Changes in microbial diversity in industrial wastewater evaporation ponds following artificial salination

R E S E A R C H A R T I C L E

Changes inmicrobial diversity in industrialwastewaterevaporationponds followingarti¢cial salinationEitan Ben-Dov1,2, Orr H. Shapiro1, Ronen Gruber1, Asher Brenner1,3 & Ariel Kushmaro1,4

1Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Be’er-Sheva, Israel; 2Achva Academic College, M.P. Shikmim, Israel;3Unit of Environmental Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Be’er-Sheva, Israel; and 4National Institute for

Biotechnology, Ben-Gurion University of the Negev, Be’er-Sheva, Israel

Correspondence: Ariel Kushmaro,

Department of Biotechnology Engineering

and National Institute for Biotechnology,

Ben-Gurion University of the Negev,

PO Box 653, Beer-Sheva 84105, Israel.

Tel.: 1972 8 647 9024; fax: 1972 8 647

2983; e-mail: [email protected]

Received 4 February 2008; revised 5 May 2008;

accepted 30 May 2008.

First published online 18 July 2008.

DOI:10.1111/j.1574-6941.2008.00549.x

Editor: Alfons Stams

Keywords

microbial diversity; industrial wastewater

evaporation ponds; artificial salination; sulfate-

reducing bacteria.

Abstract

The salinity of industrial wastewater evaporation ponds was artificially increased

from 3–7% to 12–16% (w/v), in an attempt to reduce the activity of sulfate-

reducing bacteria (SRB) and subsequent emission of H2S. To investigate the

changes in bacterial diversity in general, and SRB in particular, following this

salination, two sets of universal primers targeting the 16S rRNA gene and the

functional apsA [adenosine-50-phosphosulfate (APS) reductase a-subunit] gene of

SRB were used. Phylogenetic analysis indicated that Proteobacteria was the most

dominant phylum both before and after salination (with 52% and 68%, respec-

tively), whereas Firmicutes was the second most dominant phylum before (39%)

and after (19%) salination. Sequences belonging to Bacteroidetes, Spirochaetes and

Actinobacteria were also found. Several groups of SRB from Proteobacteria and

Firmicutes were also found to inhabit this saline environment. Comparison of

bacterial diversity before and after salination of the ponds revealed both a shift in

community composition and an increase in microbial diversity following salina-

tion. The share of SRB in the 16S rRNA gene was reduced following salination,

consistent with the reduction of H2S emissions. However, the community

composition, as shown by apsA gene analysis, was not markedly affected.

Introduction

The biodiversity of indigenous microorganisms that are

capable of efficient remediation of xenobiotic pollutants in

various extreme environments (pH, temperature or salinity)

has been extensively studied in the last decade (Kanaly et al.,

2000; Demirjian et al., 2001; Kasai et al., 2001; Nogales et al.,

2001). Identification of key organisms in pollutant degrada-

tion processes is essential for the development of optimal in

situ bioremediation strategies and for a better understand-

ing of microbial food webs. Industrial wastewater environ-

ments contain a complex population of microorganisms

that metabolize organic and inorganic chemicals to generate

energy and cellular components and to counter external

osmotic pressure (Bramucci et al., 2003; Roberts, 2005).

Furthermore, intense competition for limited carbon re-

sources in durable wastewater environments may result in

the evolution of novel genes and biochemical pathways.

While the engineering aspects of industrial wastewater

bioreactors are well understood, the compositions and

interactions of microbial communities in this environment

have received little attention (Bramucci et al., 2003).

One of the negative aspects of municipal and industrial

wastewater treatments is the production of H2S (which is

also a possible precursor of other odorants and significantly

enhances microbially mediated corrosion of treatment

facilities) by anaerobic sulfate-reducing bacteria (SRB)

(Postgate, 1984). Although sulfate reduction may account

for up to 50% of the mineralization of organic matter and

biocorrosion in wastewater treatment systems, the microbial

diversity and population dynamics of SRB, at the genus level

in wastewater systems, remain mostly unknown (Kuhl &

Jorgensen, 1992; Ito et al., 2002).

