MARZIA MILETTO INSIDE 191107 LA - Universiteit Utrecht · 2020. 3. 4. · 1Department of Microbial Wetland Ecology, Netherlands Institute ... dsrB-based denaturing gradient gel electrophoresis

CChhaapptteerr IIIIII___________________________________________________________

Biogeography of Sulfate-Reducing Prokaryotes in River Floodplains

Marzia Miletto1Alexander Loy2

A. Martijn Antheunisse3Roos Loeb4

Paul L.E. Bodelier1Hendrikus J. Laanbroek1

1Department of Microbial Wetland Ecology, Netherlands Institute of Ecology (NIOO-KNAW), the Netherlands

2 Department of Microbial Ecology, Faculty of Life Sciences, University of Vienna, Austria

3 Department of Landscape Ecology, Institute of Environmental Biology, Utrecht University, the Netherlands

4 Department of Aquatic Ecology & Environmental Biology, Institute for Water and Wetland Research, Radboud University Nijmegen,

the Netherlands

Submitted

Marzia Miletto (2007). Sulfate-reducing prokaryotes in river floodplains. PhD thesis, Utrecht University, Science Faculty, pp. 148.

Sulfate-reducing prokaryotes in river floodplains

41

ABSTRACT

In this study we conducted a large scale field survey to describe the biogeography of sulfate-reducing prokaryotes (SRPs) communities in river floodplain soils. Twenty-nine soil samples were collected in tidal and non-tidal areas along the rivers Meuse, Rhine and Overijsselse Vecht (the Netherlands). The SRPs communities were examined using a polyphasic approach consisting of 16S rRNA gene-based oligonucleotide microarray, dsrB-based denaturing gradient gel electrophoresis (DGGE) and polar lipid-derived fatty acids (PLFAs) analyses. Fingerprints obtained with these three methods were used as a proxy to describe the SRPs diversity in the floodplain samples. The occurrence of known SRPs as suggested by microarray hybridizations was cross-confirmed by DGGE as well as PLFA analyses. Each set of profiles was subjected to a combined multivariate/correlation analysis, in order to compare SRPs community profiles and to highlight the environmental soil and pore water variables influencing the distribution of the SRPs communities along environmental gradients, as described by microarray/DGGE/PLFAs analyses. Floodplain soils harbored diversified SRPs communities displaying biogeographic patterns. Nearly all profiles from the tidal sites consistently separated from the non-tidal sites, independently from the screening method and the multivariate statistics used. The distribution of the microarray/DGGE/PLFAs-based fingerprints in the principal component plots could be correlated to 8 soil variables, i.e. soil organic matter, total nitrogen, phosphorous and potassium, and extractable ammonium, nitrate, phosphate and sulfate, as well as 7 pore water variables, i.e. phosphate, sulfate, sulfide, chloride, sodium, potassium and magnesium ions. Indication of a salinity- and plant nutrient-dependent distribution of SRPs related to Desulfosarcina, Desulfomonile, and Desulfobacter was suggested by microarray, DGGE and PLFAs analyses.

INTRODUCTION

Natural floodplains are among the most productive and biodiverse ecosystems worldwide (Spink et al., 1998). Nevertheless, habitat alterations due to regulation of natural river hydrology with dams and dikes, agricultural land use, navigation, species invasion and pollution, place floodplains among the most threatened environments (Tockner and Stanford, 2002). The European policy for river management is currently aiming at the ecological rehabilitation of typical biodiverse floodplain


Chapter III – Biogeography of sulfate-reducing prokaryotes

42

environments by removing dikes and creating zones for water retention in periods of peak river discharge (van Stokkom et al., 2005). However, the strong pollution of groundwater and soil in floodplains as a result of past agricultural activities forms an important biogeochemical constraint for the development of characteristic species-rich wetlands, which often does not meet the restoration targets (Lamers et al., 2006). Sulfate is considered to be one of the most potent pollutants in riverine areas (Lamers et al., 1998) giving rise to the production of the chemically reactive and phytotoxic sulfide by sulfate-reducing prokaryotes (SRPs) upon flooding of the river forelands. Members of this diverse group of anaerobic microorganisms gain energy for metabolic processes by using sulfate as terminal electron acceptor for the oxidation of various carbon sources. The dissimilatory reduction of sulfate has a central role in the global sulfur cycle and represents one of the most important organic matter mineralization processes in various environments (Santegoeds et al., 1998; Holmer and Storkholm, 2001; Ito et al., 2002a; Dhillon et al., 2003; Jonkers et al., 2005). Despite the importance of sulfate reduction in the biogeochemistry of waterlogged soils (Wind et al., 1999; Loy et al., 2004) and subsurface environments (King et al., 2002), the information on the diversity and distribution of SRPs in terrestrial environments is scarce. In this work, molecular fingerprints based on 16S rRNA gene-targeted microarray, dsrB-targeted denaturing gradient gel electrophoresis (DGGE), and polar lipid-derived fatty acids (PLFAs) analyses were used to describe the diversity of SRPs in floodplain soils. The environmental soil and pore water parameters that might determine the biogeography of the SRPs communities in river floodplains were unmasked using a combination of multivariate and correlation analyses.

MATERIALS and METHODS

Sampling sites and environmental data The samples analyzed in this study (29 in total) were collected in the period of June-September 2002 at 11 floodplains along three of the major Dutch river systems, i.e. Meuse, Rhine and Overijsselse Vecht (Figure 1). A selection of characteristics for the sampling sites is listed in Table 1. Sites were selected based on vegetation and hydrology. Beside vegetation, which varied considerably among locations, sites were also quite heterogeneous with respect to their hydrodynamics; some sites were under tidal influence, while the non-tidal areas differed in the frequency and duration of flooding events per year.



43

North Sea

Rhine

Meuse

Overijsselse Vecht

Roz Hei

Spi

Stw BlK Ewk

Meg

OuW

Kon

HdD

Str

North Sea

Rhine

Meuse

Overijsselse Vecht

Roz Hei

Spi

Stw BlK Ewk

Meg

OuW

Kon

HdD

Str

FIGURE 1 Map of the Netherlands showing the topographic location of the 11 sampling sites. Meuse sites (from left to right): Heinenoord, Hei; Spijkenisse, Spi; Megen, Meg; Koningsteen, Kon). Rhine sites: Rozenburg, Roz; Steenwaard, Stw; Blauwe Kamer, BlK; Ewijk, Ewk; Oude Waal, OuW. Overijsselse Vecht sites: Streukel, Str; Huis den Doorn, HdD. , tidal locations; , non-tidal locations.

The following soil and pore water variables were measured in each station. Soil parameters included moisture (Ws) and organic matter (OMs) content; concentration of total nitrogen (TNs), total phosphorus (TPs), and total potassium (TKs) determined by using a salicylic acid thiosulphate modification of the Kjeldahl digestion; 0.2 M KCl-extracted nitrate (NO3 s)


Rive

rSi

teSa

mpl

e co

deH

ydro

dy-

nam

icsa

Char

acte

ristic

plan

t

sp

ecies

(H' ve

g)W

s

(%)

OM

s

(%)

TNs

(mg/

kg)

TPs

(mg/

kg)

TKs

(mg/

kg)

SO42

– s(m

g/kg

) Cl

– pw

(µm

ol/l

)pH

Meu

se

Hein

enoo

rd (5

1°49

' N, 4

°30'

E)

HeiI

T (1

.1/-

20)

Phra

gmite

s aus

tralis

(0.3

3)64

.15.

51.

80.

62.

00.

224

16.8

6.9

Hei

IIT

(1.1

/-48

)Sc

irpus

lacu

stris

(0.1

6)76

.17.

