Experimental Evolution of a Bacteriophage Virus Reveals the Trajectory of Adaptation across a Fecundity/ Longevity Trade-Off
Published October 12, 2012

Richard H Heineman
Section of Integrative Biology
University of Texas at Austin
AustinTexasUnited States of America

Sam P Brown email@example.com
Section of Integrative Biology
University of Texas at Austin
AustinTexasUnited States of America

Centre for Infection, Immunity and Evolution
University of Edinburgh
EdinburghUnited Kingdom

Experimental Evolution of a Bacteriophage Virus Reveals the Trajectory of Adaptation across a Fecundity/ Longevity Trade-Off
Published October 12, 2012Received July 30, 2012; Accepted August 29, 2012;Editor: Michael A. Brockhurst, University of York, United Kingdom Funding: This work was supported by the Human Frontier Science Program (HFSP) grant LT755/2004 (SPB), The Wellcome Trust grant 082273/Z/07/Z (SPB), and the National Institutes of Health (NIH) grant GM 57756 (Jim Bull) provided financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: Sam Paul Brown is a PLOS ONE Editorial Board member. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials. *
Life history theory attempts to account for how organisms lead their lives, balancing the conflicting demands of reproduction and survival. Here, we track the genomic and phenotypic evolution of the bacteriophage virus T7 across a postulated fecundity/longevity constraint. We adapted T7 to a challenging survival environment (6M urea). Our evolved strain displayed a significant improvement in propagule survival, coupled with a significant loss of fecundity (reduced growth rate on host cells). However, the increased resistance to urea did not generalise to increased resistance against temperature stress, highlighting that propagule durability is environment dependent. Previous comparative studies predicted that changes in propagule resistance would be mediated by changes in capsid proteins or gene deletions. In contrast, we found that point mutations in internal core protein genes (6.7 and 16) were responsible for the increased urea resistance of our evolved strain. Prior to the emergence of the 6.7 and 16 mutations, a distinct set of 5-point mutations peaked at over 20% prevalence before attenuating, suggestive of negative epistatic interactions during adaptation. Our results illustrate that parasites can adapt to specific transmission environments, and that this adaptation can impose costs on the subsequent ability to exploit host cells, potentially constraining durable parasites to lower virulence. Citation: Heineman RH, Brown SP (2012) Experimental Evolution of a Bacteriophage Virus Reveals the Trajectory of Adaptation across a Fecundity/Longevity Trade-Off. PLoS ONE 7(10): e46322.

Introduction

Models based on natural selection make a number of simplifying assumptions in order to draw general conclusions. Most generally, they assume certain traits to be constrained via trade-offs while allowing others to evolve freely. In the absence of constraints, such models would predict a world filled with 'Darwinian Demons', organisms that reproduce at a ferocious rate, and yet never die [1]. While it is possible to relax any assumption regarding constraints, in practice this must be done sparingly to preserve the usefulness of the model. Empirical tests are therefore important for identifying which life history trade-offs are most likely to shape evolution.

The principal route to documenting biological constraints is to look for patterns in comparative data -for instance, do species that live longer tend to reproduce more slowly [2]? A recent comparative analysis of bacteriophage virus life histories concluded that this was the case. De Paepe and Taddei [3] reported a 10fold difference in propagule stability (in a constant and benign environment) across 16 bacteriophages (viruses) of E. coli, and further pointed to a significant cost associated with increasing propagule durability. They found that viruses with more durable propagules tended to also be slower to reproduce on E. coli host cells, with typically lower burst sizes (number of viral progeny released on the death of a host cell), highlighting a potential fecundity/longevity trade-off in phages.

Viruses are transmitted from host to host as a metabolically inert propagule (a virion), featuring a protein capsule (capsid) encasing a tightly-coiled genome. Transmission depends on the integrity of a protein casing (the capsid), so denaturants such as high temperatures [4] or urea [5] greatly accelerate viral decay. However, it is not well known how evolutionary adaptation to resist one of these selective pressures generalizes even to other relatively similar denaturants.

Durability as a life history trait may be even more complicated by nonlinear phage decay. In animal viruses [4] and phages [6], viral decay in response to heat follows a 2-component inactivation curve, with rapid early decay followed by slower decay of an isogenic thermally resistant population. This subpopulation might have a major effect on the epidemiology and selective pressures acting on viruses under harsh conditions. How general is this nonlinearity across other environmental stresses? Does this alternative phenotype persist after selection for increased durability? Is the adapted phage phenotypically similar to the original resistant population, which might suggest that adaptation largely involved more phages taking on the resistant form?

To answer these questions and test the hypothesized trade-off between durability and growth rate experimentally, we exposed the dsDNA lytic bacteriophage T7 to a stressful environment of 6 M urea. Gupta et al. [5] demonstrated that survival of T7 in 6 M urea is readily improvable via selection. However, they did not explore the possible trade-off between propagule durability and growth rate or explore the genetic basis of adaptation at the population level. Here, we measure correlated changes in other aspects of pathogen life history and examine the genetic basis of evolution. Finally, we discuss the epidemiological and life-history implications of a trade-off between propagule durability and the rate of host exploitation.

