Interactions between Zn and bacteria in marine tropical coastal sediments
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Interactions between Zn and bacteria in marine tropicalcoastal sediments
Olivier Pringault & Hlna Viret & Robert Duran
Received: 6 July 2011 /Accepted: 11 September 2011 /Published online: 28 September 2011# Springer-Verlag 2011
AbstractPurpose The main goals of this study were (1) to examinethe effects of zinc on the microbial community structure ofanthropogenically impacted sediments in a tropical coastalecosystem and (2) to determine whether these microbialbenthic communities may enhance the adsorption of zinc.Methods The interactions between zinc and bacteria intropical sediments were studied in sediment microcosmsamended with 2.5 mg L1 of Zn in the water phase andincubated for 8 days under different environmental con-ditions, oxic/anoxic and glucose addition. At the end ofincubation, microbial structure was assessed by molecularfingerprints (T-RFLP) analysis and Zn speciation in thesediment was determined by sequential extraction.Results In the three studied sediments, Zn spiking resulted inonly slight changes in bacterial community structure. Incontrast, the addition of low concentrations of glucose (5 mM)strongly modified the bacterial community structure:
capacities, can remobilize metal from sediments (Flemminget al. 1990; Petersen et al. 1997). This ability to mitigate thefate of metals opens up interesting perspectives for the usethese microorganisms in bioremediation applications (Gadd2000).
Coastal sediments support many different kinds ofbacterial metabolism involving degradation of organicmatter under both oxic and anoxic conditions. Consequent-ly, benthic bacterial communities can influence the fate of ametal in various ways, employing a number of differentmetabolic processes. For example, sulfate-reducing bacte-ria, through the production of sulfide, which strongly bindsdivalent heavy metals, could play a major part in metalremoval at the sedimentwater interface if the environmen-tal conditions are conducive to sulfate reduction (White etal. 1997). As a consequence, benthic bacterial communitiesmay directly interact with the fate of metal by, e.g., sulfideproduction or indirectly interact by oxygen depletion thusfavoring sulfide production. As in most coastal environ-ments, the inshore waters of New Caledonia are subject tolarge increases in urbanization together with an importantdevelopment in nickel mining activities. Unfortunately, thisdevelopment is concomitant with inadequate wastewatertreatment, resulting in large inputs of heavy metals such asZn, Cr, and Ni into the lagoon (Fernandez et al. 2006).Coastal sediments in the vicinity of Nouma (NewCaledonia capital, 100,000 inhabitants in 2000) exhibithigh levels of Zn, Ni, and Cr contamination (Dalto et al.2006). Such conditions are, therefore, favorable for theestablishment of microbial communities that may tolerateelevated metal inputs. Indeed, metal supplies can structurebenthic microbial communities by exerting a selectionpressure on nonmetal-resistant bacteria in favor of metal-tolerant bacteria (Mertens et al. 2006; Nazaret et al. 2003;Rasmussen and Srensen 2001).
In this study, special interest has been given to Znbecause high levels (525 g/L) are often observed in thewater column in the vicinity of Nouma due to water runoffof the surrounding area. This Zn contamination stronglymodified the phytoplankton community structure in pristineareas, whereas in polluted sites, the effects were lesspronounced (Rochelle-Newall et al. 2008). Similarly, recentstudies have shown that benthic microbial communities inNew Caledonia sediments can maintain their metabolicactivity under Zn concentrations that are known to beinhibitory for nonmetal-tolerant bacteria (Pringault et al.2008; Viret et al. 2006). To understand fully the impact ofmetal on microorganisms, the consequences of metalpollution on microbial diversity must also be assessed inorder to determine the potential role of the pollutants onmicrobial community structure. The pollution-inducedcommunity tolerance (PICT) concept (Blanck 2002) sug-gests that the microbial community structure of polluted
sites will be only slightly modified upon contaminantexposure due to pollution adaptation, as it has beenobserved for phytoplankton communities upon Znexposure (Rochelle-Newall et al. 2008) or for pesticides(Pesce et al. 2009; Vercraene-Eairmal et al. 2010) orpolycyclic aromatic hydrocarbon (Lekunberri et al. 2010).However, the link between PICT and microbial structure isnot always straightforward (Brard and Benninghoff2001) and nonpollution-tolerant populations may domi-nate in environments where species adapted to contami-nation are expected to dominate (Pringault et al. 2008).Environmental factors (nutrient availability, irradianceconditions) can strongly influence the selective actionof the pollutants on the microbial community structure,depending on the season and the microbial succession(Brard et al. 1999; Guasch et al. 1997; Lekunberri et al.2010). In addition, the effects of a metal on microbialdiversity will also depend on its bioavailability, which canbe modified by the metabolic processes involved in its fate(Gillan 2004; Gillan et al. 2005). In other words, metalconcentrations can be high in a coastal environment, but ifit is present in an innocuous form, it is likely that it wouldexert only a weak pressure on microbial communityselection.