03.

10.

94.

40.

322

50.0

6.8

Kon

ings

teen

(51°

9' N

, 5°5

0' E

)K

onI

NT

(5/4

)Le

ersia

oryzo

ides

(0.5

6)59

.813

.13.

30.

66.

80.

318

64.7

6.9

Meg

en (5

1°49

' N, 5

°35'

E)

Meg

IN

T (2

/2)

Carex

elat

a (0

.78)

35.9

6.3

2.5

0.4

7.2

0.1

292.

36.

5M

egII

NT

(2/2

)G

lyceru

a ma

xima

(0.4

2)37

.05.

61.

50.

34.

60.

479

2.1

6.5

Spijk

eniss

e (5

1°50

' N, 4

°22'

E)

SpiI

T (1

.2/-

42)

Scirp

us m

ariti

mus

(0.1

6)29

.61.

40.

30.

31.

60.

528

03.4

6.7

SpiII

T (1

.2/-

68)

Phra

gmite

s aus

tralis

(0.0

7)31

.41.

10.

30.

41.

70.

427

91.7

6.9

SpiII

IT

(1.2

/-9)

Epil

obium

hirs

utum

(0.6

0)33

.32.

20.

60.

31.

90.

325

74.1

7.1

SpiIV

T (1

.2/-

74)

Scirp

us la

custr

is (0

.05)

43.9

4.1

1.0

1.3

3.0

0.2

2692

.36.

9Rh

ine

Blau

we

Kam

er (5

1°57

' N, 5

°36'

E)

BlK

IN

T (4

/3)

Carex

acu

tiform

is (0

.56)

44.5

13.7

8.4

2.0

7.6

0.4

605.

27.

1Bl

KII

NT

(4/3

)M

enth

a aq

uatic

a (0

.76)

24.4

4.0

1.1

0.4

5.7

0.1

605.

27.

1E

wijk

(51°

53' N

, 5°4

4' E

)E

wkI

NT

(3/3

)Ci

rsium

arve

nse

(0.6

8)10

.53.

41.

00.

44.

80.

914

00.5

7.1

Ew

kII

NT

(4/4

)Pe

rsica

ria h

ydrop

iper

(0.8

4)30

.28.

71.

91.

56.

50.

837

9.7

7.1

Oud

e W

aal (

51°5

1' N

, 5°5

3' E

)O

uWI

NT

(4/2

)G

lyceri

a ma

xima

(0.5

9)46

.912

.95.

31.

012

.11.

175

1.7

6.7

OuW

IIN

T (4

/3)

Iris p

seuda

corus

(0.8

2)51

.218

.85.

20.

99.

50.

531

4.4

6.9

OuW

III

NT

(4/3

)M

enth

a aq

uatic

a (0

.83)

35.2

10.7

4.2

0.8

8.8

0.6

3168

.96.

7Ro

zenb

urg

(51°

54' N

, 4°1

4' E

)Ro

zIT

(1.4

/-79

)Sc

irpus

mar

itimu

s (0

.18)

35.2

1.5

0.4

0.4

1.7

0.4

5430

7.4

7.1

RozI

IT

(1.4

/-25

)Ph

ragm

ites a

ustra

lis (0

.01)

32.5

1.2

0.3

0.5

1.4

0.1

4382

3.6

7.0

RozI

IIT

(1.4

/54)

Elym

us a

theri

cus

(0.8

9)18

.23.

01.

30.

41.

30.

227

088.

37.

0Ro

zIV

T (1

.4/4

6)E

pilob

ium h

irsut

um (0

.87)

44.4

8.0

3.5

0.6

1.9

0.4

2308

.56.

8St

eenw

aard

(51°

58' N

, 5°1

2' E

)St

wI

NT

(5/5

)Li

mosel

la aq

uatic

a (0

.59)

23.7

3.7

1.9

0.7

7.8

0.2

1915

.27.

3St

wII

NT

(4/4

)Pl

antag

o majo

r (0

.77)

32.1

7.7

4.8

1.2

6.7

0.3

2888

.37.

2St

wII

IN

T (5

/4)

Bide

ns tr

iparti

ta (0

.73)

31.3

7.3

2.3

0.8

8.1

0.2

3091

.37.

2O

verij

ssels

e H

uis d

en D

oorn

(52°

33' N

, 6°7

' E)H

dDI

NT

(2/2

)Fr

itilla

ria m

eleag

ris (0

.95)

39.8

15.3

4.6

1.2

4.2

0.6

268.

05.

9V

echt

HdD

IIN

T (3

/2)

Calth

a pa

lustri

s (0

.78)

43.7

16.9

5.7

0.9

5.5

1.1

1025

.85.

9H

dDII

IN

T (3

/3)

Carex

acu

ta (0

.68)

51.4

25.5

7.9

2.9

4.7

0.4

949.

46.

0H

dDIV

NT

(4/3

)G

lyceri

a ma

xima

(0.6

4)49

.820

.58.

11.

31.

61.

111

15.1

5.8

Stre

ukel

(52°

35' N

, 6°6

' E)

StrI

NT

(5/4

)Ca

rex a

cuta

(0.4

6)78

.558

.88.

41.

13.

32.

814

29.8

6.6

StrI

IN

T (5

/4)

Phra

gmite

s aus

tralis

(0.3

4)86

.666

.720

.31.

86.

91.

713

15.9

6.5


44



45

and ammonium (NH4+s); available phosphate (PO43 s) obtained with an ammonium lactate-acetic acid-extraction; sulfate (SO42 s) in demi-water extracts. Pore water parameters included concentrations of nitrate (NO3 pw), ammonium (NH4+pw), phosphate (PO43 pw), sulfate (SO42 pw), sulfide (S2 pw); the total concentration of many mono-, di- and trivalent ions (Cl pw, Na+pw,K+pw, Mg2+pw, Ca2+pw, Mn2+pw, Zn2+pw, Fe2+/3+pw, Al3+pw); pH (pHpw) and alkalinity (Alkpw). Extraction and analytical methods are described in detail elsewhere (Antheunisse et al., 2006). Sterilized stainless steel cylinders ( =3 cm, h=20 cm) were used to collect soil samples. Cores were transported anoxically to the laboratory at 4°C and, immediately upon arrival, roots were removed and the remaining top 15 cm soil was homogenized by passing through a sterilized 1-mm mesh sieve. A sub-sample of the homogenized soil was stored at 20°C until further analysis.

DNA extraction DNA was extracted from 0.3 g wet weight of soil using the UltraClean Soil DNA kit (MoBio, Solana Beach, CA, USA) according to the manufacturer’s instructions. Quantification of the electrophoresed and ethidium bromide-stained DNA extracts was performed by comparison to two dilutions of the SmartLadder quantification standard (Eurogentec, Seraing, Belgium); digital images analysis was carried out using the software package Phoretics 1D Advanced (Nonlinear Dynamics, Newcastle upon Tyne, UK).

PCR amplification of 16S rRNA genes and microarray analysesPrior to microarray hybridization, almost complete 16S rRNA gene fragments were amplified from environmental DNA extracts with the

TABLE 1 Selected characteristics of the sampling sites. H veg, vegetation Shannon’s diversity index; Ws, moisture content; OM, organic matter; TNs, total soil nitrogen; TPs, total soil phosphorus; TKs, total soil potassium. a T, tidal (tidal amplitude, cm/mean high water level to sampling point height, cm). NT, non-tidal (flood duration per year/flood frequency per year). Flood duration classes: 1, 0-5 d y–1; 2, 6-20 d y–1; 3, 21-40 d y–1; 4, 41-100 d y–1; 5, 100+ d y–1 inundation. Flood frequency classes: 1, 0-1 f y–1; 2, 2-3 f y–1; 3, 4-6 f y–1; 4, 7-15 f y–1; 5, 16+ f y–1 (Antheunisse et al., 2006).