In the current study, the microbial diversity of evapora-

tion ponds, holding partially treated industrial wastewater

before and after a salination process, was examined. The

ponds are the final treatment stage of a combined waste-

water stream, contributed by several chemical plants

FEMS Microbiol Ecol 66 (2008) 437–446 c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

(manufacturing various pesticides, pharmaceuticals, alipha-

tic and aromatic halogens) at the Ramat-Hovav industrial

park, in the Negev desert, Israel (Belkin et al., 1993). Organic

matter concentration in the wastewater stream is 2–2.5 g C L�1

(on the basis of total organic carbon measure), of which over

30% reaches the evaporation ponds. The major form of sulfur

in the raw wastes entering the ponds is sulfate (SO42�), with a

concentration of c. 3 g L�1 (Belkin et al., 1993). Sulfate

concentrations in the evaporation ponds have gradually

increased with time due to natural evaporation, and reached

levels of 3.5–7 g L�1 before the artificial salination. The current

level of sulfate (after salination) in the ponds is about 15 g L�1.

Receiving a mixture of saline, high-strength industrial waste-

water, these ponds make a unique habitat for various micro-

organisms (Ben-Dov et al., 2006). In order to reduce foul

odors emitted by the ponds in general and H2S as a result of

SRB activity in particular, the evaporation ponds’ salinity was

artificially increased from an initial 3–7% to a final concen-

tration of between 12% and 16% (w/v), by addition of

300 000 tons of salt (95–98% NaCl; byproduct of magnesium

production of Dead Sea Industries, Israel). Wastewater was

drawn from one end of a given evaporation pond, mixed with

solid salts in a large dissolving tank to a final salinity of 20%

(w/v) and returned to the same pond at the opposite end until

the desired concentration was achieved. While H2S odor

nuisances were reduced dramatically (from p.p.m. to p.p.b.

levels) during the few days following salination, the shift in

bacterial communities and their adaptation to the new

environmental conditions remained unknown. This informa-

tion is essential for predicting and monitoring future un-

wanted developments in this environment.

In this study, universal primers targeting the 16S rRNA

gene and conserved primers for the functional gene apsA

[encoding adenosine-50-phosphosulfate (APS) reductase a-

subunit] were used to investigate changes in bacterial

population before and after artificial salination of the

industrial wastewater evaporation ponds.

Materials and methods

Sampling and nucleic acid extraction

Salination of the evaporation ponds was accomplished

between 18 August 2003 and 27 October 2003. Sampling of

ponds before salination was carried out on 12 August 2003,

and sampling of ponds after salination was carried out

between 29 March 2004 and 8 November 2004 (at least 6

months following salination), after the ponds had reached

steady levels of salinity between 12% and 16% (w/v).

Total genomic DNA from hypersaline industrial waste-

water samples (with salinity of 3–7% before and 12–16%

after salination) was extracted from pellets (80–110 mg were

obtained from 30 mL samples) using the MoBio Power Soil

DNA isolation kit (MoBio Laboratories Inc., Solana Beach,

CA) with one modification: DNA bound to the silica filter

membrane was washed twice with a C5 solution. The

purified DNA (0.6–1.8 mg extracted from 30 mL) was eluted

in 60 mL of C6 solution (MoBio Laboratories Inc.) and

stored at � 20 1C. Genomic DNA from Escherichia coli was

obtained by the same method and DNA concentrations were

determined by an ND-1000 UV–Vis Spectrophotometer

(NanoDrop Technologies Inc., Wilmington, DE).

PCR amplification of 16S rRNA gene and apsAgene fragments

Total DNA was amplified by PCR, using a Mastercycler

gradient thermocycler (Eppendorf, Westbury, NY). The pri-

mers used were the forward primer 8F (GGATCCAGACTTT

GATYMTGGCTCAG), modified by shortening of the 50-end,

and the reverse primer 1512R (GTGAAGCTTACGGY

TAGCTTGTTACGACTT), both universal primers targeting

16S rRNA genes, and taken from Felske et al. (1997).

A c. 0.9-kb fragment of a-subunit of adenosine-50-phos-

phosulfate (APS) reductase gene apsA from total genomic

DNA was amplified, using APS7-F (GGGYCTKTCCGCYAT

CAAYAC) and APS8-R (GCACATGTCGAGGAAGTCTTC)

primers (Friedrich, 2002). Reaction mixtures included a

12.5-mL ReddyMix (PCR Master mix containing 1.5 mM

MgCl2 and 0.2 mM concentration of each deoxynucleoside

triphosphate) (ABgene, Surrey, UK), a 1 pmol each of the

forward and reverse primers, 1–2mL of the sample prepara-

tion, plus water, to bring the total volume to 25 mL. An

initial denaturation-hot start of 4 min at 95 1C was followed

by 30 cycles of the following incubation pattern: 94 1C for

40 s, 54 1C for 40 s and 72 1C for 60–120 s (depending on the

length of the fragment). The procedure was completed with

a final elongation step at 72 1C for 20 min.