The main goals of this study were (1) to examine theeffects of zinc on the microbial community structure ofanthropogenically impacted sediments in a tropicalcoastal ecosystem and (2) to determine whether thesemicrobial benthic communities may enhance the adsorp-tion of zinc. For that purpose, microcosms were set upunder different environmental conditions in order toinvestigate some of the main factors that might controlthe fate of Zn and its interactions with the microbialcommunity.
2 Material and methods
2.1 Sampling procedure
Sediment was collected with a Van Veen grab sampler(Hydrobios). The top surface (approximately 1 cm thick)was collected with a sterile plastic spoon and immediatelystored at ambient temperature (in degrees Celsius) anddarkness in a plastic box tightly sealed to avoid aircontamination. Seawater was sampled with a Niskin bottleat approximately 1 m above the sediment surface. Threedifferent stations were sampled (Fig. 1). The first stationwas located close to the city of Nouma (St. Marie Bay[SM]) The coast around this station is highly urbanizedand at the head of the bay there is a sewage outfall thatdeposits minimally treated household and urban wasteand runoff to the bay. The second station was located
880 Environ Sci Pollut Res (2012) 19:879892
close to the mouth of the Dumba River (Dumba Bay[DB]) and is subjected to both anthropogenic andterrigenous inputs. The latter was located in a smallbay 20 km north of Nouma and is subjected toagrochemical inputs (Port Laguerre [PL]). A moredetailed description of the sampling areas can be foundin Ouillon et al. (2010) and Fichez et al. (2010). Sedimentcharacteristics (granulometry, carbonate content, and or-ganic carbon content) were analyzed according to theprocedures described in Fernandez et al. (2006).
2.2 Microcosm setup
Upon return to the laboratory (
sterile N2 for half an hour and then the microcosms weretightly closed. Microcosms were labeled as follows: B+ or B
for microcosms nonsterilized or sterilized, respectively, andM+ or M for microcosms amended with Zn2+ or non-amended with Zn2+, respectively. Glu designates the micro-cosms amended with glucose. For each oxic/anoxiccondition, one microcosm (B+M) was incubated withoutmetal spiking as a control, one microcosm (B+M+) wasamended with 2.5 mg L1 of zinc (ZnCl2), and onemicrocosm (BM+) was amended with zinc (2.5 mg L1)and formaldehyde (43 ml final volume) in order to sterilizethe sediment and the water column. A fourth microcosm(B+M+Glu) was amended with zinc (2.5 mg L1) andglucose (5 mM, final concentration) in order to stimulatebacterial metabolic processes as suggested by Diaz-Ravinaand Baath (1996). A fifth microcosm (B+MGlu) was alsoincubated with glucose and bacteria but without Zn in bothoxic and anoxic conditions. Microcosms were incubatedduring 8 days and samples were collected daily for theanalysis of metal concentration in the water phase. Samplingof the water phase was performed under sterile conditionsand under a permanent flux of sterile N2 for the microcosmsincubated under anoxic conditions.
2.3 Metal analysis
Dissolved concentration of metal in the water phase wasmonitored daily during the microcosm incubation. For thatpurpose, a volume of 10 ml (in triplicate) was sampled andfiltered through 0.45 m pore membrane (GF/C). Acidifi-cation of the samples was achieved with nitric acid (3%final concentration). Dissolved concentration of metal wasanalyzed using an inductively coupled plasma opticalemission spectrometry (ICP-OES) following the methodof Moreton et al. (2009). At the beginning and at the end ofincubation, repartition of the metal within the different solidphases of the sediment was determined using the sequentialextraction according to the procedure of Rauret et al. (1999).This sequential extraction allows to separate three distinctfractions, phase a (exchangeable, acid- and water-solublefraction), phase b (reducible fraction), and phase c (oxidiz-able fraction). Phase a corresponds to the most bioavailableform, phase b corresponds to the form associated with ironand manganese oxyhydroxides, and phase c corresponds tothe form associated with organic matter. Zn concentrations ineach phase were then measured using an ICP-OES followingthe method of Moreton et al. (2009).
2.4 Nutrient analysis
Nutrient concentration was measured in the watercolumn. Ammonium concentration was measured fluoro-metrically, according to the protocol of Holmes et al.
