 162 out of total 282 Acinetobacter isolates) was identi¢ed as genospecies 7 (Acinetobacter johnsonii), with an average activation level of 0.71. This value is slightly lower than 0.8 obtained from the testing data of Baumann et al [13] and Morton and Barrett [31]. The average positive reaction values for each test of 162 isolates assigned to genospecies 7 are shown in the last column in Table 1 for comparison. 3.4. PP accumulation, L-HB utilization and nitrate reduction
Overall, approximately half of the isolates (139 out of 282 isolates) accumulated PP to a varying degree (Table 4). For the isolates from the conventional AS samples, only 19% (average value out of 10^34%) showed PP accumulation, as compared to much higher percentages of AS samples from the biological P removal plant. Furthermore, out of 71 isolates that accumulated PP with 50% stained area or greater, 22 isolates could use L-HB as a sole source of carbon and energy, indicating the capability for both PP and PHB metabolism. However, no particular trend of the isolates for PP or PHB metabolism was apparent in di¡erent AS samples, nor in di¡erent genospecies groups of Acinetobacter. Only 35 out of 282 isolates (12%) were able to reduce nitrate to nitrite; of these eight could further reduce nitrite to nitrogen gas. No distinct trend was evident for the source of the denitrifying isolates; some denitrifying isolates were obtained from the conventional AS plant.
Table 4 Results of PP accumulation and L-HB utilization tests Sample ID PP accumulationa +++ ++ + 3 % L-HBb utilization PAER 0 3 10 9 59 0 PFS 0 2 14 5 76 0 PDCCD 0 6 11 5 77 0 PANA 2 10 7 3 85 2 AAER 2 3 5 10 50 2 AFS 2 5 0 1 88 2 ADCCD 7 6 4 7 71 7 AANA 3 3 3 20 31 3 AANO 5 4 4 19 41 4 1 4 8 25 34 1 BAER BFS 0 1 1 18 10 0 BDCCD 1 1 1 21 13 1 a PP accumulation level based on % of cell surfaces stained with methylene blue. +++ = 75% or greater; ++ = 50%; +=25% or less; 3 = none. b Only the isolates that showed 50% PP accumulation or greater were tested for L-HB utilization. 4. Discussion
The delineation of di¡erent DNA groups among members of Acinetobacter by Bouvet and Grimont [17], later con¢rmed by an independent study [29], allows identi¢cation at the species level. The correlation between DNA groups and their phenotypes is invaluable in providing a simple and rapid identi¢cation [19,30]. In this study, the backpropagation neural network was evaluated for the identi¢cation of Acinetobacter genospecies, and the results were promising. Gerner-Smidt et al. [30] reported that 80% of Acinetobacter genospecies were correctly predicted at the 80% con¢dence level with the maximum likelihood method based on the same 28 phenotypic data; and the percentage correctly predicted decreased with a higher con¢dence level. In the present study, more than 95% of the 104 tested strains were correctly identi¢ed by the trained neural network with the activation level greater than 0.80 (Table 2). However, the comparison of the positive reactions of 28 tests for genospecies 7 between our data and others (Table 1) demonstrates a possibility of the misidenti¢cation either at training or at actual testing stages, suggesting that the neural technique is not perfect. Also, problems exist for genospecies 8 and 9 and genospecies 1, 2, and 3 although the group 1, 2 and 3 may be resolved by a further re¢nement of
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225
the training procedures. Nonetheless, based on the
stance, Lo ë tter and van der Merwe [36] have shown a
ability to handle variable phenotypic input data,
positive correlation between
identi¢cation of microbial species by the neural net-
phosphotransacetylase activities and P removal e¤-
work approach may provide an alternative means to
ciency.
currently used clustering or other taxonomical meth-
Most
Acinetobacter
L-HB
dehydrogenase/
isolates (247 out of 282) were
ods. Moreover, other phenotypic data such as mem-
unable to reduce nitrate to nitrite. These results
brane protein and fatty acid pro¢les can also be used
agree with Juni [37] in that most members of the
as input data for neural networks. For example,
genus
methyl-esters of intracellular fatty acids have been
This property is another desirable characteristic of
successfully used as input data for a neural network
microorganisms responsible for enhanced P removal.
to assign di¡erent marine bacteria [22].
The presence of nitrate in anaerobic zones has been
As compared to the data of Baumann et al. [13]
Acinetobacter
shown to adversely a¡ect P release and PHB accu-
that mainly consisted of genospecies complex 1^3
mulation.
from soil isolates, genospecies 7 was shown to be
would
Acinetobacter
predominant
in general do not reduce nitrate.
If
have
Acinetobacter metabolized
were
short
denitri¢ers, chain
fatty
they acids
species from AS samples
with nitrate respiration, and not stored them as
in this study as also reported by others [24,32]. The
PHB. Further, the higher redox environment posed
signi¢cant number of
Acinetobacter
species in all
by the presence of nitrate may inhibit either P release
sludge samples further shows that they are pervasive
or fermentation activities of other facultative anae-
under various environmental conditions. However,
robes.
a
higher
the
percentage
ability
to
of
the
accumulate
PP
isolates was
exhibiting
found
from
In conclusion, the wide occurrence and physiological characteristics of
Acinetobacter
species (PP and
samples of biological P removal processes. In other
PHB accumulation and inability to reduce nitrate) in
Acinetobacter
conventional or modi¢ed AS plants clearly indicate
words, the ability to accumulate PP by
Acinetobacter species exhibit desirable character-
species might be enhanced by the imposed anaerobic
that
stress.