Clone library construction and sequencing

PCR products were purified by electrophoresis through a

0.8% agarose gel (Sigma), stained with ethidium bromide

and visualized on a UV transilluminator. The approximately

heterologous 16S rRNA gene (1.5 kb) and apsA (0.9 kb)

products were excised from the gel and the DNA was

purified from the gel slice using the Wizard PCR Prep kit

(Promega, Madison, WI). The gel-purified PCR products

were TA cloned into the pGEM-T Easy vector (Promega), or

pCRII-TOPO-TA cloning vector, as specified by Invitrogen

(Carlsbad, CA) and transformed into calcium chloride-

competent HD5 a E. coli cells, according to the manufac-

turer’s instructions and standard techniques.

Plasmid DNA was isolated from individual clones by

the Wizard Plus SV Minipreps DNA purification system

(Promega). Aliquots from a subset of the samples of purified

plasmid DNA were digested with the restriction enzyme

FEMS Microbiol Ecol 66 (2008) 437–446c� 2008 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

438 E. Ben-Dov et al.

EcoRI (MBI Fermentas) for more than 4 h at 37 1C, and the

digested product was separated by electrophoresis on a 1%

agarose gel (Agarose Low EEO; Hispanagar, Spain). After

staining with ethidium bromide, the bands were visualized

on a UV transilluminator to select clones containing the

appropriate-sized insert.

The clones with the correct plasmid insert were then used

for sequencing. Sequencing (with M13-F and M13-R pri-

mers annealed the plasmid) or with 8F and APS7-F forward

primers for 16S rRNA and apsA genes, respectively, was

performed by an ABI PRISM dye terminator cycle sequen-

cing ready reaction kit with an AmpliTaq DNA polymerase

FS and DNA sequencer ABI model 373A system (Perkin-

Elmer).

Sequence analysis

All rRNA gene sequences of each group were first compared

with those in the GenBank database with the basic local

alignment search tool BLAST network service (http://

www.ncbi.nlm.nih.gov/blast/blast.cgi). The CLASSIFIER pro-

gram (version 1.0; assign 16S rRNA gene sequences to a

taxonomical hierarchy) and the LIBRARY COMPARE program

(compare two sequence libraries using the RDP Classifier),

available at the Ribosomal Database Project-II web site

(Maidak et al., 1999), were used to find diversity on different

ranks of related sequences. The sequences were aligned using

CLUSTALW with the MEGA package: MOLECULAR EVOLUTIONARY

GENETICS ANALYSIS, version 3.1 package (Kumar et al., 2004)

and positions not sequenced in all isolates or with alignment

uncertainties were removed. Phylogenetic trees were con-

structed by the neighbor-joining method (Saito & Nei,

1987) with the MEGA package (Kumar et al., 2004). Bootstrap

resampling analysis (Felsenstein, 1985) for 100 replicates

was performed to estimate the confidence levels of tree

topologies.

For diversity analyses, sequences were grouped into

operational taxonomic units (OTUs) on the basis of rRNA

gene sequence similarity. First, a distance matrix was gener-

ated using the MEGA package (Kumar et al., 2004). This

matrix was then fed to the DOTUR computer program with all

default options (Schloss & Handelsman, 2005). Rarefaction

curves were plotted for 90% and 97% sequence similarity

levels and were then extrapolated, using the SIGMAPLOT 2000

software pack; Chao1 richness estimates and Shannon–

Weaver diversity index were also obtained from DOTUR.

Nucleotide sequence accession number

The sequences from this study have been deposited in

the NCBI GeneBank database under accession number

DQ662411–DQ662540 for 16S rRNA gene sequences; and

EF596013–EF596023 and EF052922–EF053000 for apsA

sequences.

Results and discussion

Impact of salination process on microbialdiversity

To determine the microbial diversity in the wastewater

environment, universal 16S rRNA gene primers (8F and

1512R) were used for the amplification and subsequent

construction of 16S rRNA gene libraries. A total of 103

clones were partially sequenced (Z400 bp). The diversity of

SRB (90 clones) in this hypersaline industrial wastewater was

investigated using a pair of primers (APS7-F and APS8-R)

targeting the functional apsA gene (Friedrich, 2002).