(1999). The concentration of nutrients (NO2+NO3, PO4,and SiO3) was measured using an Autoanalyzer III(Bran+Luebbe), according to Raimbault et al. (1990).More details of the analytical protocols can be found inGrenz et al. (2010)
2.5 Microbial population analysis
The microbial community structure of the sediments wasanalyzed at the beginning and at the end of incubation.Sediment was homogenized prior to distribution into 2 mlEppendorf tubes. The tubes were immediately stored at80C until DNA extraction. Total DNA was extractedfrom the sediment using the UltraClean Soil DNA IsolationKit using the alternative lysis method (MoBio LaboratoriesInc., USA). The extracted genomic DNA samples werestored at 20C until further processing. Bacterial smallsubunit of ribosomal RNA (16S rRNA) genes wereamplified by polymerase chain reaction (PCR) using theuniversal primers pair R1nF and U2 (annealing to con-served regions of bacterial 16S rRNA) that amplify afragment of approximately 1,060 bp of the 16S rRNA gene.The fo rwa rd p r ime r R1nF (5 -GCTCAGATTGAACGCTGGCG-3) corresponded to positions 22 to 41of Escherichia coli 16S rRNA, and the reverse primer U2(5-ACATTTCACAACACGAGCTG-3) corresponded tothe complement of positions 1,085 to 1,066. Amplificationwas performed according to the procedure described byFourans et al. (2004). As suggested by Osborn et al.(2000), we used the terminal restriction fragment at the 5end (5-T-RF) to obtain a greater polymorphism. PrimerR1nF was fluorescently labeled with 5-tetrachloro-fluorescein. The PCR products were purified using GFXcolumns (cutoff, 100 bp; GE Healthcare, USA) prior todigestion. The restriction enzyme used in terminal restric-tion fragment length polymorphism (T-RFLP) analysis wasHaeIII (New England Biolabs, Beverly, UK). Fragmentanalysis was performed with an ABI Prism 310 (AppliedBiosystems, Foster City, USA). Dominant T-RFs from 35 to500 bp greater than 30 fluorescent units in intensity wereselected. T-RFLP profiles were normalized by calculatingrelative abundances from fluorescence intensity of each T-RF, according to Edlund and Jansson (2006). Whileassignment of identities may be uncertain using T-RFLPanalysis (Osborne et al. 2006), it does not preclude the useof this technique to compare whole communities. Profilesgenerated from different samples can be compared to assessthe similarity between communities (Whittaker index),allowing spatial or temporal changes to be detected withoutthe necessity of identifying each peak in the profiles(Danovaro et al. 2006; Pringault et al. 2008). This approachhas been largely used to compare the effects of physical andbiotic factors on sediment microbial communities (Bordenave
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et al. 2007; Edlund et al. 2006; Pereira et al. 2006) usingmultivariate statistical analysis (Dollhopf et al. 2001;Pringault et al. 2008).
2.6 Mathematical analysis
The concentration of dissolved metal in the overlying waterof the sediment can be described by the Langmuirs law(Schmitt et al. 2001):
rt k Ct qe qt 1where r represents the adsorption rate, k the constant ofadsorption, q(t) the quantity of metal adsorbed onto thesediment, and qe the quantity of adsorbed metal at theequilibrium. q(t) can be calculated from the mass balanceequation:
qt Co Ct V=m 2where Co represents the initial concentration of metal, C(t)the concentration of metal as a function of time t, V thevolume of the water phase (800 ml), and m the mass of thesediment (80 g). At the end of incubation, the final quantityof adsorbed metal (qf) was calculated and compared withthe estimated qe.
In order to estimate the similarity between two T-RFLPprofiles, the Whittaker similarity index (W) was calculatedusing the following equation:
ai1 ai2j j2
where ai1 and ai2 are the percentage contributions toamplified DNA of the ith T-RF in samples 1 and 2,
respectively. Since this index takes into account T-RFsrelative abundances, it provides a better estimate of thesimilarity between two microbial communities (Hewsonand Fuhrman 2006).
Variations of microbial community structure wereassessed by correspondence analysis (CA) performed onall data from T-RFLP profiles according to the proceduredescribed by Fourans et al. (2006) and Duran et al. (2008).CA was performed with MVSP v3.12d software (KovachComputing Service, Anglesey, Wales). In all calculations,we assumed, as explained by Luna et al. (2004), that thenumber of T-RFs represents the species number and thattheir relative peak height represents the relative abundanceof each component. Relative abundances of T-RFs havebeen transformed with arcsine (x0.5), according to Legendreand Legendre (1998), to get a normal distribution of thedata since it is a condition required before applyingmultivariate statistical analysis (Dollhopf et al. 2001).
3 Results and discussion
3.1 Sediment composition
The three stations had a similar sediment compositionregarding mud content (Table 1). Mud was alwaysdominant and the proportion of mud (fraction 90% in the three stations. Differences wereobserved for the carbonate fraction, with values up to61.4% for...