istics for enhanced biological P removal. Also, the
U
34
did not signi¢cantly decrease the num-
Acinetobacter species, particularly Acinetobacter johnsonii, under varied environmental
ber of isolates from the ¢ltered-biomass samples. Be-
conditions suggests that P removing capability can
cause DCCD inhibits ATPase that would otherwise
be obtained by imposing alternating anaerobic/aero-
generate ATP by respiration, the results indicate a
bic conditions. However, roles of other PP-accumu-
possible existence of an alternative mode of metabol-
lating bacteria, e¡ects of facultative anaerobes, and
The use of 2
Acinetobacter
10
ic energy generation in
M DCCD in the isolation of
Acinetobacter
pervasiveness of
[33]. For exam-
environmental conditions such as organic loadings
Aci-
and pH on the eco-physiology of enhanced P remov-
strain 210A has been demonstrated [34],
ing bacterial species under strict anaerobic condi-
and the occurrence of high PP :AMP phosphotrans-
tions warrant further investigation. Finally, the use
Acine-
of a neural network approach for identifying bacte-
ple, ATP production from PP in cell extracts of
netobacter
ferase activities among other PP-accumulating
tobacter
isolates also reported [19].
rial species appears promising.
The propensity for both PP and PHB metabolism was widespread among the
Acinetobacter
isolates ;
the ability of accumulating PP and PHB, however,
Acknowledgments
was not con¢ned to some unique genospecies as reported in this study and others [35]. Both PP and
The BD413 trp E27 auxotroph provided by Dr.
PHB metabolisms are critical for microorganisms re-
R.C. Bayly of Monash University, Victoria, Austral-
sponsible for biological P removal in AS processes
ia and useful discussions on neural network tech-
since the role of PP hydrolysis is to provide energy
niques with Dr. Ming He are gratefully acknowl-
necessary for the uptake of short fatty acids to ac-
edged. The support from the NSF BES-9625183 is
cumulate PHB under anaerobic conditions. For in-
acknowledged.
FEMSEC 810 5-11-97
M.H. Kim et al. / FEMS Microbiology Ecology 23 (1997) 217^227
226
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Environ. Microbiol. 57, 3585^3592. [11] Cloete, T.E. and Steyn, P.L. (1988) The role of
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as a phosphorus removing agent in activated sludge. Water Res. 22, 971^976. [12] Hiraishi, A., Masamune, K. and Kitamura, H. (1989) Characterization of the bacterial population structure in an anaerobic-aerobic activated sludge system on the basis of respiratory quinone pro¢les. Appl. Environ. Microbiol. 55, 897^901. [13] Baumann, P.M., Doudoro¡, M. and Stanier, R.Y. (1968) A
Moraxella Acinetobacter). J.
study of the
group. II. Oxidase-negative species
(genus
Bacteriol. 95, 1520^1541.
[14] Juni, E. (1972) Interspecies transformation of
Acinetobacter :
genetic evidence for a ubiquitous genus. J. Bacteriol. 112, 917^ 931. [15] Pagel, J.E. and Seyfried, P.L. (1976) Numerical taxonomy of aquatic
Acinetobacter with the recognition of Acinetobacter baumannii sp. nov., Acinetobacter haemolyticus sp. nov., Acinetobacter johnsonii sp. nov., and Acinetobacter junii sp. nov. and emended descriptions of Acinetobacter calcoaceticus and Acinetobacter lwo¤i. Int. J. Syst. Bacteriol. 36, 228^240. genus
Acinetobacter
species. J. Gen. Microbiol. 95, 220^232.
[16] Johnson, J.L., Anderson, R.S. and Ordal, E.J. (1970) Nucleic acid homologies among oxidase-negative
Moraxella
species.
J. Bacteriol. 101, 568^573. [17] Bouvet, P.J.M. and Grimont, P.A.D. (1986) Taxonomy of the
[18] van Landschoot, A., Rossau, R. and De Ley, J. (1986) Intraand intergeneric similarities of the ribosomal ribonucleic acid
Acinetobacter.
cistrons of
Int. J. Syst. Bacteriol., 36, 150^
160. [19] Ka ë mpfer, P., Bark, K., Busse, H.-J., Auling, G. and Dott, W. (1992) Numerical and chemotaxonomy of polyphosphate ac-
Acinetobacter
cumulating
strains
with
high
polyphos-
phate :AMP phosphotransferase (PPAT) activity. Syst. Appl. Microbiol. 15, 409^419. [20] Rumelhart, D.E. and McClelland, J.L. (1986) Parallel Distributed Processing, Vols. 1 and 2. MIT Press, Cambridge, MA. [21] Kennedy, M.J. and Thakur, M.S. (1993) The use of neural networks to aid in microorganism identi¢cation : a case study of
Haemophilus
species identi¢cation. Antonie van Leeuwen-
hoek 63, 35^38. [22] Bertone, S., Giacomini, M., Ruggiero, C., Piccarolo, C. and Calegari, L. (1996) Automated systems for identi¢cation of heterotrophic marine bacteria on the basis of their fatty acid composition. Appl. Environ. Microbiol. 62, 2122^2132. [23] Banks, C.J. and Walker, I. (1977) Sonication of activated sludge £ocs and the recovery of their bacteria on solid media. J. Gen. Microbiol. 98, 363^368. [24] Beacham, A.M., Seviour, R.J. and Lindrea, K.C. (1992) Polyphosphate
accumulating
abilities
of
Acinetobacter
isolates
from a biological nutrient removal pilot plant. Water Res. 26, 121^122. [25] Cruikshank,
R.