The overall diversity of cloned sequences was analyzed on

two levels (90% and 97% similarity) using cluster analysis by

the DOTUR program (Schloss & Handelsman, 2005). The

results are summarized in Table 1. As the total number of

comparable cloned sequences for samples predating the

salination process was lower then that of clones sequenced

following salination (44 vs. 59), we also compared 44

sequences of the latter group, randomly selected. This

analysis proved essential, as results differed considerably

when using part of or the entire clone library. A total of 24

OTUs were observed from samples obtained before salina-

tion at the 97% similarity level (roughly representing species

Table 1. Number of OTUs and richness estimation of 16S rRNA gene libraries from industrial wastewater evaporation ponds before and after

salination�

16S rRNA gene clone library

No. of clones

sequenced

Richness estimators

No. of OTUs Chao1 value Shannon–Weaver index

Cutoff 97% Cutoff 90% Cutoff 97% Cutoff 90% Cutoff 97% Cutoff 90%

Before salination 44 24 17 75 24 2.85 2.55

After salination 59 33 (28)w 24 (22)w 88 (98)w 45 (62)w 3.19 (3.13)w 2.71 (2.72)w

�Shannon–Weaver diversity index and Chao1 richness estimator were computed using DOTUR.

Numbers of OTUs, Chao1 estimated richness and Shannon–Weaver diversity index are shown for both 3% and 10% differences in nucleic acid

sequence alignments.wNumbers in parentheses represents value obtained for first 44 sequences.


439Bacterial diversity in wastewater following salination

level), compared with 28 and 33 OTUs obtained after

salination for the 44 randomly selected and 59 sequences of

the full library, respectively. This result was repeated for the

Chao1 richness estimator and Shannon diversity index

(Table 1). A similar image was obtained for the 90%

similarity level (roughly representing phylum-level diver-

sity). A total of 17 OTUs were observed before salination,

compared with 22 and 24 OTUs following salination for 44

and 59 clones, respectively. A similar trend was obtained for

the Caho1 and Shannon indices (Table 1), indicating an

increase in diversity at the phylum–order level. Additional

diversity prediction was performed, by fitting a nonlinear

regression equation to rarefaction curves generated by the

DOTUR program and extrapolating the regression line until a

plateau was achieved. The extrapolated curves for 97%

similarity reached a plateau at 60 and 78 OTUs before and

after salination, respectively. For 90% similarity, regression

lines leveled at 27 and 49 OTUs before and after salination,

respectively, (Fig. 1), again indicating an increase in diversity

following the salination process.

Comparison of the 16S rRNA gene sequences with the

NCBI database revealed a shift in the composition of

the microbial community following salination (Fig. 2).

The dominant classes before salination were Clostridia

(38%), Deltaproteobacteria (23%) and Gammaproteobacteria

(14%), as well as Betaproteobacteria (9%) and Epsilonproteo-

bacteria (5%). Following salination, the community was

dominated by Alphaproteobacteria (42%), Betaproteobacteria

(22%) and Clostridia (20%). No representatives of Alpha-

proteobacteria were found before the salination process and

no representatives of Delta- or Epsilonproteobacteria were

found after it. Comparison of two libraries obtained after

salination, one of 2004 (displayed here) and another of 2005

[obtained with 8F and 907R primer set (Ben-Dov et al.,

2006)], revealed the presence of Alphaproteobacteria (which

has not been found in any library before salination) with

similar concentrations of 42% and 38%, respectively.

The Alphaproteobacteria that have evolved after salination

are related to Alphaproteobacteria sequences retrieved

from different saline environments such as seawater and

deep-sea sediments, and wastes or diesel fuel in saline-

contaminated sites (see Fig. 3b). Of special interest were

sequences of Deltaproteobacteria that were all related to SRB

species, mostly Desulfovibrio or Desulfuromons. Their ab-

sence from clone libraries constructed following salination

suggested a possible decrease in SRB numbers in the ponds.

The only ribotype related to a known SRB following salina-

tion was 204-61-3, with a 91% similarity to Fusibacter

paucivorans (AF050099), a strictly anaerobic, halotolerant,

thiosulfate-reducing strain of the order Clostridiales

(Fig. 3b), isolated from a saline oil-producing well (Ravot

et al., 1999).

The specific diversity of SRB in the ponds was analyzed by

comparison of partial sequences from the functional apsA

gene from total DNA samples obtained before and after

salination, which permitted a more detailed look at the

changes in biodiversity. A total of 90 clones were analyzed, of

which only 21 sequences originated before the salination

process. From the limited data obtained, it appears that SRB

Fig. 1. Evaluation of bacterial diversities in the industrial wastewater

evaporation pond samples before and after salination. Rarefaction

curves were calculated with 3% (&, �; before and after salination,

respectively) and 10% (}, n; before and after salination, respectively)

sequence dissimilarity cutoff values. Prediction of cumulative lines was

performed by fitting a modified hyperbolic equation y = x/(ax1b), where

y is the cumulative number of OTUs, x is the number of clones analyzed

and a, b are constants. Number of OTUs at X ! 1 was calculated as

Y1= 1/a.