Methods

Selection for resistance to environmental stress

Our experimental protocol centred on a modified batch-culture protocol, with T7 grown on high density E. coli IJ1133 [E. coli K-12, F-DlacX74 thi D(mcrC-mrr)102::Tn10], which was also used for all assays. Our ancestral T7 line, T7 A , was pre-adapted to the host strain and the high-density growth conditions (T7 Hi from [7]). During adaptation to urea, the passaging was modified to alternate growth in high density E. coli with a survival challenge in 6 M urea. In the growth phase, E. coli was grown from a frozen stock (20% glycerol/80% LB, stored at 280uC) to a concentration of 1-2610 8 cells/ml in 10 ml of LB (37uC, 200 RPM in shaking water bath). Between 500 and 10 5 phages were added (roughly K the phages that survived the previous urea treatment) and these phages were allowed to replicate until the cell culture had entirely lysed (less than 1 hr), at which point the lysate was chloroformed to kill cells, spun down and filtered (0.2 mm) to remove host cells and cellular debris, and stored on ice. The initial lysate was mutagenised with N-methyl-N'-nitro-N-nitrosoguanidine at 0.5 mg/mL to generate diversity.

The phage culture was then subjected to survival selection. 200 ul of phage in LB was added to 1800 ul of 10 mM Tris-HCl (pH 7.5 at 22uC in a 200 RPM water bath) to a final concentration of 6 M urea. Samples were taken during urea exposure at regular intervals, diluted and stored on ice before plating and plaque counting. As propagule durability improved during the adaptation process, the period of exposure to urea increased from 4-5 min to 20-24 min. Dialysis in 10 mM Tris-HCl (pH 7.5) was used to remove urea before starting next growth phase. After 11 rounds of urea exposure and regrowth, the adaptation resulted in T7 E .

Life-history measurements

Both ancestral and evolved strains were assayed for growth rate on E. coli and survival over 15 minutes in 6 M urea. Growth rate was measured under conditions similar to those used in the growth phase of adaptation except that phages were not allowed to grow beyond 10 7 phage/ml (see [7]). Growth is measured in doublings/ hour, calculated as [log 2 (N t /N 0 )]/t, where N t is the number of phages in the flask at time t hours.

Survival in urea was determined by linear regression of logtransformed titers measured at 1,2,3,4, and 5 minutes of exposure, and expressed as halvings/min (assuming a first order inactivation process). Exponential decay was approximately constant over this time, while longer exposure to urea resulted in significantly slower decay rate. For some assays of T7 A and T7 E , survival was also measured from 10 to 15 minutes. All urea survival assays were done at 24uC, but conditions were otherwise identical to those used during adaptation. Survival (or inactivation) was also assayed for filtered cultures in LB without urea under a range of temperature regimes (37uC over 50 days and 63uC over 5 hours). In order to ensure independence of data points and avoid contamination, each time point in the heat assays was sampled from a different tube.

Sequencing

In order to characterize the genetic changes underlying the period of adaptation to environmental urea, T7 E was fully sequenced, as were all sites in the recombinant phage (see below) that differed between evolved and ancestral strains. This sequencing was performed from PCR products using an ABI3100 analyzer and DNA Star Lasergene Seqman software (version 5.05). We also sequenced the entire genome of T7 at three points along the adaptation using 454 pyrosequencing to reveal polymorphism. Genomic DNA was isolated from lysates grown from the growth phase of passages 3, 8 and 12. Indels and sequence changes present at less than 2.5% were ignored to prevent false positives for mutations. Wild-type T7 (GenBank accession number V01146, [8]) was used as a reference sequence to define the locations of changes.

Recombinant strain engineering

In order to identify the phenotypic impact of mutations, T7 A and T7 E were cut with restriction enzymes and re-ligated [9] to generate T7 +g6.7+g16 , a genome with only the coding changes (in genes 6.7 and 16) that spread during adaptation. These were then transfected into competent IJ1126 cells [E. coli K-12, F-recC22 sbcA5 endA Gal-thi supL D(mcrC-mrr)102:Tn10].

Results

T7 was adapted under conditions selecting for both survival and growth rate. The phenotypic correlates of this adaptation were assayed, and the genetic basis of survival identified.

Evolution of urea durability and associated reproductive cost

T7 rapidly adapts to a selective challenge from environmental urea. The original phage (T7 A ) has an initial decay rate of 2.35 halvings/min, while after only 11 passages through urea, the evolved phage (T7 E ) decays at 1.38 halvings/min ( Figure 1, Table 1; 2-tailed t-test, p,0.0006). In addition to the selected increase in propagule durability, we also found a correlated response in growth rate of the phage on high density E. coli, with mean growth rate decreasing during experimental evolution from a growth rate of .80 doubling/min to a growth rate of 0.77 db/min ( Figure 1, Table 1; 2-tailed t-test, p,0.05), despite continued selection for growth. Our experimental results therefore reveal a trade-off; the evolved strain is more durable in urea, yet has a reduced growth rate.

Survival in response to other environmental conditions

We then measured the rate of decay of our evolved and ancestral phage lines under distinct environmental challenges, to examine whether resistance to urea generalises to resistance to other environmental stressors. Long-term measurements of decay in relatively benign conditions (no urea, 37uC) showed no significant difference in the rates of decay of evolved and ancestral phages ( Table 1, 2-tailed t-test, p,0.61). The phages also do not decay differently under conditions of significant thermal stress (63uC, Table 1, ANOVA, p,0.19).

We also attempted to adapt T7 A to hot (63uC) water. Despite imposing 99.99% mortality over 3 overnight passages, we found no apparent response to selection (data not shown).