(1965)
Medical
Microbiology,
11th
edn.
Churchill-Livingstone, Edinburgh. [26] Gerhardt, P. (1981) Manual of Methods for General Bacteriology. American Society of Microbiology, Washington, DC. [27] Dawes, E.A. and Senior, P.J. (1973) The role and regulation of energy reserve polymers in microorganism. Adv. Microbial. Physiol. 10, 135^266. [28] Baughman, D.R. and Liu, Y.A. (1995) Neural Networks in Bioprocessing and Chemical Engineering. Academic Press, San Diego, CA. [29] Tjernberg, I. and Ursing, J. (1989) Clinical strains of
bacter
Acineto-
classi¢ed by DNA-DNA hybridization. APMIS 97,
595^605. [30] Gerner-Smidt, P., Tjernberg, I. and Ursing, J. (1991) Reliability of phenotypic tests for identi¢cation of
Acinetobacter
spe-
cies. J. Clin. Microbiol. 29, 277^282. [31] Morton, D.J. and Barrett, E. (1982) Gram-negative respiratory bacteria which cause ropy milk constitute a distinct cluster within the genus
Acinetobacter.
Curr. Microbiol. 7, 107^
112. [32] Duncan, A., Vasiliadis, G.E., Bayly, R.C. and May, J.W. (1988) Genospecies of
Acinetobacter
isolated from activated
sludge showing enhanced removal of phosphate during pilotscale treatment of sewage. Biotechnol. Lett. 10, 831^836. [33] Suresh, N., Warburg, R., Timmerman, M., Wells, J., Coccia, M., Roberts, M.F. and Halvorson, H.O. (1985) New strategies
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227
bacter strain 210A and its identi¢cation as Acinetobacter johnsonii. FEMS Microbiol. Ecol. 102, 57^64.
[34] van Groenestijn, J.W., Deinema, M.H. and Zehnder, A.J.B.
[36] Lo ë tter, L.H. and van der Merwe, E.H.M. (1987) The activities
Acinetobacter
of some fermentation enzymes in activated sludge and their
(1987) ATP production from polyphosphate in
relationship to enhanced phosphorous removal. Water Res.
strain 210A. Arch. Microbiol. 148, 14^19. [35] Bonting, C.F.C., Willemsen, B.M.F., van Vliet, W.A., Bouvet, P.J.M., Kortstee, G.J.J. and Zehnder, A.J.B. (1992) Additional characteristics of the polyphosphate-accumulating
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21, 1307^1310. [37] Juni, E. (1978) Genetics and physiology of Annu. Rev. Microbiol. 32, 349^371.
FEMSEC 810 5-11-97
Acinetobacter.
isolates from di¡erent activated sludge processes: characteristics and neural network identi¢cation
Acinetobacter
Michael H. Kim a , Oliver J. Hao a *, Nam S. Wang ;
a b
b
Department of Civil Engineering, University of Maryland, College Park, MD 20742, USA
Department of Chemical Engineering, University of Maryland, College Park, MD 20742, USA
Received 5 March 1997; revised 18 April 1997; accepted 18 April 1997
Abstract
species were isolated from various full-scale activated sludge processes based on their abilities to transform an BD413 trp E27 auxotroph. Approximately half of the Acinetobacter isolates (149 out of 282 isolates) were able to accumulate polyphosphate, and some used L-hydroxybutyrate as a sole carbon and energy source. Additionally, most of the Acinetobacter isolates were unable to reduce nitrate. These characteristics of Acinetobacter species are desirable for microorganisms responsible for enhanced phosphorus removal in different activated sludge processes. The backpropagation neural network technique was further applied to assign the isolates to distinct Acinetobacter genospecies based on their phenotypic characteristics. In particular, Acinetobacter johnsonii was consistently the major genospecies from different samples obtained from the enhanced phosphorus removal processes or the conventional plant without biological phosphorus removal. Acinetobacter
Acinetobacter calcoaceticus
Keywords :
reduction ;
Activated sludge process; Neural network; Enhanced phosphorus removal; Polyphosphate; Poly-L-hydroxybutyrate; Nitrate Acinetobacter
1. Introduction
Most wastewater treatment plants are required to remove phosphorus (P) because of its role in eutrophication. The biological means for P removal provides a cost-e¡ective alternative to chemical precipitation methods. The conventional activated sludge (AS) process can be easily modi¢ed to remove P along with organics when an anaerobic stress is imposed on the process [1]. AS microorganisms exposed to alternating anaerobic/aerobic conditions ex* Corresponding author. Tel.: +1 (301) 405-1961; Fax: +1 (301) 405-2585; E-mail: [email protected]
hibit enhanced P removal capability far greater than the normal microbial growth requirements by storing ortho-P as intracellular polyphosphate (PP) under aerobic conditions [2,3]. The stored PP is then hydrolyzed to yield the energy necessary for formation of carbon reserve polymers, e.g. poly-L-hydroxybutyrate (PHB), under the imposed anaerobic conditions. Among AS bacteria, Acinetobacter species have been believed to be responsible for enhanced biological P removal [4,5]. They are strictly aerobic, Gramnegative, non-motile, catalase-positive, and oxidasenegative; they grow well on short chain fatty acids (e.g. acetate), but not on simple carbohydrates (e.g.
0168-6496 / 97 / $17.00 ß 1997 Federation of European Microbiological Societies. Published by Elsevier Science B.V. PII S 0 1 6 8 - 6 4 9 6 ( 9 7 ) 0 0 0 2 5 - 1
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M.H. Kim et al. / FEMS Microbiology Ecology 23 (1997) 217^227
218
glucose).