Others11%

Epsilonproteobacteria5%

Gammaproteobacteria14%

Betaproteobacteria9%

Clostridia38%

Deltaproteobacteria23%

Others13%

Gammaproteobacteria3%

Betaproteobacteria22%

Clostridia20%

Alphaproteobacteria42%

(b)

(a)

Fig. 2. Distribution of bacterial 16S rRNA gene sequences at the class

level, retrieved from industrial wastewater evaporation ponds before

(a) and after (b) salination.



community composition was not affected by this process.

All apsA gene fragments obtained were divided into three

major clades (Z88% within clade similarity), related to a

small number of Desulfovibrio species. No major clades

appeared or disappeared following the increase in

salt concentration. Several minor clades (one to three

sequences) representing microdiversity (closely related

sequences) within the three major ones were detected after

salination that had not been detected before salination, but

this is likely due to the higher sampling effort in the former

library.

The number of OTUs for the 16S rRNA gene found in the

hypersaline wastewater evaporation ponds (Table 1) is

comparable to other saline environments, such as an Indian

soda lake (Lonar Crater Lake; pH 10–10.5 and salinity of

about 8%) where 44 phylotypes (Z97% similarity) were

observed (Wani et al., 2006). A much higher diversity of 752

species (Z97% similarity) was found in the Guerrero Negro

(a)

Uncultured soil bacterium (AY242740)204-33-3

Wall-less Spirochaeta sp. (M87055)204-33-9Great Salt Lake 27% salinity sediment Halanaerobiaceae sp. clone (DQ386221)

Halanaerobiaceae halophilic sulfate-reducing bacterium (DQ386220)204-33-13

208-35-2 (7)Desulfuromonas thiophila sulfur-reducing from anoxic sediment (Y11560)

Desulfocella halophila fatty-acid-oxidizing, sulfate-reducing (AF022936)208-35-9

Desulfobotulus sp., sulfate-reducing from a salt marsh sediment (U85470)Desulfovibrio desulfuricans (CP000112)209-38-22209-38-16

208-35-21208-35-4

Pusillimonas noertemannii salicylates degraded (AY695828)Alcaligenes monasteriensis utilizing disulfide 3,3'-dithiodipropionic acid (AY880023)

204-33-5Uncultured compost bacterium (DQ346502)

209-38-4204-33-19

204-33-1 (6)Pseudomonas stutzeri anaerobic oxidation of 2-chloroethanol (AF411219)

Uncultured Arcobacter sp. from pink microbial mat (AY569293)208-35-6 (2)

208-35-16Uncultured Bacteroidetes/Chlorobi group bacterium (DQ211487)

100

100

100

99

58

65

80

100

100

100

100

100

99

81100

100

100

95

99

98

100

99

100

99

80100

79

68

81

78

52

84

64

63

76

55

63

0.05

Firmicutes

Deltaproteobacteria

Betaproteobacteria

Gammaproteobacteria

Epsilonproteobacteria

Actinobacteria

Spirochaetes

Unidentified

Firmicutes

Bacteroidetes

0.05

208-35-8 (2)High salinity effluent treatment plant clone (DQ439562)

208-35-14Tissierella praeacuta (X80833)

Clostridium acidiurici (M59084)209-38-2

208-35-10 (6)Landfill leachate bioreactor Alkalibacterium sp. (AY554414)

208-35-5Garciaella petrolearia nitrate-thiosulfate-reducing bacterium (AY176772)

Oil reservoir clone (AY570635)

Fusibacter paucivorans thiosulfate-reducing (AF050099)208-35-1

204-33-2 (3)

Uncultured bacterium from deep-sea cold seep area (AB069798) 209-38-1

Actinobacterium sp. from subsurface water of the Kalahari Shield (DQ234644)204-33-14

Fig. 3. Phylogenetic trees based on 16S rRNA gene sequences, which were retrieved from industrial wastewater before (a) and after (b) salination.

Ribotypes marked (�) were represented by closely related (Z97% similarity) sequences in both libraries. The trees were constructed by the neighbor-

joining method (Saito & Nei, 1987) with the MEGA package (Kumar et al., 2004) using partial sequences of 16S rRNA gene. The bar represents five

substitutions per 100 nucleotide positions. Bootstrap probabilities (Felsenstein, 1985) are indicated at branch nodes. The numbers in parentheses

indicate the total number of similar clones on the basis of Z97% identity for each representative sequence.



hypersaline (8%) microbial mat. This result is possibly due

to the far greater diversity of chemical niches in the micro-

bial mat, which allows more opportunities for specialization

within the microbiota (Ley et al., 2006).