Variable rates of decay

For both T7 A and T7 E , the constant decay model provided an excellent fit over the first 5 minutes, with R 2 values largely around 0.98-0.99 and never lower than 0.92. However, decay rate was significantly slower between minutes 10-15 (T7 A from 2.35 to 0.17 halvings/min, p,0.004; T7 E from 1.38 to 0.70 halvings/min, p,0.006). Similarly, in the 63uC experiments, we found that for both T7 A and T7 E the phages appear to die faster within the first hour than they do from 1.5 to 5 hours (ANOVA, p,0.0004). There was no such pattern observed following exposure to 37uC. As decay assays used lysates grown from isolates, it is unlikely that this consistent nonlinearity is a result of genetic variance in the population.

Genetic basis of a trade-off

Only 5 genetic changes spread during adaptation, all of which were point mutations and only two of which (in genes 6.7 and 16) were coding (Table 2). In order to confirm that only coding changes were responsible for survival evolution, we created a recombinant strain with only coding changes and assayed for survival in urea. The resultant phage (T7 +g6.7+g16 ) had a death rate of 1.51 halvings/min, greatly decreased from that of T7 A (2.35 halvings/min, p,0.004 by 2-tailed t-test) and similar to that of T7 E (1.38 halvings/min, p,0.29 by 2-tailed t-test). This suggests that the increased durability of the evolved strain is largely due to coding changes ( Figure 2).

Polymorphisms in an adapting population

There were a number of other changes present in the adapting population, suggesting a more complicated route to adaptation than might be surmised by the small number of mutations required for the final trade-off. Most interestingly, a number of mutations, including a coding change in gene 13, had spread to relatively high frequency (greater than 20%) by the end of the third growth period but were then lost entirely ( Figure 3, Table 2). The mutations responsible for the trade-off in the final population were present only at very low rates at this point. After this initial burst of changes, most mutations not found in T7 E were present only at low frequency, with the exception of a change in gene 1.6. No mutations were fixed over the course of adaptation, despite enormous increases in virion survivability during adaptation.

Discussion

Propagule trade-offs and comparative data

Our results in urea experimentally confirm the existence of a phenotypic trade-off between viral survival and reproduction ( Figure 1), that had previously been inferred through a comparative analysis of the life-history characteristics of 16 coliphages [3]. However, our experimentally observed trade-off is far less constraining than that inferred from the comparative data. Taking only the 12 lytic double-stranded DNA viruses from De Paepe & Taddei's [3] dataset, we can infer that a doubling of propagule survival can be bought for an approximate halving of viral growth rate (growth rate re-calculated as Ln(burst size)/latency period). In contrast, our evolved doubling of propagule durability in T7 was bought for only a ,3% reduction in growth rate ( Figure 1); why the discrepancy in the magnitude of the trade-off?

The use of comparative data to determine life-history trade-offs is a valuable first step to identifying evolutionary constraints, but it is difficult to know the cause of the correlation when we do not know the selective pressures organisms have faced [2]. Our results illustrate that the T7 virus can readily adapt to improve propagule survival against at least some environmental stressors, but suggest that different environmental challenges may entail very different trade-off landscapes. Thus the relatively weak trade-off that we observed between fecundity and longevity in urea may be stronger in other environments. When we measured durability under conditions more closely resembling those used in de Paepe and Taddei [3], we found that the evolved and ancestral strain had little difference in durability, in keeping with the minimal change in growth rate ( Table 1). The relative cheapness of adaptation to survive in urea may not apply to heat resistance, consistent with our inability to register any immediate response to selection on heat resistance.

Even more contradictory comparative results on propagule durability and longevity were reported by [10], who observed a positive correlation between virulence and propagule durability across a comparative survey of viral and bacterial pathogens of humans. One possible explanation for this discrepancy lies in the  Evolution across a Trade-Off PLOS ONE | www.plosone.org evolution in certain species of a discrete spore forming environmental dispersal stage, distinct from the within-host propagule responsible for cell-to-cell dispersal. By elaborating distinct dispersal stages to face more perilous environmental challenges, the trade-off between environmental survival and within-host (or patch) proliferation may be effectively decoupled. A further and more general explanation for conflicting comparative results is that the ranking of 'propagule quality' or durability is likely to be highly sensitive to the environmental assay used to measure durability, as highlighted by our measurements in heat and urea. These results imply that propagule survival (like pathogen growth, and consequent virulence) is a complex and multi-dimensional trait, which cannot be readily reduced to simple two-dimensional tradeoffs. To begin to unravel the complexities of survival, we now turn to the molecular mechanisms that caused the phenotypic shift towards greater resistance to urea.

Mechanisms of a trade-off

How has the virus changed to become more resistant to urea, and yet slower growing on E. coli? De Paepe and Taddei's [3] comparative analysis identified two key statistical predictors of a virus's position along a fecundity/longevity tradeoff: the density of DNA packaging and the volume of the capsid. Small capsids with a high density of DNA were associated with the production of many fragile propagules (e.g. R17, T7), whereas big, loosely packed capsids were more costly yet tougher (e.g. PRD1, P4). This suggested that T7 adaptation to urea would involve changes in gene 10 (coding for the capsid protein), or release of packaging pressure via large deletion mutations. In line with these predictions, an earlier adaptation of T7 to survive in urea resulted in a large deletion [5]. Interestingly, segmented genomes in an RNA virus also survived better than unsegmented ones, suggesting a wide range of possibilities for decreasing pressure from genetic material, although no tradeoff was found in this case [11].