Based
on
these
nutritional
Fuhs and Chen [6] ¢rst suggested that
requirements,
quently,
Acinetobacter
phenotypic test results as an input was used to assign
a
neural
Acinetobacter
network
modeling
[20]
using
28
species use incomplete oxidation products, such as
the
acetate and ethanol generated by facultative anae-
tion groups [17]. Thus, the extent of the distribution
robes
of di¡erent
under
anaerobic
conditions,
to
store
PHB.
Subsequently, under carbon-limiting aerobic condi-
Acinetobacter
tions,
isolates to DNA/DNA hybridiza-
Acinetobacter
genospecies in various en-
vironmental conditions could be examined.
cells exhibit a competitive edge
A neural network can correctly capture seemingly
over other aerobic heterotrophs and proliferate in
complex input and output patterns by mimicking the
such
have
shown
learning
Acinetobacter
species
backpropagation neural network that learns and ap-
are present in alternating anaerobic/aerobic AS proc-
proximates input and output patterns by examples
esses [6^10]. However, other PP-accumulating bacte-
has been applied to speech synthesis and recognition,
ria may also play a role in enhanced P removal proc-
visual pattern recognition, analysis of sonar signals,
esses [9^12].
medical diagnosis, and learning in process control
that
systems. a
In
signi¢cant
fact,
Acinetobacter
The
many
number
has
studies
of
also
been
frequently
ability
of
the
brain
[21].
In
particular,
a
iso-
systems [20]. However, the use of neural networks
lated from other sources (e.g. soils and water) and
for identi¢cation of microorganisms is rather limited.
shown to be phenotypically and genotypically heter-
For example, only two case studies of identi¢cation
ogeneous [13]. The identi¢cation of the
Acinetobacter
Haemophilus
using a neural network approach for
genus is often based on the results of simple bio-
species [21] and marine bacteria [22] have been re-
chemical tests, e.g. API test kit (4, 5), despite the
ported. In the present study, a non-linear learning
fact that the transformation assay for
Acinetobacter
is well established [14]. The earliest attempts to classify
Acinetobacter
into di¡erent species were based
capability
of
a
neural
bacter
was used to identify
carbon substrates [13,15] that showed two or four
species
broad
bacter
[16,17],
DNA/DNA
rRNA/DNA
hybrid
hybrid
thermal
thermal
stability
stability
and
16S rRNA sequence similarity map [18] have also been used to classify di¡erent Furthermore,
Acinetobacter
chemotaxonomic
methods
species.
based
was
evaluated
for
Acineto-
genospecies, and the trained neural network
on the numerical taxonomy on the use of di¡erent
phena.
network
highly variable phenotypic characteristics of
level.
Acinetobacter
Finally,
di¡erent
isolates at geno-
groups
of
Acineto-
genospecies were characterized for PP accu-
mulation,
denitri¢cation
capability,
and
PHB
utilization for their possible role in enhanced P removal processes.
on
the analysis of a particular biomarker such as respiratory quinone [12], diaminopropane [10], fatty acid
2. Materials and methods
and soluble proteins [19] pro¢les, have been used for identi¢cation Among
and/or
these,
estimation
however,
of
phenotypic
Acinetobacter. tests
2.1. AS samples
provide
the most simple and rapid method for isolated cul-
Samples were obtained from two biological P re-
tures. In particular, the 28 phenotypic characteristics
moval
AS
correlated with DNA/DNA hybridization data [17]
water
treatment
provide a comprehensive classi¢cation scheme and
Both
facilitate identi¢cation of di¡erent
Acinetobacter gen-
Phostrip
at
plant
(Fig.
the in
1a)
Little
Patuxent
Howard
and
2
A O
waste-
County,
MD.
processes
(Fig.
1b) are designed for e¡ective P removal by the imposed anaerobic conditions. However, the Phostrip
ospecies.
Aci-
process does not involve nitri¢cation and denitri¢ca-
genospecies present in the `man-made' AS
tion, whereas nitri¢cation/denitri¢cation is achieved
To better understand the diversity of di¡erent
netobacter
processes
Acinetobacter
2
species were isolated from
in the A O process by recycling the nitri¢ed e¥uent
various biological P removal and conventional AS
through anoxic zones (without oxygen, but with ni-
processes. The identi¢cation at the genus level was
trate). The AS samples were also obtained from a
Acinetobacter
conventional plant (Fig. 1c) without biological P re-
BD413 trp E27 auxotroph [14]. Subse-
moval at the Blue Plains wastewater treatment plant
ecosystem,
based on their abilities to transform an
calcoaceticus
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M.H. Kim et al. / FEMS Microbiology Ecology 23 (1997) 217^227
219
Fig. 1. Source of AS samples from di¡erent AS processes.
in Washington, DC. The locations of samples from
Na2 B4 O7 W10H2 O.
di¡erent zones are shown in Fig. 1.
(ADM ; basal medium supplemented with 2 g of
The
acetate
de¢ned
medium
Na acetate per liter) was used for isolation.