Several studies have been directed towards changes in

microbial communities along a salinity gradient. Foti et al.

(2008) studied the microbial diversity of several soda lakes

in Russia, with Na1 concentrations varying between 60 and

Uncultured bacterium from ultradeep gold mine borehole (AF546917)208-42-20 (3)

Lodide-oxidizing bacterium (AB159201)Roseobacter sp. dimethylsulfoniopropionate metabolism (AY332660)208-42-21 (2)

AlphaProteobacterium growth on diesel fuel in saline environ. (DQ153930)209-44-16

AlphaProteobacterium growth on diesel fuel in saline environ. (DQ153889)208-42-8 (6)

Uncultured bacterium from Sewage Sludge in Slurry-Composting Process (AB241602) 208-49-2 (8)Hyphomicrobium sulfonivorans strain, methylotrophic from Antarctica (AY305006)

208-42-2Pedomicrobium fusiforme (Y14313)208-42-6208-42-9Uncultured bacterium from activated sludge (DQ250535)208-42-7

Thalassospira sp., pyrene-degrading from the Pacific Open Sea (DQ659435)208-42-15

Pseudomonas stutzeri anaerobic oxidation of 2-chloroethanol (AF411219)208-49-5

Carbazole-degrading Marinobacterium sp. (AB196257) 209-44-15

209-44-2Alcaligenes monasteriensis utilizing disulfide 3,3'-dithiodipropionic acid(AY880023)

Uncultured compost bacterium (DQ346502)209-44-13 (2)

Pusillimonas noertemannii salicylates degraded (AY695828)208-42-5 (6)

208-42-4 (4)Uncultured bacterium Sludge in Slurry-Composting Process (AB241607) Uncultured bacterium from landfill leachate (AJ853534)

Cytophaga sp. from deep-sea halocline (AM157648)204-61-6

208-49-17 (2)

208-49-21Bacteroides sp. clone from landfill leachatebioreactor (AY554420)

208-49-13Reactor system treating monochlorobenzene contaminated groundwater clone (AF407408) Clostridium orbiscindens (AY730665)

208-42-14Chlorine substituents from chlorobenzenes clone (AJ488078) 204-56-11

Anaerobic sludge digester clone (CR933235)208-42-22

Uncultured fatty acid-oxidizing Halanaerobacter (DQ173895) 204-56-6

Harbor sediment Aminomonas sp. clone (DQ394907) 208-49-22

Anaerobic clone from swine lagoon (AY953219) 208-42-13

Fusibacter paucivorans thiosulfate-reducing (AF050099)Oil reservoir clone (AY570635)

204-61-3 (2)Clostridium halophilum (X77837)

Tissierella praeacuta (X80833)204-61-10

Soehngenia saccharolytica (AY353956)High salinity effluent treatment plant clone (DQ439562)

208-42-16 (2)Polychlorinated-dioxin-dechlorinating microbial community clone (AB186884)

Desulfonosporus thiosulfogenes (Y18214)209-44-12

Wall-less Spirochaeta sp. (M87055) 204-56-2

Spirochaeta sp. trichloroethene-dechlorinating (AF357916)Oil field Spirochaeta sp.clone (AY800103)

208-49-11

100

100

100

100

100

90

6371

50

86100

100

100

63

52100

99

97

75

82

81

65

57100

100

100

9856

100

100

85

99

59

100

100

99100

100

100

100

99

94

5865

88

67

62

65

88

57100

0.05

Firmicutes

Spirochaetes

Bacteroidetes

Alphaproteobacteria

Gammaproteobacteria

Betaproteobacteria

208-42-7

208-42-15

209-44-2

208-49-21

204-56-11

59

Microbial fuel cell enriched with acetate clone (AY491548)

(b)

Fig. 3. Continued.



200 g L�1. Wu et al. (2006) studied the microbial diversity of

mountain lakes on the Tibetan plateau, with salinities

ranging from 0.02% to 22.3%. Benlloch et al. (2002) and

Casamayor et al. (2002) both studied the same set of coastal

solar salterns with a salinity ranging from close to seawater

(c. 4%) to saturation (37%), using different approaches. All

these works found a similar trend, with the number of OTUs

remaining more or less constant along the gradient, but

the number of genera decreasing with increased salinity. The

net result was an increase in microdiversity, i.e., several

closely related species occupying the same niche. A some-

what different result was obtained for different tannery

effluent-related saline sludge samples with 7, 46, 52 and

72 g NaCl L�1, where the number of bacterial OTUs (Z97%

sequence similarity) decreased from 231 to 144, 108 and 50,

respectively (Lefebvre et al., 2006).