There were no deletions in our adaptation, however. This lack of deletions may be because our ancestral strain had already undergone a deletion mutation during an earlier adaptation to lab passaging conditions [9]. This adaptation involved minimal selection for durability, and a similar deletion shortens the generation time of T7 under some conditions (unpublished data), suggesting the primary benefit of the change is in infection. However, this does not rule out a pleiotropic effect on survival, potentially boosting the durability of our ancestral T7 A strain relative to wild type T7.

While the capsid protein was predicted to evolve in response to harsh conditions, phenotypic evolution was instead driven by changes to other virion proteins. Gene 16 encodes an internal core protein involved in genome entry [12]. The change was in the muralytic domain [13], which degrades the cell wall at initiation of infection [14], and which frequently evolves during selection for rapid growth rate [9]. In fact, T7 A carries 3 changes in this region. It is possible that the new change compensates for a stability cost of these earlier changes [15]. Gp6.7 is an internal head protein that may act as a plug to stabilize the genome inside the capsid [16].

Thus the gp6.7 change is likely to prevent genome destabilization or even release of the genome from the virion upon exposure to urea, suggesting that the mechanism by which urea kills T7 is in large part genome damage. Gp6.7 is involved in morphogenesis [16]; a change that provides greater stability may also slow virion production.

Urea, like heat, is a protein denaturant [17], and for this reason we suspected that correlated evolution was likely. The independence of survival in these two environments emphasizes that the precise nature of environmental pressures affect how phages will respond on a molecular level.

Complexity of adaptation

The temporal dynamics of population genetic structure reveals a distinct pattern of two sequential waves of mutant expansion/ contraction ( Figure 3, Table 2). The first wave is characterised by 5 point mutations that reach or exceed 20% frequency at passage 3, before receding. The second, rising wave is characterised by many of the mutations featured in T7 E and in particular, the functionally significant changes to genes 16 and 6.7. Clonal interference is unlikely to completely explain this pattern, given that each growth phase of the adaptation ended with very high multiplicity of infection, and thus ample potential for recombination of beneficial alleles. Besides, while hitchhiking may have caused the spread of some of the mutations, it is unlikely to have been a general driver of the first wave, as all 5 mutants subsequently decreased in frequency. While some of the changes may have increased in frequency due to bottlenecking during death steps, bottlenecking to 500+ phages is unlikely to have resulted in the spread of so many neutral mutants to such high frequency. We suggest that the sequential pattern is most likely the result of negative epistasis, with the first wave of mutations representing an alternative, inferior pathway to resistance, antagonistic to the second wave.

The lack of fixation of any allele throughout the course of adaptation ( Figure 3, Table 2), despite dramatic shifts in phenotype, is surprising. Very many viruses were eliminated during each survival challenge, which should lead to strong selection for even small survival differences. However, this was mitigated by the trade-off with growth rate and the large impact of non-genomic factors on survival, which may frequently be the case with life history traits in nature. It is, of course, also possible and even likely that more time could have resulted in fixation of the changes leading to resistance.

Propagule survival as a complex and multi-dimensional trait

In addition to a strong environmental sensitivity in both resistance and the evolvability of resistance, we also find temporal variation in decay rates, with decay rate slowing during continued exposure. While some recent studies have assumed that the process of phage inactivation follows a first order kinetic [3,5], other studies have found similar nonlinear effects in response to degradation at high temperature [4,6]. We demonstrate that this effect, unlike the evolution of resistance, is conserved between high temperature and urea environmental pressures, although in accordance with other studies linearity was restored at lower temperatures [4]. The non-constancy of phage decay in time as well as across environments points to the coexistence of multiple pathways of degradation (e.g. protein denaturation, nucleotide cleavage) and multiple and interchangeable phenotypic states (e.g. extent of nucleotide hydrogenation or binding to metal ions) characterised by different resistances, as inferred for several viruses [4][5][6]. For example, the bacteriophage WX174 exists in two Ancestral clone T7 A is the reference strain; evolved clone T7 E was sampled from the population at transfer number 12. Functionally significant mutations to genes 6.7 and 16 are highlighted. Full data presented in Table 2. doi:10.1371/journal.pone.0046322.g003 phenotypic forms; one is more thermostable but attaches less well [6]. It is possible that in some viruses phenotypic variation represents a viral bet hedging strategy; a sub-set of genetically identical but more resistant propagules will hedge against the arrival of harsher conditions, much as some seeds that germinate late may increase the multiplicative fitness of a plant by protecting against a bad year [18]. In the case of WX174, on the other hand, individual virions respond to harsh conditions by undergoing conformational changes that grant them increased resistance [6].

If an analogous phenotypic plasticity applies to T7, one obvious evolutionary response to selection on durability would be for the phage to produce only more stable forms, or transform to them under even mild conditions. Under our adaptive conditions, these resistant viruses, genetically identical to the entire viral population, made up the vast majority of the viruses transmitted to the next generation. Thus, by far the most important targets of selection were for increased likelihood of entering this subpopulation or for survival rate once in it.

Interestingly, the nongenetic variation persists after selection. In fact, this variation is larger than the difference between evolved and ancestral phages; the ancestral virus decayed more slowly from 10-15 min than the evolved did from 1-5 min ( Figure 1, Table 1). The persistence of nonlinear decay suggests that it might be a constraint on the system. Even a small proportion of phages with a higher survival rate would be of disproportionate importance to determining the selection pressures exerted by harsh environments. Given the seemingly chaotic environment that phages are faced with in nature, these ''persistor'' individuals may greatly affect microbial population dynamics, and persistors in animal viruses may be epidemiologically important, playing a role analogous to bacterial spores.