3
1 Sludge samples were initially diluted (10 ) in a 3 M phosphate bu¡er solution (pH = 7.2) and 10
2.2. Isolation procedure
3
claving) contained the following ingredients per liter :
mixed in a high speed blender for 20 min. The 2 8 blended sludge was further diluted (10 ^10 ),
A basal medium (pH adjusted to 7.4 before auto-
3
3
1 g of (NH4 )2 SO4 , 2 g KH2 PO4 , 0.8 g MgSO4 W7H2 O,
plated on ADM agar, and incubated at 20³C for
10 ml of the stock mineral solution, and 1 ml of trace
7 days. Random isolates from plates were picked
metal solution. The stock mineral solution contained
for further tests. Additionally, for each aerobic zone
(per liter) 20 g nitriloacetic acid, 14.6 g KOH, 6.6 g
sample from the three AS processes, 10 ml of each
CaCl2 W2H2 O, 0.0186 g (NH4 )8 Mo7 O24 W4H2 O, and 0.2
blended sludge sample was further ¢ltered through a
g FeSO4 W7H2 O. The trace metal solution contained
Whatman No. 2 ¢lter. The biomass on each ¢lter
(per
g
paper was washed 10 times with 1 ml phosphate buf-
g
fer solution and transferred into a test tube. The bio-
MnSO4 W4H2 O, 0.04 g CuSO4 W5H2 O, and 0.018 g
mass was sonicated (Bransonic Model 350) at a
liter)
0.25
ZnSO4 W7H2 O,
g 0.5
EDTA g
(disodium
FeSO4 W7H2 O,
salt),
1.1
0.154
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M.H. Kim et al. / FEMS Microbiology Ecology 23 (1997) 217^227
Fig. 2. Structure of backpropagation neural network.
power output of approximately 26 J/s for 100 s to break up the sludge £ocs [23]. The sonicated samples were further diluted, and plated onto ADM plates with or without 2U1034 M DCCD (N,NP-dicyclohexylcarbodiimide, an ATPase inhibitor). The additional treatments to aerobic sludge samples were performed to enhance the isolation of Acinetobacter from sludge £ocs and to evaluate any alternative ATP generation from the stored PP. Initially, the colonies were examined microscopically on a wet mount for motility; colonies showing £agellum motility were discarded. Cells were further characterized by Gram-staining, and only Gram-negative isolates were transferred to ADM plates for further puri¢cation. After 2 days of incubation at 30³C, purity was con¢rmed microscopically.
Na3 citrateW2H2 O, and heating at 65³C for 2 h. A small amount of auxotroph, cultured overnight in the ADM broth supplemented with 100 mg/l of Ltryptophan, was mixed with approximately 0.1 ml of each crude DNA solution in a spot (ca. 1 cm diameter) on an ADM agar plate supplemented with 50 mg/l of L-tryptophan. The non-DNA added auxotroph and the DNA solution alone (without cells) were also spread on the same plate as control. After incubation at 30³C for 2 days, colonies from DNA and non-DNA treated sample were streaked onto ADM agar plate without L-tryptophan supplement and incubated at 30³C for 2 days. Only transformants exhibiting growth on ADM without L-tryptophan supplement were positively identi¢ed as Acinetobacter.
2.3. DNA transformation assay
2.4. Biochemical characterization
The colony transformation method using the competent strain BD413 trp E27 obtained from Dr. R. Bayly (Monash University, Victoria, Australia) was performed as described by Juni [14]. A crude DNA was extracted by adding a loopful culture of each isolate in a solution consisting of 0.05% sodium dodecyl sulfate, 0.15 M NaCl, and 0.015 M
For each Acinetobacter isolate, the 28 physiological, biochemical and nutritional tests (Table 1) were performed as described by Bouvet and Grimont [17]. 2.5. PP accumulation and nitrate reduction
The accumulation of PP in each isolate was tested
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M.H. Kim et al. / FEMS Microbiology Ecology 23 (1997) 217^227
with the ADM broth in which the concentrations of the original P and carbon source were increased to 100 mg/l P and 5.67 g/l NaAc, respectively [24]. Each isolate was stained with a modi¢ed Neisser method [25]. The capability of isolates to reduce nitrate (to nitrite) and denitrify (to N2 ) was tested with tryptocasein soy broth, supplemented with 0.17% agar and 0.1 g KNO3 /l [26]. The concentration of nitrite was monitored spectrometrically at 540 nm. For those isolates with only nitrate reduction capabilities, nitrite accumulated in the medium. However, isolates that could denitrify accumulated nitrite initially; nitrite then disappeared due to further reduction to N2 . 2.6. PHB metabolism
The key enzyme, L-hydroxybutyrate (L-HB) dehydrogenase, is common to the pathways of both L-HB utilization and PHB synthesis in all organisms capable of PHB metabolism [27]. Therefore, the result of L-HB utilization tests was used as an indicator for PHB metabolism for those isolates exhibiting PP accumulation. 2.7. Backpropagation neural network
The backpropagation neural network consisted of input (i neurons), hidden (j neurons), and output (k neurons) layers with a bias at the input and hidden layers (Fig. 2). Adjacent layers are interconnected by weighted branches, i.e. weight matrices ji and kj for input to hidden and hidden to output layers, respectively, where subscripts denote weights associated with ith neuron of the input layer to jth neuron of the hidden layer and jth neuron of the hidden layer to kth neuron of the output layer. In the forward propagation mode, a set of input data ( vector) is presented to neurons in the input layer which simply transmit the input values to the hidden layer, and, subsequently, each neuron in the hidden and output layers except the bias unit calculates its activation (c) as a function of the weighted sum (z) of its input as follows: 1 1 c z 1 e3z V
W
x
zj
X 0 X
2a
Wji xi
Vkj cj
i
zk
0
2b
j