It is generally assumed that the more extreme (physically

or chemically limited) an environment is, the less biological

diversity will be supported by it, providing a working

hypothesis regarding which environments will have a high

or a low diversity. However, despite such unfavorable

conditions for life, recent studies have revealed unexpectedly

high microbial diversities in what has traditionally been

considered an extreme environment (Bowman et al.,

2000; Nubel et al., 2001; Chanal et al., 2006). In a study

by Øvreas et al. (2003), the changes in the prokaryotic

communities at different salinity levels (22%, 32%, and

37% salt) of a set solar saltern ponds were investigated.

Their results suggested a possible increase in total

genetic diversity from 22% to 32% salinity, whereas at 37%

salinity, the diversity was reduced to nearly half that at

22% salinity.

Field studies have provided evidence that abiotic factors

are of considerable importance in determining species

growth and richness within a community (Gough et al.,

1994; Grace & Pugesek, 1997; Smith, 2007). For example, a

hump-shaped model of algal species diversity was observed

along exposure to gradients of stress (such as salinity, light

and sea depth), disturbance factors (wave effects, ice scour-

ing and grazing) and in relation to biomass value, where the

most diverse communities were found at sites with inter-

mediate stress and/or disturbance levels and intermediate

primary production (Kautsky & Kautsky, 1989). A hump-

shaped relationship is formed in terms of portions of

environmental gradient (Smith, 2007). The species rich-

ness–biomass relationship clearly depends on the magnitude

of the changes of the environmental gradients along which

the community parameters are measured (Guo & Berry,

1998). The greater the range in environmental conditions,

the more complete will be the development of the ‘hump-

shaped’ relationship (Gough et al., 1994), a result frequently

observed in the measured microbial diversity of experimental

and natural aquatic systems (Smith, 2007).

The rationale behind the proposed models is that compe-

tition intensity increases as the rate of biomass production

increases. In microhabitats with a modest supply of re-

sources, groups of species can coexist, and each group will be

at a relatively lower dominance level, but species richness

will be higher. After biomass production reaches a critical

range, competition becomes sufficient to eliminate less

competitive species from the community (Guo & Berry,

1998). Our results displayed an increase in bacterial diversity

when going from 3–7% to 12–16% salt concentration, at

both 90% and 97% sequence similarity (Table 1; Fig. 1). This

result implies a ‘positive’ response to an increase in salinity.

This is very likely the rising side of a hump-shaped curve. A

very wide range (often two orders of magnitude or more) of

factors is often required in order to reveal clear and

statistically convincing evidence of the entirety of a hump-

shaped relationship (Smith, 2007). It may be safely assumed

that a further increase in salinity would eventually result in a

decrease in microbial diversity.

Diversity of bacteria in industrial wastewaterevaporation ponds

Representatives of 16S rRNA gene sequences that were

retrieved from industrial wastewater were aligned with

closely related sequences to construct phylogenetic trees for

sequences obtained before (Fig. 3a) and after salination (Fig.

3b). Many of the different sequences, both before and after

salination, were related to sequences retrieved from different

saline environments such as lakes, deep-sea sediments, waste

treatment or other chemically contaminated sites (Fig. 3).

Proteobacteria was the most dominant phylum both

before and after salination (52% and 68%, respectively).

The sequences 208-42-20, 208-42-21 (Fig. 3b) were related

(95–98% similarity) to iodide-oxidizing bacteria (AB159200,

AB159201, AB159209, AB114422) of Roseovarius and Roseo-

bacter sp. that oxidize iodide (I�) to free molecular (I2) or

volatile organic iodine, which were identified as diiodo-

methane (CH2I2), chloroiodomethane (CH2ClI) and methyl

iodide (CH3I) (Amachi et al., 2001; Fuse et al., 2003). The

wastewater produced by several plants in the Ramat-Hovav

industrial area contains high concentrations of halogenated

organic compounds, mainly chlorinated and brominated

(Belkin et al., 1993), that are probably used as substrates by

these bacteria.

Ribotype 209-44-15 (Fig. 3b) was closely related (498%

similarity) to a carbazole-degrading marinobacterium

(AB196257) (carbazole is a group of organic heterocyclic

aromatic compounds containing a nitrogen atom in a

dibenzopyrrole system) (Inoue et al., 2005). Carbazole and

its derivatives are widely used as an intermediate in the

synthesis of pharmaceuticals, agrochemicals, dyes, pigments

and other organic compounds. Some sequences (204-33-1,



208-49-5; Fig. 3) were closely related (99% and 98%,

respectively) to Pseudomonas stutzeri (AF411219) that uses

2-chloroethanol as its sole energy and carbon source (Dijk

et al., 2003). 2-Chloroethanol and other closely related

derivatives are used in industry, mainly for the synthesis of

insecticides and as a solvent and are contributed to the

effluent by several chemical plants at the Ramat-Hovav

industrial park.