Within this complex adaptive landscape, our results illustrate that the T7 virus can readily adapt to improve propagule survival against at least some environmental stressors, but that this resistance cannot be generalised to other environmental challenges. The differences between survival in urea and at high temperature suggests that phages, and viruses in general, may face a wide range of survival-based selective pressures, some of which are easily responded to and others which are not. In this way survival may be as complicated as growth rate, which is also subject to diverse and contrasting selective pressures in different environments [7,[19][20][21][22][23][24][25]. Understanding how and when parasites can adapt to environmental challenges via changes in propagule design will help broaden our understanding of pathogen ecology, evolution and control.

Parasite life-history constraints and implications for virulence

The implications of a trade-off between viral fecundity and longevity are far-reaching. If similar results hold for animal viruses, a number of medical implications can be considered. First, on a within-host scale, our results imply that viruses can potentially adapt to certain extra-cellular control agents (i.e. components of an immune response, or extra-cellular anti-viral drugs), and furthermore that this adaptation may yield a reduction in the growth rate of the virus on cells in the absence of the control agent -a cost of resistance mediated by propagule design. A key difference from our experimental framework is that the growth environment (within cells) and the survival challenge (among cells) are found within the same patch or host, thus the virus within a multi-cellular host must face a significant extra-cellular survival challenge after every round of intra-cellular replication (as opposed to after several rounds of replication in favourable within-patch conditions, as in our experimental protocol). Brown et al. [26] theoretically explored resistance evolution (in an intra-cellular bacterial pathogen) to extra-cellular drug control, and predicted an evolutionary shift towards prolonged intra-cellular growth and consequently a reduced overall growth rate, as part of a 'refuge' strategy, guarding against frequent extra-cellular exposure. Our results lend further strength to the prediction of longer and slower intra-cellular growth as a response to extra-cellular control, as a result of both a 'refuge' strategy [26] and as a result of a mechanistic constraint of adaptation to enhance inter-cellular propagule survival.

Moving to an epidemiological scale, we can now consider the impact of pathogen adaptation to external transmission environments, i.e. the evolution of indirect transmission. Our model system was a bacteriophage virus, but we can again extrapolate to animal viruses if we consider the high-density E. coli flask to represent a host (or more generally, a resource patch) and the urea flask to represent the transmission environment. Our results therefore point to a trade-off between propagule survival and the within-host growth rate of the pathogen, which can often be related to virulence (a positive link between within-host growth and host mortality has been observed across a range of pathogens, including viruses [27], bacteria [28] and protozoa [29], and is a basic assumption of many theoretical models of virulence evolution (e.g. [30])). Our results therefore lead to a reversal of the curse of the pharaoh hypothesis (the hypothesis that parasites with long-lived propagules will be highly virulent, [31][32][33][34][35][36][37]); we predict that by adapting to survival in extreme transmission environments, pathogens may instead become less able to rapidly -i.e. virulently -exploit their hosts.

Figure 1 .
1Representative decay curves in 6 M urea for ancestral phage T7 A and evolved phage T7 E . doi:10.1371/journal.pone.0046322.g001

Figure 3 .
3Mutation prevalence over genomic evolution.

Table 1 .
1Phenotypic traits of phage lines with standard errors (computed from observations) and number of assays.T7 A
T7 E
T7 +g6.7+g16

Fitness (db/min)
0.8
6
0.0051 (4)
0.77
6
0.0086 (5)

Halvings/min (urea, 1-5 min)
2.35
6
0.088 (3)
1.38
6
0.092 (4)
1.51
6
0.055 (4)

Halvings/min (urea, 10-15 min) 0.17
6
0.11 (2)
0.7
6
0.03 (3)

Halvings/min (63uC, 0-1 hr)
0.13
6
0.007 (2)
0.14
6
0.008 (2)

Halvings/min (63uC, 1.5-5 hr)
0.047
6
0.0001 (2)
0.056
6
0.0098 (2)

Halvings/day (37uC)
0.43
6
0.003 (2)
0.41
6
0.03 (2)

doi:10.1371/journal.pone.0046322.t001

Table 2 .
2Genetic changes during adaptation, relative to wild-type T7.Nucleotide
Gene
Change
Gene function
T7 A
T7 E
G3
G8
G12