In a learning mode, sets of training examples consisting of n input and output vector pairs ( and ) are repeatedly presented to the network. Each neuron in the output layer predicts a single real number, based on the input ( ) and current weight values of ji and kj . The objective is to iteratively search for sets of weights ( ji and kj ) that minimize the sum of squared errors (J) between the predictions ( ) and the desired outputs ( ) over all n training examples: n 2 yp 3dp 3 minw v J x
d
x
V
W
V
W
;
X
y
d
1
p
In the backpropagation mode, the weights ji and are updated after presentation of each sample in proportion to the gradient of the error between the prediction and actual values at the rth iteration step as: 4 vr Vkj R drk 3yrk y2rk 13yrk and 2 vr Wji R 13drk drk Nrk Vkj 5 W
Vkj
X k
where Nrk is further de¢ned as 6 Nrk drk 3yrk yrk 13yrk In Eqs. 4 and 5, R is a learning parameter that has a value less than 1 which decreases as the training proceeds. Detailed information about neural network fundamentals as well as implementation can be found in Baughman and Liu [28]. 3. Results
3.1. Training of a neural network
Genospecies group 1^12 that had been genotypically and phenotypically characterized by Bouvet
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Table 1 Phenotypic characteristics of Acinetobacter genospecies [17] Characteristic Positive strains of each genospeciesa (%) 1 2 3 4 5 6 7 8/9 10 11 12 this studyb (8) (121) (15) (23) (17) (3) (23) (34) (4) (4) (3) (162) Growth at 44³C 3 + 3 3 3 3 3 3 3 3 3 3 Growth at 41³C 3 + + 3 90 3 3 3 3 3 3 4 Growth at 37³C + + + + + + 3 + + + + 20 Gelatin hydrolysis 3 3 3 96 3 + 3 3 3 3 3 7 Hemolysis 3 3 3 + 3 + 3 3 3 3 3 8 Q-Glutamyltransferase + 99 + 4 3 66 3 3 3 3 3 10 Citrate (Simmons) + + + 91 82 + + 3 + + 3 39 Glucose oxidation + 95 + 52 3 66 3 6 + 3 33 2 L-Xylosidase 3 95 + 52 3 66 3 6 3 3 3 2 Utilization of: DL-Lactate + + + 3 + 3 + + + + + 44 Glutarate + + + 3 3 3 3 3 + + + 15 L-Phenylalanine + 87 66 3 3 3 3 3 3 3 + 6 Phenylacetate + 87 66 3 3 3 3 94 25 50 + 3 Malonate + 98 87 3 3 3 13 3 3 3 + 5 L-Histidine + 98 87 96 + + 3 3 + + 3 25 Azelate + 90 + 3 3 3 3 + 50 25 + 8 D-Malate 3 98 + 96 + 66 22 76 + + 3 4 L-Aspartate + + + 64 40 66 61 3 + 75 3 37 L-Leucine 38 97 94 96 11 + 3 3 3 3 + 10 Histamine 3 3 3 3 3 3 3 3 + 75 3 7 L-Tyrosine + + + 5 60 66 70 3 + 75 + 23 L-Alanine + 95 94 3 3 3 3 3 + + 3 19 Ethanol + + + 96 + + + 97 + + + 99 2,3-Butanediol + + + 3 3 3 35 3 + + + 26 trans-Aconitate + 99 + 52 3 3 3 3 3 3 3 4 L-Arginine + 98 + 96 95 + 35 3 3 3 + 28 L-Ornithine + 93 + 3 3 3 4 2 3 3 3 10 DL-4-Aminobutyrate + + + + 88 3 35 40 + + + 23 a + = 100% positive; 3 = 100% negative. The number in parentheses indicates the number of each respective genospecies tested. b For 162 isolates identi¢ed as genospecies 7 in this study.
and Grimont [17] were used for training of the neural network. Additional groups of Acinetobacter genospecies, described by Tjernberg and Ursing [29], were not consolidated for the neural network identi¢cation, since only a few phenotypic properties of these additional genotypes were investigated. Among genospecies 1^12, species 8 and 9 have been grouped together in this study since they could not be distinguished from their phenotypic characters [17]. Additionally, because genospecies 1^3 share similar phenotypes [19,30], these genospecies have also been grouped as one to yield a total of nine groups out of 12 genospecies for training of the network.
One thousand training data points, equally distributed among the regrouped nine genospecies, were randomly generated based on the percent positive reaction values (Table 1); e.g. each genospecies 7 training strain independently has 61% probability of being positive for L-aspartate, and so on. Each of 28 unit characters (numerical value of 1 for positive and 0 for negative) was used as the input to the network. Initially, 10^100 hidden neurons were investigated and the optimum number of hidden neurons yielding the lowest overall error (J) of the training set was determined to be 15. Approximately 30 000 iterations with 15 hidden neurons were subsequently used to train the network. The number of
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the output layer consisted of nine neurons, representing each one of the nine genospecies. 3.2. Neural network performance evaluation
Phenotypic data of Acinetobacter genospecies from Baumann et al. [13] and Morton and Barrett [31], as discussed and assigned to genospecies 1^12 by Bouvet and Grimont [17], were used to test the trained network. Independent testing data are necessary to validate the prediction by the trained network, prior to identi¢cation of the isolates to the genospecies level. However, only 21 or 18 out of 28 tests employed by Bouvet and Grimont [17] were characterized by Baumann et al. [13] or Morton and Barrett [31], respectively. Therefore, the remaining input data were obtained from the Bouvet and Grimont percent positive reaction values (Table 1), similar to procedures used in the training of the network. Ninety-nine strains out of a total of 104 were correctly predicted by the trained network with an average activation value above 80% (Table 2). The prediction was based on the highest neuron activation level at the output layer; if any particular testing set exhibited its highest activation level below 0.5, the isolate was designated as non-identi¢ed. Each of the nine genospecies groups with the exception of species 10 and 12 was represented in the test strains and correctly predicted by the trained network. Hence, the backpropagation network is clearly able to assign the strains into di¡erent DNA/ DNA hybridization groups based on their phenotypes.