46

primer sets 616V-630R (Juretschko et al., 1998) and 616V-1492R (Kane et al., 1993) according to established protocols (Loy et al., 2004). Preparation of the 16S rRNA gene-based oligonucleotide microarray for SRPs (SRP-PhyloChip), labeling of PCR products, microarray hybridization and data analysis was performed as described previously (Loy et al., 2002). Probes with a signal-to-noise ratio equal to or greater than 2.0 were considered as positive. Probes that did not show positive signals in any sample were excluded from statistical analyses. Desulfovibrio halophilus/Desulfovibrio oxyclinaespecific-primers (Table 2) were used to confirm some of the microarray results. The cycling conditions were as follows: template DNA was added after pre-incubation of the PCR mixture at 94 °C for 5 min, followed by 30 cycles of denaturation at 94°C for 40 s, annealing at 60°C for 40 s, and elongation at 72°C for 60 s. A final elongation step was performed at 72°C for 10 min. A positive control, i.e. a 16S rRNA gene-containing plasmid from Desulfovibrio halophilus, and a negative control without DNA were included in each PCR assay.

TABLE 2 Additional primers used in this study.

Short name (full namea)Annealing temp (°C) Sequence 5'-3' Specificity Reference

DVHO130F (S-*-Dvho-0130-a-S-18)

60 ATC TAC CCG ACA GAT CGG Desulfovibrio halophilus, Desulfovibrio oxyclinae

This study

DVHO1424R (S-*-Dvho-1424-a-A-18)

60 TGC CGA CGT CGG GTA AGA See above This study

a Name of 16S rRNA gene-targeted oligonucleotide primer based on the nomenclature of Alm et al. (1996).

PCR amplification of dsrAB-dsrB and DGGE analysis dsrAB fragments of approximately 1.9-kb were amplified using the primers DSRlFmix and DSR4Rmix (Loy et al., 2004), supplemented with primers DSR1Fc, DSR1Fd and DSR4Re (Zverlov et al., 2005), respectively. The reaction was performed in a MBS 0.5S thermocycler (ThermoHybaid, Ashford, UK) using the Expand High Fidelity PCR system (Roche Applied Science, Basel, Switzerland). The reaction mixture (total volume 50 µl) contained 100 ng of soil DNA, 1 Mg-free Expand High Fidelity buffer, 0.5 µM of each primer, 200 µM of each deoxynucleotide, 1.5 mM MgCl2, 250 ng µl–1 of bovine serum albumin (BSA, New England Biolabs, Beverly, MA, USA), and 2.6 U of Expand High Fidelity Enzyme mix. Thermal cycling



47

was carried out as described previously (Loy et al., 2004). PCR fragments of the expected size (1.9-kb) were purified by electrophoresis in a 1.5% [w/v] intermediate-melting-point agarose gel (MetaPhor agarose, Cambrex, East Rutherford, NJ, USA), subsequent excision with a sterile surgical blade and purification with the QIAquick Gel Extraction Kit according to the manufacturer’s instructions (QIAGEN GmbH, Hilden, Germany). Fragments of dsrB of approximately 350-bp in length were amplified from dsrAB PCR products and separated by DGGE as described in Chapter II. Gels were stained for 1 h in a 1 µg ml–1 ethidium bromide solution prior to digital recording with an AutoChemiTM UVP BioImaging System (UVP Inc. Upland, CA, USA). The software package Phoretics 1D Advanced was used to analyze gel images; band detection and matching were performed automatically to avoid biases associated with manual band processing. Bands showing intensity under a certain value (5% of the highest peak within a lane) were omitted from further analyses. Retardation factor (Rf) values were assigned to bands by using a reference mix of 10 dsrB fragments that was run together with the samples to account for gradient heterogeneities. Bands having similar Rf values were considered as corresponding and grouped into a match (the maximum acceptable displacement to call a match between bands was set to Rf±0.001). The different Rf classes were used for statistical analyses.

PLFAs analysis Polar lipid-derived fatty acids were extracted from 3 g of freeze-dried soil with a modified Bligh-Dyer extraction (Boschker et al., 1998; Boschker et al., 2001). Fractionation of total lipid extract into different polarity classes was performed on silicic acid by sequential elution with chloroform, acetone, and methanol. Fatty acids were extracted from the polar lipids of the methanol fraction, and derivatized in fatty acid methyl esters (FAMEs) using mild-alkaline methanolysis. C12:0 and C19:0 FAMEs were used as internal standards. FAMEs identification was performed by comparison of retention time data with known standards on a Thermo Finnagan TRACE GC-MS system. FAMEs concentration was determined using a Thermo Finnagan TRACE GC-FID system equipped with a polar capillary column (SGE, BPX-70; 50 m 0.32 mm 0.25 µm). Oven conditions were the following: 80°C for 1.5 min, increase to 120°C at 20°C min 1, increase to 240°C at 3°C min 1. Nomenclature of PLFAs was used as described previously (Guckert et al., 1985). Thirty-tree different PLFAs were measured. However, only those seven PLFAs that are commonly found in SRPs (Kohring et al., 1994) were used for statistical analyses; these include



48

10MeC16:0, aC17:0, iC17:1w7c, C17:0, cyC17:0, C17:1w6c, cyC19:0. PLFAs relative abundance was calculated as percentage of the total PLFAs abundance.

Statistical analysis of microarray, DGGE and PLFAs patternsThe software package STATISTICA v7.1 (StatSoft Inc., Tulsa, OK, USA) was used for all statistical analyses. The initial data matrixes consisted of microarray probes, DGGE bands and PLFAs as variables and the corresponding scores (presence/absence of probe signals or bands, and PLFAs relative abundance, respectively) as the values within each variable. For microarray probes and DGGE bands presence-absence data, binary similarities for every pair of samples (hybridization/DGGE profiles) were inferred using the Jaccard coefficient (CJ) calculated as

100c)b(acCJ

where ‘a’ is the number of positive probes or DGGE bands in the first sample, ‘b’ is the number of positive probes/bands in the second sample, and ‘c’ is the number of corresponding probes/bands positive in both samples. Euclidean distances were computed for PLFAs concentrations. Cluster analysis was performed using the unweighted pair group average algorithm (UPGMA). Principal component analysis (PCA) was used to investigate the structure of the three multivariate datasets and produce a reduced number of statistically independent variables (principal components) describing the datasets variance. A separate PCA based on correlations was performed for the three datasets. PCA was chosen as ordination method because the sample loadings on the principal components could be analyzed further with correlation analysis, with the final aim to investigate which environmental parameters relate to the observed similarities within microarray, DGGE, and PLFAs profiles. The Spearman rank R nonparametric statistics was used in correlation analyses between the principal components from the three datasets and all soil and pore water variables listed above. Results with p>0.05 were considered not significant.