1257-2736
0.3-0.7 fusion,
Deletion
Various a
+
+
+
+
+

0.4-0.6 deletion

15094
5
G-.T A248S
DNA Polymerase
+
+
0.745
0.928
0.925

24088
10B
G-.A E375K
Minor coat protein
+
+
+
+
+

30861
16
A-.C Q89H
Internal core protein
+
+
+
+
+

30945
16
T-.G N117K
Internal core protein
+
+
+
+
+

32860
16
G-.A G756S
Internal core protein
+
+
+
+
+

34975
17
A-.G T118A
Tail fiber
+
+
+
+
+

36492
17.5
T-.G I50S
Suspected holin
+
+
+
+
+

189
Terminal repeat
C-.T
2
2
0.051
0.028

3456
1
C-.T R96C
RNA polymerase
2
2
2
0.029

4886
1
G-.A G572G
RNA polymerase
2
2
2
0.037

7435
1.3
C-.T P321S
DNA ligase
2
2
0.107
0.054

8108
1.6
C-.T A68V
Non-essential, unknown
2
2
0.545
0.484

11152
3.5
C-.T D149D
Lysozyme, regulation
2
2
0.123
0.085

12565
4A
G-.A G334D
Helicase
2
2
2
0.026

19032
6.7
G-.A E57K
Adsorption, virion head
protein

+
2
0.85
0.955

19923
7.7
G-.A V26M
Non-essential, unknown
2
2
2
0.025

20240
7.7
A-.G (ochre to amber) Non-essential, unknown
2
0.288
0.047
0.058

21027
8
C-.T S263F
Head-tail connector
2
2
2
0.036

24537
11
T-.C Y104H
Tail protein
2
0.047
2
2

27135
12
C-.T T765I
Tail protein
2
2
0.034
0.039

27574
13
A-.G M90V
Internal head protein
2
0.204
0.045
0.081

29770
15
G-.A K482K
Internal core protein
2
0.257
0.058
0.05

30719
16
C-.A P42H
Internal core protein
+
0.029
0.693
0.705

35083 a
17
C-.T L154L
Tail fiber
+
0.228
0.092
0.138

35385 a
17
G-.A Q254Q
Tail fiber
+
2
0.036
0.043

36093
17
C-.T L490L
Tail fiber
2
2
0.039
2

39566 a
After 19.5
C-.A
+
0.306
0.076
0.056

a

indicates noncoding change.
+indicates mutation present at greater than 97.5%.
2indicates mutation present at lower than 2.5%.
doi:10.1371/journal.pone.0046322.t002

Figure 2. Survival of ancestral, evolved, and recombinant
phages in 6 M urea. Error bars indicate ±1 standard error.
doi:10.1371/journal.pone.0046322.g002

Evolution across a Trade-Off

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AcknowledgmentsWe thank Jim Bull for hosting, encouraging and troubleshooting this work; Sinead Collins and Nick Colegrave for discussion.Author ContributionsConceived and designed the experiments: SPB RHH. Performed the experiments: SPB RHH. Analyzed the data: SPB RHH. Contributed reagents/materials/analysis tools: SPB RHH. Wrote the paper: SPB RHH.
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The Interaction of CuS and Halothiobacillus HT1 Biofilm in Microscale Using Synchrotron Radiation-Based Techniques
2013

Huirong Lin
Department of Environmental Engineering
Zhejiang University
310058HangzhouChina

Institute of Urban Environment
Chinese Academy of Science
361021XiamenChina

Guangcun Chen
Department of Environmental Engineering
Zhejiang University
310058HangzhouChina

Shenhai Zhu email@example.com.
Department of Environmental Engineering
Zhejiang University
310058HangzhouChina

Yingxu Chen email@example.com.
Dongliang Chen
Department of Environmental Engineering
Zhejiang University
310058HangzhouChina

Institute of High Energy Physics
Synchrotron Radiation Facility
Chinese Academy of Science
100049Beijing, BeijingChina

Wei Xu
Institute of High Energy Physics
Synchrotron Radiation Facility
Chinese Academy of Science
100049Beijing, BeijingChina

Xiaohan Yu email@example.com
Institute of Applied Physics, Chinese Academy of Science, Shanghai Synchrotron Radiation Facility
201800ShanghaiChina

Jiyan Shi firstname.lastname@example.com
Department of Environmental Engineering
Zhejiang University
310058HangzhouChina

The Interaction of CuS and Halothiobacillus HT1 Biofilm in Microscale Using Synchrotron Radiation-Based Techniques

Int. J. Mol. Sci
14201310.3390/ijms140611113Received: 21 March 2013; in revised form: 2 May 2013 / Accepted: 16 May 2013 /International Journal of Molecular Sciences * Author to whom correspondence should be addressed; E-Mail: OPEN ACCESS Int. J. Mol. Sci. 2013, 14 11114Halothiobacillus HT1 biofilmdistributionspeciationcoppersulfur
In order to investigate the microbe-mineral interaction in the micro scale, spatial distribution and speciation of Cu and S in Halothiobacillus HT1 biofilm formed on a CuS surface was examined using synchrotron-based X-ray techniques. Confocal laser scanning microscope (CLSM) results indicated that Halothiobacillus HT1 biofilm formation gave rise to distinct chemical and redox gradients, leading to diverse niches in the biofilm. Live cells were distributed at the air-biofilm and membrane-biofilm interface. CuS was oxidized by Halothiobacillus HT1 biofilm, and copper penetrated into the biofilm. Sulfide was oxidized to cysteine (77.3%), sulfite (3.8%) and sulfonate (18.9%). Cu-cysteine-like species were involved in the copper homeostasis. These results significantly improve our understanding of the interfacial properties of the biofilm-mineral interface.

Introduction

Microbes cycle metals through biogeochemical processes, including accumulation, transformation and biomineralization, thus leading to changes in toxicity and bioavailability [1,2]. Sulfide oxidation by microbes is used for the biological production of metals, such as copper, gold and zinc. Sulfur oxidizing bacteria contribute strongly to the biological oxidation of metal sulfide [3,4]. However, contrary to their significant role in the global sulfur cycle and the biotechnological importance, the microbial fundamentals of sulfur oxidation are incompletely understood, due to the complexity of this reaction.

The microbe-mineral interface serves as a solid phase source of electrons, and biogeochemistry of the microbe-mineral interactions have been paid great attention to during the past few decades [5][6][7]. Many of the critical processes occur at the biofilm-mineral interface on the molecular scale. Therefore, attention should be focused on microenvironments, where chemical transformations occur. A better understanding of the interfacial properties of the biofilms-mineral interface, especially the interfacial chemical processes at the micron and nanometer levels, is needed [6,8].