Firmicutes was the second most dominant phylum both

before (39%) and after (19%) salination. This phylum was

represented by sequences related to SRBs, thiosulfate redu-

cing, and other halophylic or halotolerant species, with a

wide range of metabolic activities (Fig. 3).

SRB may use sulfate–sulfite, thiosulfate or elemental

sulfur, either as alternative electron acceptors or for dispro-

portionation of sulfur compounds in their anaerobic energy

metabolism (Bak & Pfennig, 1987; Jørgensen & Bak, 1991).

The diversity of SRBs in this hypersaline industrial waste-

water was investigated by a set of primers targeting the

functional apsA gene (Ben-Dov et al., 2007) in addition to

16S rRNA gene clone libraries. The ApsAB amino-acid

sequences have been proposed as useful phylogenetic mar-

kers. However, comparative analysis of ApsA sequences

produced a tree topology partially inconsistent with the

corresponding 16S rRNA gene phylogeny (Klein et al., 2001;

Friedrich, 2002). This may explain some of the differences

between 16S rRNA gene and apsA clone libraries’ results.

Sequences of the 16S rRNA gene-related SRB, before

salination, mostly belonged to Deltaproteobacteria or Firmi-

cutes (Fig. 3a). SRB-related 16S rRNA gene sequences

obtained after salination all belonged to the phylum Firmi-

cutes (Fig. 3b). Only one SRB-related ribotype (204-33-2)

appearing before salination was represented by a sequence

(204-61-3) obtained following salination.

In contrast, sequences amplified by primers for the

functional apsA gene, both before and after salination, were

all homologs (85–94% similarity) to different Deltaproteo-

bacteria species and included Desulfovibrio longus, Desulfo-

vibrio aespoeensis, Desulfovibrio simplex, Desulfomicrobium

escambiense, Desulfocella halophila and Desulfovibrio desul-

furicans species (Ben-Dov et al., 2007). Desulfovibrio desul-

furicans was the only species detected by both the 16S rRNA

gene and the apsA-targeting primers. This species is known

for its ability to remove sulfate, organic substances in

general and aromatic compounds (benzothiophene and

dibenzothiophene) in particular from industrial wastewater

(Setti et al., 1993; Kosinska & Miskiewicz, 1999). Sequences

related to SRB were less abundant in the 16S rRNA clone

library following an increase in salt concentration and

belonged to different bacterial classes compared with se-

quences obtained before salination. However, the sequences

amplified using apsA did not display a clear change in the

SRB community composition. This result, combined with a

significant reduction in H2S concentrations (from p.p.m. to

p.p.b. levels) in the ponds area during the few days following

salination, indicates a possible inhibition of SRB activity in

the ponds, due to increased salt concentrations.

Comparison of bacterial diversity before and after

salination of the industrial wastewater evaporation ponds

indicated a shift in both the richness and the composition of

the microbial community (Table 1; Figs 1–3). This is hardly

surprising, as salinity is a central parameter for bacterial

selection and community composition and may well be the

most important factor dividing different microbial commu-

nities (Lozupone & Knight, 2006). Evaporation ponds are

still widely used as an end treatment solution for industrial

wastewater. The biological processes in these ponds, espe-

cially those resulting in the emission of poisonous or

odorous substances, are of some interest, as they may cause

a nuisance to neighboring industry or civil areas. The

unique experiment described here, aiming to salt out the

microorganisms responsible for these processes, was at least

partially successful, as H2S emissions were reduced follow-

ing artificial salination. However, halotolerant and ha-

lophylic communities are likely to take over this

environment, possibly overturning these results in the long

run. Therefore, the challenge remains to explore the micro-

bial communities that take part in pollutant degradation

processes in this environment, for use in the control and

development of optimal in situ bioremediation strategies in

the future.

Acknowledgements

This work was supported by research funds from the Ramat-

Hovav Council and the BMBF-MOST Cooperation in Water

Technologies Grant WT-501. We thank Esti Kramarsky-

Winter for useful comments on the manuscript.

Authors’ contribution

E.B.-D. and O.H.S. contributed equally to this work.

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Changes in microbial diversity in industrial wastewater evaporation ponds following artificial salination

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