49

RESULTS

Cluster analysis The distance tree constructed on Jaccard similarity values based on the SRP-PhyloChip results sorted the samples in two major clusters, generally corresponding to the tidal or non-tidal character of the location (Figure 2a). The first group included most of the tidal areas situated in the Rhine-Meuse delta (all Rozenburg sites, as well as SpiI, SpiII, and SpiIV) in the western part of the Netherlands. Exceptions were MegI and MegII that were included in this cluster although situated eastern along the river Meuse. The second cluster comprised most of the non-tidal sites, located on the Meuse (KonI), Rhine (StwII, BlKI-II, and OuWI-III), and Overijsselse Vecht (StrI-II and HdDI-IV), with the exception of the tidal sites HeiI-II and SpiIII, although the linkage distance between SpiIII and the non-tidal cluster was considerably high. Also for DGGE data, the distinction between tidal and non-tidal sites was evident, although the linkage distances were generally higher (Figure 2b). Exceptions were given by KonI and EwkI, displaying similarities with DGGE profiles from Heinenoord and Spijkenisse. Finally, classification of floodplain samples using PLFAs profiles as input data separated again the samples according to their tidal or non-tidal character. Also in this case there were a few exceptions, as MegII, KonI and EwkI were included in the tidal cluster and RozIII-IV were included among the non-tidal sites (Figure 2c).

PCA Component plots (PC1 PC2) for microarray, DGGE, and PLFAs data are given in Figure 3. PC1-2 of microarray and PLFAs data explained a cumulative 50% and 63% of the original variability, well summarizing the two datasets. In contrast, the percentage of variance explained was rather low (26%) for DGGE profiles. Consistent with cluster analysis, microarray principal component 1, capturing most of the dataset variance (36%), separated the sites in tidal and non-tidal (Figure 3a). The first class, at negative values on PC1m, included most of the tidal sites located in the Rhine-Meuse delta; the second class, at positive values along the PC1m, comprised most of the non-tidal sites. Interestingly, as in cluster analysis, MegI-II showed similarities with the first group, while SpiIII and HeiI-II grouped with the second, although according to their hydrological characteristics they were consistent with the non-tidal and tidal group, respectively. Two main clusters were evident