Table 3 Distribution of Acinetobacter genospecies isolateda Genospecies 1^3 4 5 6 7 8/9 10 11 12 Non-identi¢ed 2 1 15 4 PAER PDCCD 1 4 3 12 2 PFS 5 15 1 PANA 3 7 10 1 1 AAER 3 1 15 1 AFS 6 2 ADCCD 2 7 12 2 1 AANA 4 1 19 1 1 2 1 AANO 2 1 24 1 3 1 6 1 2 17 9 1 2 BAER BFS 4 2 1 4 5 2 1 1 BDCCD 1 5 12 3 3 Total 20 13 16 24 162 24 3 9 3 8 a P = Phostrip process; A = A2 O process; B = Blue Plains conventional activated sludge process. Subscripts denote: AER = aerobic, ANA =anaerobic, ANO = anoxic, F+S = ¢ltered and sonicated aerobic sample, DCCD = F+S sample with 2U1034 M of DCCD. 3.3. Acinetobacter species
Two di¡erent types of activated sludges exhibiting biological P removal (Phostrip and A2 O) and the conventional AS without biological P removal were evaluated for the presence of Acinetobacter species. Despite di¡erent types of samples and locations, 83^ 95% of randomly picked colonies (22^40 colonies per plate) were typically identi¢ed as Acinetobacter species through DNA transformation of BD413 trp E27 auxotroph. The dilution plates used ranged from 1033 to 1036 , due to di¡erent concentrations of vol-
Table 2 Evaluation results of the trained networksa Genospecies Number of Number of correctly Averaged activation for the tested strains predicted strains correctly predicted strains 1^3 54 54 0.96 4 7 7 0.99 5 3 3 0.88 6 3 3 0.81 7 19 16 0.88 8/9 13 12 0.80 11 5 4 0.95 Total 104 99 a The data used for evaluation are from Baumann et al. [13] and Morton and Barrett [31].
FEMSEC 810 5-11-97
Number of misidenti¢ed strains 0 0 0 0 2 1 1 4
Number of nonidenti¢ed strains 0 0 0 0 1 0 0 1
224
M.H. Kim et al. / FEMS Microbiology Ecology 23 (1997) 217^227
atile suspended solids (980^3350 mg/l of biomass concentration). No direct comparison could be made about the number of Acinetobacter species isolated from ¢ltered sludge £oc and their respective unsonicated aerobic samples, nor from di¡erent sources. Overall, the percentage of Acinetobacter species in each sample was comparable to other studies, ranging between 40 and 90% [7,8,11]. A total of 282 isolates of Acinetobacter species were assigned to distinct genospecies based on their 28 phenotypic test results through the prediction of the trained network (Table 3). At least one of each nine genospecies groups was isolated from AS samples. However, a signi¢cant portion of samples including those of conventional AS samples (57%, or 162 out of total 282 Acinetobacter isolates) was identi¢ed as genospecies 7 (Acinetobacter johnsonii), with an average activation level of 0.71. This value is slightly lower than 0.8 obtained from the testing data of Baumann et al [13] and Morton and Barrett [31]. The average positive reaction values for each test of 162 isolates assigned to genospecies 7 are shown in the last column in Table 1 for comparison. 3.4. PP accumulation, L-HB utilization and nitrate reduction
Overall, approximately half of the isolates (139 out of 282 isolates) accumulated PP to a varying degree (Table 4). For the isolates from the conventional AS samples, only 19% (average value out of 10^34%) showed PP accumulation, as compared to much higher percentages of AS samples from the biological P removal plant. Furthermore, out of 71 isolates that accumulated PP with 50% stained area or greater, 22 isolates could use L-HB as a sole source of carbon and energy, indicating the capability for both PP and PHB metabolism. However, no particular trend of the isolates for PP or PHB metabolism was apparent in di¡erent AS samples, nor in di¡erent genospecies groups of Acinetobacter. Only 35 out of 282 isolates (12%) were able to reduce nitrate to nitrite; of these eight could further reduce nitrite to nitrogen gas. No distinct trend was evident for the source of the denitrifying isolates; some denitrifying isolates were obtained from the conventional AS plant.