Microbes usually operate as consortia of organisms rather than as single cells. Biofilms are physiologically distinct from bacteria growing in a free-swimming planktonic state and present genetic and physiological heterogeneity [9][10][11]. Growth of biofilm can enhance resistance to metal toxicity. Previous report showed that the ability of biofilms to survive heavy metals stress is better than planktonic microbes [12]. The metabolic activity, microenvironment characteristics and microbial community composition of biofilm are involved in resistance to metal toxicity. Biofilms can sorb metals and retard metal diffusion, leading to protection in the interior of the biofilms [13]. The genetic basis for metal resistance in sulfur oxidizing bacteria has been studied by several investigators [14]. Basic understanding of environmental materials and processes at the molecular scale is essential for risk assessment and management and reduction of environmental pollutants. Therefore, the description of the speciation and distribution of metals in biofilms is critically important for modeling and understanding the detoxification mechanism.

The aim of this study was to investigate the interaction in sulfur oxidizing bacteria biofilm-metal sulfide at the micro scale. A heavy-metals-tolerant Halothiobacillus HT1 was chosen [15]. The interaction in the CuS and biofilm of the Halothiobacillus interface was studied. The spatial distribution and speciation of copper in Halothiobacillus HT1 biofilm formed on CuS was determined using synchrotron-based X-ray fluorescence microscopy (XRF) and micro-X-ray absorption near edge structure (micro-XANES) analysis. Cell viability was detected using live-dead staining. Sulfur speciation was measured using sulfur K-edge XANES.

Results

Spatial Distribution of Live and Dead Halothiobacillus HT1 Cells in the Biofilms

Halothiobacillus HT1 is a heavy-metals-tolerant sulfur oxidizing bacterium, which belongs to Gammaproteobacteria, Halothiobacillus (GenBank accession number GU013549). In order to analyze the interaction of Halothiobacillus HT1 biofilm and CuS, the spatial distribution of live and dead cells in Halothiobacillus HT1 biofilm sections was studied using Live/Dead staining combined with CLSM imaging. As shown in Figure 1, after 72 h cultivation, the HT1 cells presented different distributions in the biofilm formed on CuS. At the air-biofilm interface and membrane-biofilm interface, the CLSM imaging results showed green, while in the middle, the results showed red. These results indicated that there were more live cells at the air-biofilm interface and membrane-biofilm interface than in the middle.

Spatial Distributions of Cu in Biofilm and Cu Speciation

XRF is considered to be a powerful tool for quantitative mapping of trace element distributions [16]. It can visualize the metal ion distribution in tissues or cells. The colony biofilms were thicker in the center and thinner at the edges. The thickness of the biofilm grown on CuS was about 150 μm, while the control was about 100 μm. Figure 2 showed the Halothiobacillus HT1 biofilms section, the scanning area of XRF and the distribution characters of the elements in the scanning area. The elements were not evenly distributed in the biofilms. There was a Cu accumulation layer in the middle of the Halothiobacillus HT1 biofilm in the presence of CuS, suggesting Cu penetrated about 100-150 μm from the membrane interface.

Compared with bulk XAFS, micro-XAFS could analyze the speciation of elements in small ozone. The Cu K-edge micro-XANES spectra were conducted to determine the speciation of Cu in the center and at the edge of the biofilm formed on CuS. Linear combination arithmetic was used to determine the probable Cu speciation in the sample as analyzed by the LSFitXAFS program. There were some differences in the XANES spectra of the center and the edge, as shown in Figure 3. As shown in Table 1, species resembling Cu-cysteine was the major species both in the center (67.8%) and at the edge (48.4%). In the center of the biofilm reacted with CuS, Cu-alginate-and Cu-citrate-like species accounted for 21.4% and 10.8%, respectively. While at the edge, Cu-histidine-(22.3%) and CuS (19.3%)-like species accounted for minor proportions, with Cu-alginate-and Cu-citrate-like species in smaller proportions (2.9% and 7.1%, respectively).   Table 1. The results of fitting the micro-XANES spectra with a linear combination of the measured data sets of representative model compounds. Copper oxidation states were assigned according to the similarity of the absorption edge energies of reference compounds. Rss: residual sum of squares.

Samples

Cu-cysteine (%) Cu-alginate (%) Cu-citrate (%) Cu-histidine (%) CuO (%) Rss

Sulfur Speciation in the Biofilms

As an essential macronutrient for microorganisms, plants and animals, sulfur (S) exists in a variety of organic and inorganic species, with oxidation states ranging from −2 to +6. The spectra of reference compounds and the samples are shown in Figure 4. Different reference compounds presented different structures of S. For example, cysteine, reduced glutathione presented R-SH, while cystine, oxidized glutathione presented R-S-S-R. There were multiple oxidations of sulfur in biofilm formed on CuS. As shown in Table 2, species resembling cysteine (R-SH) were the major sulfur species in the biofilm (77.3%), with species resembling sulfite (SO 3

Discussion

The Relationship of Cu Distribution and Live/Dead Cells in Biofilms

Using CLSM, two active zones were found in the biofilm of HT1: the air-biofilm interface and the membrane-biofilm interface. Biofilm formation seemed to give rise to distinct chemical and redox gradients. The different distributions of the cells indicated that oxygen played an important role in the distribution of live cells [17]. At the air-biofilm interface, oxygen was enough for the growth of aerobes. As a result, Halothiobacillus HT1 cells in this area were active, leading to the larger quantities of live cells than in the middle. While at the membrane-biofilm interface, where the oxygen density was relatively low, more live cells were detected than in the middle. The reason might be attributed to the fact that this area played an important role in the transport of nutrition. Therefore, the cells were activated at the medium-biofilm interface. Biofilm growth of pathogenic bacteria might influence the rate of carbon and inorganic nutrient cycling [18]. The XRF results showed nonuniform distributions of the elements (Figure 4). Our results supported the previous reports that there were diverse niches in biofilms, and biofilms were physiologically distinct from bacteria in planktonic form [19][20][21]. The presence of gradients of dissolved gases, nutrients and solute concentrations might be responsible for the distribution of live and dead cells of Halothiobacillus HT1.