50

Spi

IVR

ozIV

Spi

IIM

egII

Spi

IR

ozII

Roz

IIIR

ozI

Meg

IS

piIII

HdD

IIIH

dDI

HdD

IIO

uWIII

Stw

IIB

lkII

Hei

IIH

eiI

StrI

HdD

IVO

uWII

OuW

IS

trII

Blk

IK

onI0.0

0.2

0.4

0.6

0.8

1.0Li

nkag

e di

stan

cea

SpiIV

RozI

VSp

iIIM

egII

SpiI

RozI

IRo

zIII

RozI

Meg

ISp

iIII

HdD

III

HdD

IH

dDII

OuW

III

Stw

IIBl

KII

Hei

IIH

eiI

StrI

HdD

IVO

uWII

OuW

ISt

rII

BlkI

Kon

I

1.00.80.60.40.20.0

OuW

IIO

uWI

HdD

IVS

trII

StrI

Meg

IIM

egI

Stw

IIS

twIII

Stw

IB

lkII

Spi

IR

ozII

Roz

IVR

ozIII

Roz

IK

onI

Spi

IVH

eiII

Ew

kIS

piII

Spi

IIIH

eiI0.2

0.4

0.6

0.8

1.0

Link

age

dist

anceb

OuW

IIO

uWI

HdD

IVSt

rII

StrI

Meg

IIM

egI

Strw

IISt

wII

ISt

wI

BlK

IISp

iIRo

zII

RozI

VRo

zIII

RozI

Kon

ISp

iIVH

eiII

Ew

kISp

iIISp

iIII

Hei

I

0.8

0.6

0.4

0.2

1.0

HdD

IIH

dDIII

HdD

IS

twIII

Stw

IIO

uWII

OuW

IS

trII

HdD

IVB

lkII

Blk

IO

uWIII

Meg

IE

wkI

IS

twI

StrI

Roz

IVR

ozIII

Spi

IVS

piII

Spi

IM

egII

Hei

IH

eiII

Kon

IS

piIII

Roz

IIE

wkI

Roz

I0.0

2.0

4.0

6.0

8.0

10.0

Link

age

dist

ancec

10.08.06.04.02.00.0

HdD

IIH

dDII

IH

dDI

Stw

III

Stw

IIO

uWII

OuW

ISt

rII

HdD

IVBl

KII

BlK

IO

uWII

IM

egI

Ew

kII

Stw

ISt

rIRo

zIV

RozI

IISp

iIVSp

iII SpiI

Meg

IIH

eiI

Hei

IIK

onI

SpiII

IRo

zII

Ew

kIRo

zI

Spi

IVR

ozIV

Spi

IIM

egII

Spi

IR

ozII

Roz

IIIR

ozI

Meg

IS

piIII

HdD

IIIH

dDI

HdD

IIO

uWIII

Stw

IIB

lkII

Hei

IIH

eiI

StrI

HdD

IVO

uWII

OuW

IS

trII

Blk

IK

onI0.0

0.2

0.4

0.6

0.8

1.0Li

nkag

e di

stan

cea

SpiIV

RozI

VSp

iIIM

egII

SpiI

RozI

IRo

zIII

RozI

Meg

ISp

iIII

HdD

III

HdD

IH

dDII

OuW

III

Stw

IIBl

KII

Hei

IIH

eiI

StrI

HdD

IVO

uWII

OuW

ISt

rII

BlkI

Kon

I

1.00.80.60.40.20.0

Spi

IVR

ozIV

Spi

IIM

egII

Spi

IR

ozII

Roz

IIIR

ozI

Meg

IS

piIII

HdD

IIIH

dDI

HdD

IIO

uWIII

Stw

IIB

lkII

Hei

IIH

eiI

StrI

HdD

IVO

uWII

OuW

IS

trII

Blk

IK

onI0.0

0.2

0.4

0.6

0.8

1.0Li

nkag

e di

stan

cea

SpiIV

RozI

VSp

iIIM

egII

SpiI

RozI

IRo

zIII

RozI

Meg

ISp

iIII

HdD

III

HdD

IH

dDII

OuW

III

Stw

IIBl

KII

Hei

IIH

eiI

StrI

HdD

IVO

uWII

OuW

ISt

rII

BlkI

Kon

I

1.00.80.60.40.20.0

1.00.80.60.40.20.0

OuW

IIO

uWI

HdD

IVS

trII

StrI

Meg

IIM

egI

Stw

IIS

twIII

Stw

IB

lkII

Spi

IR

ozII

Roz

IVR

ozIII

Roz

IK

onI

Spi

IVH

eiII

Ew

kIS

piII

Spi

IIIH

eiI0.2

0.4

0.6

0.8

1.0

Link

age

dist

anceb

OuW

IIO

uWI

HdD

IVSt

rII

StrI

Meg

IIM

egI

Strw

IISt

wII

ISt

wI

BlK

IISp

iIRo

zII

RozI

VRo

zIII

RozI

Kon

ISp

iIVH

eiII

Ew

kISp

iIISp

iIII

Hei

I

0.8

0.6

0.4

0.2

1.0

OuW

IIO

uWI

HdD

IVS

trII

StrI

Meg

IIM

egI

Stw

IIS

twIII

Stw

IB

lkII

Spi

IR

ozII

Roz

IVR

ozIII

Roz

IK

onI

Spi

IVH

eiII

Ew

kIS

piII

Spi

IIIH

eiI0.2

0.4

0.6

0.8

1.0

Link

age

dist

anceb

OuW

IIO

uWI

HdD

IVSt

rII

StrI

Meg

IIM

egI

Strw

IISt

wII

ISt

wI

BlK

IISp

iIRo

zII

RozI

VRo

zIII

RozI

Kon

ISp

iIVH

eiII

Ew

kISp

iIISp

iIII

Hei

I

0.8

0.6

0.4

0.2

1.0

0.8

0.6

0.4

0.2

1.0

HdD

IIH

dDIII

HdD

IS

twIII

Stw

IIO

uWII

OuW

IS

trII

HdD

IVB

lkII

Blk

IO

uWIII

Meg

IE

wkI

IS

twI

StrI

Roz

IVR

ozIII

Spi

IVS

piII

Spi

IM

egII

Hei

IH

eiII

Kon

IS

piIII

Roz

IIE

wkI

Roz

I0.0

2.0

4.0

6.0

8.0

10.0

Link

age

dist

ancec

10.08.06.04.02.00.0

HdD

IIH

dDII

IH

dDI

Stw

III

Stw

IIO

uWII

OuW

ISt

rII

HdD

IVBl

KII

BlK

IO

uWII

IM

egI

Ew

kII

Stw

ISt

rIRo

zIV

RozI

IISp

iIVSp

iII SpiI

Meg

IIH

eiI

Hei

IIK

onI

SpiII

IRo

zII

Ew

kIRo

zI

HdD

IIH

dDIII

HdD

IS

twIII

Stw

IIO

uWII

OuW

IS

trII

HdD

IVB

lkII

Blk

IO

uWIII

Meg

IE

wkI

IS

twI

StrI

Roz

IVR

ozIII

Spi

IVS

piII

Spi

IM

egII

Hei

IH

eiII

Kon

IS

piIII

Roz

IIE

wkI

Roz

I0.0

2.0

4.0

6.0

8.0

10.0

HdD

IIH

dDIII

HdD

IS

twIII

Stw

IIO

uWII

OuW

IS

trII

HdD

IVB

lkII

Blk

IO

uWIII

Meg

IE

wkI

IS

twI

StrI

Roz

IVR

ozIII

Spi

IVS

piII

Spi

IM

egII

Hei

IH

eiII

Kon

IS

piIII

Roz

IIE

wkI

Roz

I0.0

2.0

4.0

6.0

8.0

10.0

Link

age

dist

ancec

10.08.06.04.02.00.0

HdD

IIH

dDII

IH

dDI

Stw

III

Stw

IIO

uWII

OuW

ISt

rII

HdD

IVBl

KII

BlK

IO

uWII

IM

egI

Ew

kII

Stw

ISt

rIRo

zIV

RozI

IISp

iIVSp

iII SpiI

Meg

IIH

eiI

Hei

IIK

onI

SpiII

IRo

zII

Ew

kIRo

zI

FIGURE 2 Classification of floodplain soil samples based on microarray (a), denaturing gradient gel electrophoresis (b), and polar lipid-derived fatty acids (c) analyses. , tidal locations; ,non-tidal locations.

FIGURE 3 Projection of the soil samples on the ordination plane formed by principal components 1 and 2 from microarray (a), denaturing gradient gel electrophoresis (b), and polar lipid-derived fatty acids (c) data. The variance explained by each component is indicated as percentage at the axes. , tidal locations; , non-tidal locations.



51

-10 -8 -6 -4 -2 0 2 4-6

-4

-2

0

2

4

SpiISpiIII

RozI-III

MegIMegII

RozIVSpiIV

SpiII

HeiI-II

othernon-tidallocations

PC1: 36%

PC2:

14%

a

KonI

4

-6

-4

-2

0

2

-10 -8 -6 -4 -2 0 2 4

-10 -8 -6 -4 -2 0 2 4 6 8-8

-6

-4

-2

0

2

4

6

RozI-IV

SpiI

SpiII-IV

BlKII

MegIPC2:

11%

StwIII

StwII

StwI

MegII

StrI

StrII

HeiI

HeiII

OuWI-IIHdDIV

KonI

EwkI

PC1: 15%

b6

-8

4

2

0

-2

-4

-6

-10 8-8 -6 -4 -2 0 2 4 6

-4 -3 -2 -1 0 1 2 3-3

-2

-1

0

1

2

3

RozIV

RozIII

EwkI

MegII

SpiI-III

SpiIV RozI

RozII

KonIHeiII

other non-tidallocations

PC1: 44%

PC2:

19%

c

HeiI

3

-3

2

1

0

-1

-2

-4 3210-1-2-3

-10 -8 -6 -4 -2 0 2 4-6

-4

-2

0

2

4

SpiISpiIII

RozI-III

MegIMegII

RozIVSpiIV

SpiII

HeiI-II


PC1: 36%

PC2:

14%

a

KonI

4

-6

-4

-2

0

2

-10 -8 -6 -4 -2 0 2 4-10 -8 -6 -4 -2 0 2 4-6

-4

-2

0

2

4

SpiISpiIII

RozI-III

MegIMegII

RozIVSpiIV

SpiII

HeiI-II


PC1: 36%

PC2:

14%

a

KonI

-10 -8 -6 -4 -2 0 2 4-6

-4

-2

0

2

4

SpiISpiIII

RozI-III

MegIMegII

RozIVSpiIV

SpiII

HeiI-II


PC1: 36%

PC2:

14%

a

KonI

4

-6

-4

-2

0

2

4

-6

-4

-2

0

2

-10 -8 -6 -4 -2 0 2 4

-10 -8 -6 -4 -2 0 2 4 6 8-8

-6

-4

-2

0

2

4

6

RozI-IV

SpiI

SpiII-IV

BlKII

MegIPC2:

11%

StwIII

StwII

StwI

MegII

StrI

StrII

HeiI

HeiII

OuWI-IIHdDIV

KonI

EwkI

PC1: 15%

b6

-8

4

2

0

-2

-4

-6

-10 8-8 -6 -4 -2 0 2 4 6-10 -8 -6 -4 -2 0 2 4 6 8-8

-6

-4

-2

0

2

4

6

RozI-IV

SpiI

SpiII-IV

BlKII

MegIPC2:

11%

StwIII

StwII

StwI

MegII

StrI

StrII

HeiI

HeiII

OuWI-IIHdDIV

KonI

EwkI

PC1: 15%

b

-10 -8 -6 -4 -2 0 2 4 6 8-8

-6

-4

-2

0

2

4

6

RozI-IV

SpiI

SpiII-IV

BlKII

MegIPC2:

11%

StwIII

StwII

StwI

MegII

StrI

StrII

HeiI

HeiII

OuWI-IIHdDIV

KonI

EwkI

-10 -8 -6 -4 -2 0 2 4 6 8-8

-6

-4

-2

0

2

4

6

RozI-IV

SpiI

SpiII-IV

BlKII

MegIPC2:

11%

StwIII

StwII

StwI

MegII

StrI

StrII

HeiI

HeiII

OuWI-IIHdDIV

KonI

EwkI

PC1: 15%

b6

-8

4

2

0

-2

-4

-6

6

-8

4

2

0

-2

-4

-6

-10 8-8 -6 -4 -2 0 2 4 6-10 8-8 -6 -4 -2 0 2 4 6

-4 -3 -2 -1 0 1 2 3-3

-2

-1

0

1

2

3

RozIV

RozIII

EwkI

MegII

SpiI-III

SpiIV RozI

RozII

KonIHeiII


PC1: 44%

PC2:

19%

c

HeiI

3

-3

2

1

0

-1

-2

-4 3210-1-2-3-4 -3 -2 -1 0 1 2 3-3

-2

-1

0

1

2

3

RozIV

RozIII

EwkI

MegII

SpiI-III

SpiIV RozI

RozII

KonIHeiII


PC1: 44%

PC2:

19%

c

HeiI

-4 -3 -2 -1 0 1 2 3-3

-2

-1

0

1

2

3

RozIV

RozIII

EwkI

MegII

SpiI-III

SpiIV RozI

RozII

KonIHeiII


PC1: 44%

PC2:

19%

c

HeiI

3

-3

2

1

0

-1

-2

-4 3210-1-2-3



52

along the principal component 2 (Figure 3a). The first cluster, at the most negative values, comprised SpiI and SpiIII, while all other locations grouped at higher values along PC2m.The projection of the samples on the ordination axis formed by components 1-2 of the DGGE data (Figure 3b), divided again tidal sites from non-tidal sites. The first group was distributed at the negative values along PC1d and at the positive values along PC2d, and vice versa for the second group. The exceptions indicated before by the cluster analysis were on the borderline between the two groups. The PLFAs-based PC1p PC2p ordination plot is represented in Figure 3c.Consistent with the cluster analysis, samples were divided according to their tidal or non-tidal character, as the fatty acids profiles of most of the eastern non-tidal sites grouped together at negative values; exceptions are RozIII-IV. Most of the western tidal sites grouped together at positive values; exceptions are again MegII, KonI and EwkI. Along PC2p SpiI, SpiII and SpiIII, which clustered together, and EwkI were separated from the other locations at the most positive and the most negative values along the axis, respectively.