Table 4 Results of PP accumulation and L-HB utilization tests Sample ID PP accumulationa +++ ++ + 3 % L-HBb utilization PAER 0 3 10 9 59 0 PFS 0 2 14 5 76 0 PDCCD 0 6 11 5 77 0 PANA 2 10 7 3 85 2 AAER 2 3 5 10 50 2 AFS 2 5 0 1 88 2 ADCCD 7 6 4 7 71 7 AANA 3 3 3 20 31 3 AANO 5 4 4 19 41 4 1 4 8 25 34 1 BAER BFS 0 1 1 18 10 0 BDCCD 1 1 1 21 13 1 a PP accumulation level based on % of cell surfaces stained with methylene blue. +++ = 75% or greater; ++ = 50%; +=25% or less; 3 = none. b Only the isolates that showed 50% PP accumulation or greater were tested for L-HB utilization. 4. Discussion
The delineation of di¡erent DNA groups among members of Acinetobacter by Bouvet and Grimont [17], later con¢rmed by an independent study [29], allows identi¢cation at the species level. The correlation between DNA groups and their phenotypes is invaluable in providing a simple and rapid identi¢cation [19,30]. In this study, the backpropagation neural network was evaluated for the identi¢cation of Acinetobacter genospecies, and the results were promising. Gerner-Smidt et al. [30] reported that 80% of Acinetobacter genospecies were correctly predicted at the 80% con¢dence level with the maximum likelihood method based on the same 28 phenotypic data; and the percentage correctly predicted decreased with a higher con¢dence level. In the present study, more than 95% of the 104 tested strains were correctly identi¢ed by the trained neural network with the activation level greater than 0.80 (Table 2). However, the comparison of the positive reactions of 28 tests for genospecies 7 between our data and others (Table 1) demonstrates a possibility of the misidenti¢cation either at training or at actual testing stages, suggesting that the neural technique is not perfect. Also, problems exist for genospecies 8 and 9 and genospecies 1, 2, and 3 although the group 1, 2 and 3 may be resolved by a further re¢nement of
FEMSEC 810 5-11-97
M.H. Kim et al. / FEMS Microbiology Ecology 23 (1997) 217^227
225
the training procedures. Nonetheless, based on the
stance, Lo ë tter and van der Merwe [36] have shown a
ability to handle variable phenotypic input data,
positive correlation between
identi¢cation of microbial species by the neural net-
phosphotransacetylase activities and P removal e¤-
work approach may provide an alternative means to
ciency.
currently used clustering or other taxonomical meth-
Most
Acinetobacter
L-HB
dehydrogenase/
isolates (247 out of 282) were
ods. Moreover, other phenotypic data such as mem-
unable to reduce nitrate to nitrite. These results
brane protein and fatty acid pro¢les can also be used
agree with Juni [37] in that most members of the
as input data for neural networks. For example,
genus
methyl-esters of intracellular fatty acids have been
This property is another desirable characteristic of
successfully used as input data for a neural network
microorganisms responsible for enhanced P removal.
to assign di¡erent marine bacteria [22].
The presence of nitrate in anaerobic zones has been
As compared to the data of Baumann et al. [13]
Acinetobacter
shown to adversely a¡ect P release and PHB accu-
that mainly consisted of genospecies complex 1^3
mulation.
from soil isolates, genospecies 7 was shown to be
would
Acinetobacter
predominant
in general do not reduce nitrate.
If
have
Acinetobacter metabolized
were
short
denitri¢ers, chain
fatty
they acids
species from AS samples
with nitrate respiration, and not stored them as
in this study as also reported by others [24,32]. The
PHB. Further, the higher redox environment posed
signi¢cant number of
Acinetobacter
species in all
by the presence of nitrate may inhibit either P release
sludge samples further shows that they are pervasive
or fermentation activities of other facultative anae-
under various environmental conditions. However,
robes.
a
higher
the
percentage
ability
to
of
the
accumulate
PP
isolates was
exhibiting
found
from
In conclusion, the wide occurrence and physiological characteristics of
Acinetobacter
species (PP and
samples of biological P removal processes. In other
PHB accumulation and inability to reduce nitrate) in
Acinetobacter
conventional or modi¢ed AS plants clearly indicate
words, the ability to accumulate PP by
Acinetobacter species exhibit desirable character-
species might be enhanced by the imposed anaerobic
that
stress.
istics for enhanced biological P removal. Also, the
U
34
did not signi¢cantly decrease the num-
Acinetobacter species, particularly Acinetobacter johnsonii, under varied environmental
ber of isolates from the ¢ltered-biomass samples. Be-
conditions suggests that P removing capability can
cause DCCD inhibits ATPase that would otherwise
be obtained by imposing alternating anaerobic/aero-
generate ATP by respiration, the results indicate a
bic conditions. However, roles of other PP-accumu-
possible existence of an alternative mode of metabol-
lating bacteria, e¡ects of facultative anaerobes, and
The use of 2
Acinetobacter
10
ic energy generation in
M DCCD in the isolation of
Acinetobacter
pervasiveness of
[33]. For exam-
environmental conditions such as organic loadings
Aci-
and pH on the eco-physiology of enhanced P remov-
strain 210A has been demonstrated [34],
ing bacterial species under strict anaerobic condi-
and the occurrence of high PP :AMP phosphotrans-
tions warrant further investigation. Finally, the use
Acine-
of a neural network approach for identifying bacte-
ple, ATP production from PP in cell extracts of
netobacter
ferase activities among other PP-accumulating
tobacter
isolates also reported [19].
rial species appears promising.
The propensity for both PP and PHB metabolism was widespread among the
Acinetobacter
isolates ;
the ability of accumulating PP and PHB, however,
Acknowledgments
was not con¢ned to some unique genospecies as reported in this study and others [35]. Both PP and
The BD413 trp E27 auxotroph provided by Dr.
PHB metabolisms are critical for microorganisms re-
R.C. Bayly of Monash University, Victoria, Austral-
sponsible for biological P removal in AS processes
ia and useful discussions on neural network tech-
since the role of PP hydrolysis is to provide energy
niques with Dr. Ming He are gratefully acknowl-
necessary for the uptake of short fatty acids to ac-
edged. The support from the NSF BES-9625183 is
cumulate PHB under anaerobic conditions. For in-
acknowledged.
FEMSEC 810 5-11-97
M.H. Kim et al. / FEMS Microbiology Ecology 23 (1997) 217^227
226
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