Contribution of environmental variables to the principal components With the principal component analysis it is possible to define the variables (i.e. microarray probes, DGGE bands, and PLFAs peaks) that contributed most to the generation of the principal components. The dsrAB-based phylogenetic inventory and dsrB-DGGE profile of SRPs in RozI soil obtained in a previous study (Chapter II) provided an independent reference for validating some of the results of the microarray, DGGE, and PLFAs analyses of this study. Six out of twenty-three positive microarray probes contributed together for 56% to PC1m (Table 3). Probes DSSDBM217 and DSSDBM998 specifically targeting Desulfosarcina variabilis (Loy et al., 2002) were positive only in RozI-III, SpiII and MegI, all belonging to the tidal group shown in Figure 2 and 3. Although two other probes with the same specificity (DSSDBM1286 and DSSDBM194) were negative (data not shown), the presence of D. variabilis-related organisms in these soil samples was indicated by sequencing of dsrAB clones and dsrB-DGGE bands from RozI soil (Chapter II). DGGE bands 24, 25, 28, and 29 from RozI soil were affiliated with D. variabilis and matched DGGE bands present in the other four sites.



53

Probe DFMII1281 perfectly matching Desulfosporosinus species (Loy et al., 2002) showed the same hybridization pattern as probes DSSDBM217 and DSSDBM998 and contributed for a 9% to PC1m. However, the presence of Desulfosporosinus species in RozI-III and SpiI could not be unambiguously determined as a second probe with corresponding specificity (DFMII1107) did not give a positive result in any of these samples (data not shown), and no dsrB sequence closely related to the Firmicutes was detected in the reference profile of RozI. Probes DSBACL143 and DSBACL225 (in total 18%, Table 3) with a perfect match to Desulfobacula species gave a positive hybridization in RozI-III (the second probe also positive in RozIV), SpiI, and all Megen sites. Strict adherence to the multiple probe concept (Loy et al., 2002) would require positive signals from further probes such as DSBACL317, DSBACL1268, and DSBASCL1434 for an unambiguous detection of Desulfobacula species. Whereas probe DSBACL317 scored positive for RozI (and in RozII-III the signal-to-noise ratio was just below the threshold value) and SpiI, probes DSBACL1268, and DSBASCL1434 were always negative (data not shown). No DGGE band related to the monophyletic Desulfobacula-Desulfotignum-Desulfospira-Desulfobacter group was retrieved from DGGE profile of RozI. However, presence of SRPs that are affiliated with this group was clearly indicated by dsrAB clone IV33 (Chapter II). Alternatively, positive signals from probes DSBACL143 and DSBACL225 might be (partly) the consequence of cross-hybridization with other members of the family Desulfobacteraceae or the genus Desulfomonile (Loy et al., 2002); presence of both taxa is confirmed in RozI soil (Chapter II). Probe DSMON999 targeting uncultured Desulfomonile-related microorganisms (Loy et al., 2007) contributed for 9% to PC1m, and scored positively in RozI-III and SpiII sites. However, also in this case the multiple probe concept was not fulfilled by a positive hybridization of additional SRP-PhyloChip probes with similar specificities, i.e. DSMON446, DSMON447, DSMON468a, DSMON468b. Presence of Desulfomonile-related SRPs at these sites was nevertheless unambiguously corroborated by comparison of the respective DGGE profiles with data on the SRPs community composition for RozI. The two probes DVHO831 and DVHO1424, specifically targeting Desulfovibrio halophilus and Desulfovibrio oxyclinae, were positive in some Spijkenisse and Rozenburg locations (data not shown), and contributed together for 28% to PC2m. However the presence of these cultured SRPs was not confirmed, because signals of two other probes with identical specificity did not exceed the threshold value (data not shown) and a


TA

BL

E 3

V

ariab

les th

at c

ontri

bute

d m

ost t

o th

e pr

incip

al co

mpo

nent

1 o

f the

micr

oarr

ay a

nd D

GG

E d

ata.

The

cont

ribut

ion

is in

dica

ted

as p

erce

ntag

e.

, tid

al lo

catio

ns;

, non

-tida

l loc

atio

ns. –

, not

ava

ilabl

e.

HeiI

HeiII

KonI

MegI

MegII

SpiI

SpiII

SpiIII

SpiIV

BlkI

BlkII

EwkI

EwkII

OuWI

OuWII

OuWIII

RozI

RozII

RozIII

RozIV

StwI

StwII

StwIII

StrI

StrII

HdDI

HdDII

HdDIII

HdDIV

Spec

ifici

ty/A

ffili

atio

nb

DSS

DBM

217

(10%

)-

--

-D

esulfo

sarci

na s

p.

DSS

DBM

998

(10%

)-

--

-D

esulfo

sarci

na s

p.

DFM

II12

81 (9

%)

--

--

Desu

lfosp

orosin

us sp

.

DSB

ACL

143

(9%

)-

--

-D

esulfo

bacu

la sp

.

DSB

ACL

225

(9%

)-

--

-D

esulfo

bacu

la sp

.

DSM

ON

999

(9%

)-

--

-U

ncul

ture

d D

esulfo

monil

e sp

.

Band

29

(7%

)-

--

--

-D

esulfo

sarci

na sp

.

Band

1 (6

%)

--

--

--

Unc

ultu

red

Desu

lfoba

ctera

ceae

Band

2 (6

%)

--

--

--

Unc

ultu

red

Desu

lfoba

ctera

ceae

PC1 m

PC1 d

Loca

tions

Prin

cipa

l Com

pone

nt,

Prob

e or

Ban

da

aBa

nd n

omen

clatu

re fo

llow

s Cha

pter

II.

bPr

obe

spec

ificit

y an

d ba

nd a

ffilia

tion

is ac

cord

ing

to L

oy e

t al.

(200

2) a

nd C

hapt

er II

, res

pect

ivel

y.


54


TA

BL

E 4

V

ariab

les t

hat

cont

ribut

ed m

ostly

to

the

prin

cipal

com

pone

nt 1

of

the

PLFA

dat

a. Th

e co

ntrib

utio

n is

indi

cate

d as

per

cent

age.

Valu

es

corr

espo

nd to

the

PLFA

relat

ive

abun

danc

e. HeiI

HeiII

KonI

MegI

MegII

SpiI

SpiII

SpiIII

SpiIV

BlkI

BlkII

EwkI

EwkII

OuWI

OuWII

OuWIII

RozI

RozII

RozIII

RozIV

StwI

StwII

StwIII

StrI

StrII

HdDI

HdDII

HdDIII

HdDIV

Spec

ifici

tyb

10M

eC16

:0 (2

5%)

3.8

6.9

6.5

3.9

4.9

8.7

9.5

5.6

8.6

2.6

2.5

6.2

3.8

2.0

1.5

3.3

7.5

8.5

5.5

4.6

4.2

0.3

1.7

3.8

1.1

3.2

3.7

2.5

4.6

Desu

lfoba

cter

sp.

CyC1

7:0

(21%

)3.

24.

44.

23.

13.

33.

73.

73.

23.

52.

23.

03.

12.

41.

83.

12.

23.

53.

53.

02.

01.

61.

83.

01.

62.

93.

02.

51.

70.

9D

esulfo

bacte

r sp

.

Loca

tions

Prin

cipa

l Com

pone

nt,

PLFA

a

PC1 p

aPL

FAs n

omen

clatu

re fo

llow

s Guc

kert

et a

l. (1

985)

bPL

FAs s

peci

ficity

is a

ccor

ding

to K

ohrin

g et

al.

(199

4)


55



56

D. halophilus/D. oxyclinae-specific 16S rRNA gene-targeted PCR carried out at stringent conditions did not yield any product. Additionally, no information could be inferred from DGGE analyses, as no dsrB band related to the Desulfovibrionales was retrieved from RozI (Chapter II). Probe SYBAC986 contributed significantly to PC2m (13%). This oligonucleotide, together with probes SYBAC697, SYBAC587a, SYBAC587b, and SYN835, is specific for microorganisms belonging to the Desulforhabdus-Desulfovirga-Syntrophobacter line of descent and gave a positive hybridization in many tidal as well as non-tidal sites (data not shown). Although probe SYBAC697 was positive for most of the sites tested and probe SYN835 was positive in SpiI, the other two probes were always negative (data not shown). The widespread distribution of Syntrophobacteraceae-related SRPs in the river floodplains samples was supported by the DGGE data, as DGGE band 13 from RozI was affiliated with this group of microorganisms (Chapter II) and corresponded to bands from most of the other sites (data not shown). Three DGGE bands (Table 3) were highlighted as those contributing for an overall 19% to the PC1d (a total of 88 different DGGE bands were detected by digital analysis of polyacrylamide gels). These bands were assigned a putative identity by matching with sequenced and phylogenetically analyzed DGGE bands of RozI (Chapter II). Band 29, is affiliated with D. variabilis and matched with bands present in RozII-IV, HeiII, SpiII and MegI-II. DGGE bands 1 and 2 are related to uncultured representatives of the family Desulfobacteraceae and showed the same Rf of bands present in other tidal, but also in some non-tidal locations (Table 3). Six additional bands contributed for a cumulative 20% to PC2d; however, their phylogenetic affiliation is unknown as they could not be matched with any of the known bands of RozI (data not shown). With respect to PLFAs, 10MeC16:0 (25%) and CyC17:0 (21%) gave the highest contribution to PC1p, and had higher values in the tidal locations (Table 4). Instead, C17:0 contributed for 66% to PC2p and also in this case the relative abundance values where higher in the tidal locations (data not shown). Although these fatty acids are not exclusively present in SRPs, among SRPs 10MeC16:0 and CyC17:0 can be considered characteristic for Desulfobacter sp., while C17:0 is a marker for Desulfobacterium, Desulfosarcina,and Desulfococcus (Kohring et al., 1994).

Correlation analysisTo interpret the microarray, DGGE and PLFAs profiles in terms of environmental parameters, correlation analysis was carried out between



57

TABLE 5 Correlation coefficients between PC1-2 for the three datasets and environmental variables. Variables with at least one significant correlation and significant values are shown. The significance level is indicated with a * (p


58

The most complex pattern of correlations appeared from the analysis between PLFAs-related principal components and soil/pore water variables of the floodplains. Most of the environmental variables were significantly associated with PC1-2p (Table 5). PC1p was positively correlated with soil ammonium, phosphate in the soil and pore water (PO43 s/PO43 pw, at high degree of significance), as well as with S2 pw. PC2p was positively related to many soil variables (OMs, TNs, TPs, TKs, NO3 s, SO42 s), and negatively to two pore water variables (Cl pw, Na+pw) with mostly high correlation coefficients (r>0.40) and various degrees of significance (p


59

of three of the major river systems crossing the Netherlands. Fingerprints obtained with three different methods, i.e. 16S rRNA gene-based microarray (SRP-PhyloChip), dsrAB-based denaturing gradient gel electrophoresis (DGGE) and polar lipid-derived fatty acids (PLFAs) analyses, were used as a proxy to describe the SRPs diversity in the floodplain samples. In some cases, the occurrence of known SRPs as suggested by microarray hybridizations could be cross-confirmed by DGGE analyses. Each set of profiles was subjected to a combined multivariate/correlation analysis. In this way it was possible to compare SRPs community profiles and to highlight the environmental soil and pore water variables possibly influencing the distribution of the microarray/DGGE/PLFAs-described SRPs communities along environmental gradients.

Surprisingly, all profiles from the tidal sites located on the Rhine-Meuse delta were consistently separated from all other not-tidal sites located more inland. This result was independent from the screening method and the multivariate statistics used (i.e. similarity/distance-based cluster analysis or correlation-based PCA), proving its robustness. Based on correlation analysis, the distribution of the microarray/DGGE/PLFAs-based fingerprints in the principal component plots formed by principal component 1 (PC1) and principal component 2 (PC2), could be related to a total of 15 variables; 8 soil variables, i.e. soil organic matter (OMs), total nitrogen (TNs), NO3–s, NH4+s, total phosphorous (TPs), PO43+s, SO42–s, and total potassium (TKs) and 7 pore water variables, i.e. PO43+pw, SO42–pw, S2–pw,Cl–pw, Na+pw, K+pw and Mg2+pw.

The distribution of the fingerprints along the PC1 based on microarray and DGGE analyses (i.e. PC1m and PC1d) correlated to salinity-related pore water variables, i.e. Cl–pw, Na+pw, K+pw, and Mg2+pw. These variables in particular showed higher values in the tidal locations of the Rhine-Meuse delta compared to the non-tidal ones located more inland, likely as a consequence of the present and past sea influence on the first locations. The probes/bands that contributed most to PC1m and PC1d were present only in some of the tidal areas. This might indicate a tight linkage between the preferential growth of the SRPs represented by these probes/bands and a condition of higher salinity and lower degree of plant nutrient enrichment. The presence of Desulfosarcina-related SRPs in Rozenburg and Spijkenisse was indicated both by microarray and DGGE analyses. Furthermore, bands 1 and 2 that contributed highly to PC1d and that were detected in Spijkenisse, Rozenburg and Heinenoord, affiliated with uncultured



60

Desulfobacteraceae. The occurrence of the Desulfobacteraceae, and in particular Desulfosarcina-related microbes, in estuarine (Joulian et al., 2001), marine (Orphan et al., 2001; Llobet-Brossa et al., 2002; Klepac-Ceraj et al., 2004; Leloup et al., 2007), and even hypersaline environments (Kjeldsen et al., 2007) was often described. In a recent biogeography study on the distribution of SRPs along an estuarine gradient, some evidence was obtained of a salinity dependent distribution of the Desulfobacteraceae, that were localized in more saline environments (Kondo et al., 2007). In another study, the SRPs communities inhabiting contrasting river mudflats were analyzed (Leloup et al., 2006). The majority of the sequences retrieved from the mixing-zone mudflat, with marine characteristics, were affiliated with the Desulfobacteraceae (Leloup et al., 2006). Finally, the SRPs communities have been compared between two sites with different characteristics, i.e. an agricultural freshwater grassland and a semi-natural oligohaline marshland with recent sea influence (Chapter IV). The Desulfosarcina-related dsrB were detected only in the oligohaline site (see Chapter IV). This further supports salinity as the key factor in determining the occurrence of Desulfosarcina-related SRPs in the environment. Similarly, the presence of Desulfomonile-related SRPs were only present in Rozenburg and Spijkenisse. In a mesocosm experiment, soil monoliths from the two sites mentioned above have been subjected to a treatment with water of different salinities (see Chapter IV). Desulfomonile-related dsrBwere detected only in the oligohaline treatment/site, indicating that the occurrence of microbes related to this cultured sulfate reducer might be dependent on salinity.

Distribution of profiles along PC1m and PC1d could also be correlated with the soil variables OMs, TNs, NO3–s, TPs, and TKs that showed lower values in the tidal locations compared to the non-tidal ones, probably due to the enrichment in plant nutrients as a consequence of agricultural land use in the latter. However, we could not find confirmation in the literature for the positive effect of lower plant nutrient concentrations on the presence of Desulfobacteraceae- and Desulfomonile-related SRPs, as suggested from our principal component analyses. Nevertheless, the occurrence of these SRPs in marine environments may be determined not only by the salinity, but also by the generally less eutrophic conditions in marine systems, compared to agricultural areas. The latter could explain the occurrence of these SRPs in the tidal floodplain soils with lower trophic status.



61

Also probe DFMII1281 targeting Desulfosporosinus and probes DSBACL143-DSBACL225 targeting Desulfobacula that contributed significantly to PC1mand PC1d gave positive results exclusively in the tidal cluster. However, the presence of these cultured SRPs was not confirmed by the other probes with the same specificity on the microarray, nor by DGGE; thus, the SRPs present in Rozenburg and Spijkenisse likely represent only uncultured Desulfosporosinus- and Desulfobacula-related organisms with different ecophysiology from their cultured relatives. Moreover, this result could be biased by cross-hybridization with Desulfomonile-related SRPs present in these samples.Finally, the presence of Desulfobacteraceae in the non-tidal and non-saline locations MegI, BlkI and OuWI, as indicated by both probes and DGGE bands suggests that regulating factors other than salinity and OMs, TNs,NO3–s, TPs, TKs might influence the occurrence of this group of microbes. This is not surprising taking the physiological versatility of these SRPs into account (Rabus et al., 2006).

Interestingly, the distribution of PLFAs profiles along PC1 seemed to be dependent on the concentration of two soil variables, i.e. NH4+s and PO43+s,and two pore water variables, i.e. PO43+pw and S2–pw that showed higher values in the tidal locations. These variables can be an indicator for anoxia, either directly in the case of NH4+s and S2–pw, or indirectly with PO43+ as the presence of sulfide might promote the release of this ion from metal-bound deposits in soil (Lamers et al., 1998); therefore these variables can be an indicator of conditions suitable for SRPs growth. As PLFAs are present in the membrane of all bacterial cells, the concentration of PLFAs is a measure of the total microbial biomass. PLFAs that contributed most to PC1p were Desulfobacter-related (10MeC16:0 and CyC17:0). They showed higher values in the tidal areas, indicating that these SRPs are more abundant in these more frequently flooded areas.

The second microarray- and DGGE-related principal components were not very informative, as the distribution of the fingerprints along these axes showed no correlation with any of the environmental soil and pore water variables. This might indicate that the distribution of the uncultured SRPs, whose 16S rRNA genes hybridized with the Desulfovibrio halophilus and Desulfovibrio oxyclinae-specific probes DVHO831 and DVHO1424 only in the tidal areas of Rozenburg and Spijkenisse, might be related to other, non measured, parameters. A similar conclusion might be drawn for the Desulforhabdus-Desulfovirga-Syntrophobacter-related SRPs detected by probe



62

SYBAC986, that gave a positive result in most of the locations studied. Finally, PC2 obtained from PLFAs data showed correlations with both salinity and plant nutrients in soil, and was associated mainly with a Desulfobacterium-, Desulfosarcina-, Desulfococcus-related PLFA (C17:0) displaying higher values in the tidal locations. However, the amount of variation explained by PC2p was rather low (i.e. 19%).

Interestingly, SRPs distribution could not be linked to the particular vegetation covering the floodplains studied (cf. Figure 1 and Table 1). This either suggests the presence of largely inactive SRPs communities in these floodplains, or that soil physico-chemical factors have a stronger impact on the composition of the SRPs communities in these habitats. A similar result has been obtained in an analysis of the distribution of ammonia-oxidizing bacteria in a vegetated, tidal freshwater marsh in the Scheldt estuary (Laanbroek and Speksnijder, submitted). The distribution of these bacteria was determined by the location in the flooding gradient and not dependent on the presence or absence of a particular plant species.

Most studies of freshwater SRPs communities have been carried out in permanently waterlogged and reduced soils, both natural, i.e. fens (Loy et al., 2004) and marshes (Castro et al., 2002), or man-made, i.e. rice paddies (Scheid and Stubner, 2001). Hence, another interesting result of this study was the presence of anaerobic sulfate reducers in all floodplain samples studied, independently from the redox state of the soil. Moreover, communities seem highly diversified, as suggested by the complex DNA-based microarray and DGGE profiles of the soils analyzed. Oxygen defense strategies have been described in SRPs (Dolla et al., 2006; Rabus et al., 2006); in addition, anoxic niches can persist in soil particles for prolonged periods of drought (Madigan et al., 2003). The results of our survey might be biased by the facts that (i) not all microorganisms classified as SRPs on the basis of the 16S rRNA gene are able to perform sulfate reduction, either as they do not possess the dsrAB or as they turn to syntrophy as an adaptative response to thrive in low-sulfate, methanogenic environments (Imachi et al., 2006), and that (ii) the dsrAB might derive also from other non-sulfate-reducing microorganisms, i.e. sulfur oxidizers (Schedel and Truper, 1979; Dahl et al., 1999; Sabehi et al., 2005), sulfite and/or organosulfonates reducers (Cook et al., 1998). Nevertheless, in a recent microcosm experiment with soil collected from two of the locations studied in this field survey, i.e. Huis den Doorn and Ewijk, showed high sulfate reduction rates upon flooding with sulfate-rich water despite the dry



63

conditions of the soil at the time of soil collection (Chapter V). Collectively, these results indicate that these soils have the potential to perform active sulfate reduction almost immediately upon flooding.

In conclusion, this study clearly shows that even in these non- or temporarily flooded soils SRPs are present in sufficiently high number to allow detection by PCR, and that their communities are highly diversified. In restoring floodplains, the anoxic biogeochemistry has most certainly to be considered, as the presence of SRPs will not be a factor limiting the onset of sulfate reduction and associated biogeochemical changes. However, from this study it is also clear that the SRPs display biogeographic patterns within the floodplains, indicating that the response of the different communities upon flooding may not necessarily be the same.

ACKNOWLEDGEMENTS

We greatly thank Sebastian Lücker for the help with microarray analysis and Kees Hordijk for the assistance with PLFAs analyses. Diana Lebherz is acknowledged for performing the D. halophilus/D. oxyclinae-specific PCR. This research was funded by the Netherlands Organization for Scientific Research (NWO, TRIAS project 835.80.010 ‘Biogeochemical constraints for sustainable development of floodplains in riverine regions’) and the Fonds zur Förderung der wissenschaftlichen Forschung (FWF, project P18836-B17).


Documents

MARZIA MILETTO INSIDE 191107 LA - Universiteit Utrecht · 2020. 3. 4. · 1Department of Microbial Wetland Ecology, Netherlands Institute ... dsrB-based denaturing gradient gel electrophoresis