In addition, a better understanding of the distribution and mass transfer process within the biofilm is important for understanding their influences on biogeochemical transformation of metals. Metal might deposit as metallic oxide, phosphate, sulfate or carbonate at certain area, due to the heterogeneous distribution of pH, Eh and O 2 . As a micro-analytical technique for the quantitative mapping of elemental distributions, XRF is compatible with fully hydrated biological samples [22,23]. In this study, XRF is used to detect the distribution of Cu in biofilm of Halothiobacillus HT1 formed on CuS. Our results indicated that CuS can be oxidized by Halothiobacillus HT1, as oxidized sulfur (sulfite and sulfonate) was detected using sulfur K-edge XANES analysis ( Table 2). As a result, ionic copper was obtained and penetrated into the Halothiobacillus HT1 biofilm. As shown in Figure 2, copper accumulated in the layer near the membrane-biofilm interface. This result indicated that the CuS and Halothiobacillus HT1 biofilm reacted actively. This might be another reason for the larger quantity of live cells in membrane-biofilm interface than in the middle. The heterogeneous distribution of Cu found in this study was similar with the result of Templeton conducted on the distribution of Pb at biofilm-metal oxide interfaces [8]. Another study also showed that Zn equally distributed in the 12 μm surface layer of the 35 μm E. coli biofilm [24]. All these results confirmed the heterogeneous distribution of metal in microbe biofilms. Synchrotron-based XRF and micro-XANES spectra were successfully applied to investigate the spatial distribution of elements within HT1 biofilms and the speciation of Cu. Our results indicated that there was spatial heterogeneity vertically ( Figure 2) and horizontally ( Figure 3 and Table 1) within the biofilms

Effects of Halothiobacillus HT1 Biofilms on CuS Transformation

Some specific properties of biofilms can be related to the heterogeneity of their structure and composition. In this study, biofilms formed on CuS were about 150 μm thick, while the control was only about 100 μm, indicating that HT1 might react with the CuS, since CuS provided substrate for it. To further analyze how microbes influence the fate of metal sulfide, speciation of Cu and S was studied. Using sulfur K-edge XANES, sulfur transformation in the biofilms was found. As shown in Figure 4, sulfur was oxidized to cysteine, sulfite and sulfonate in the biofilms formed on CuS. This might be attributed to the reaction of CuS and Halothiobacillus HT1. Sulfur oxidizing bacteria are considered to be capable of oxidizing mineral sulfide to sulfate. In this study, oxidized sulfur detected in the biofilm indicated that CuS was oxidized biologically in the presence of Halothiobacillus HT1 biofilms. However, S was found to exist as diverse molecular species (cysteine, sulfite and sulfonate), which might be the intermediate products of sulfide oxidization, but not the end product (sulfate). The presence of cysteine, sulfite and sulfonate in the biofilm indicated heterogeneity in the redox state and implied that intermediate sulfur compounds were available for metabolism by Halothiobacillus HT1.

Intimate organic-mineral associations are often the result of bacterial activity. Attached bacteria and adsorbed organic matter may interact with sorption processes on metal sulfide surface by changing the characteristics of the electrical double layer at the solid-solution interface, blocking surface sites or providing a variety of new sites for metal binding. Extracellular polymeric substances (EPS) were considered to enclose biofilms and contain many active groups. Micro-XANES is an element-specific spectroscopic tool to identify the local environment of a target atom within a sample. Therefore, we also studied the speciation of copper in the biofilm formed on CuS by using micro-XANES. There were several active groups, such as hydroxyl (-OH), sulfhydryl (-SH) and amino (-NH 2 ), in the biofilms. The Cu-cysteine analog was the major Cu species. In correspondence, sulfur K-edge XANES results also confirmed that cysteine was the main sulfur species.

In order to survive heavy metals stress, microbes usually apply different mechanisms, such as biosorption, precipitation and complexation with sulfides, phosphates or carbonates. Active efflux or detoxification of metal ions by different transformations was found to be involved in many metal resistance systems [25]. For some bacteria, it was believed that inorganic polyphosphates and transport of metal phosphate complexes were involved in heavy metal tolerance [26][27][28]. Cu homeostasis proteins were also found in previous study [29]. In this study, cysteine analogs likely played an important role in the penetration of copper into the biofilm. The high percentage of Cu-cysteine-like species in the biofilm confirmed that the Cu bond to cysteine groups could be defenses against toxic copper. Complexation with -SH groups might contribute to the detoxification of Cu since -SH-containing groups were considered to be bioactive molecules, which can maintain Cu homeostasis through cation binding. These results were consistent with some previous reports concerning other bacteria, which showed that cellular proteins rich in thiol groups participated in the copper homeostasis [30].

At the biofilm-mineral interface, many critical processes occur. Our study attempted to study the interaction of CuS and Halothiobacillus HT1 biofilm at the micro scale. More work about the interfacial properties of the biofilm-mineral interface are needed in the future. For example, scanning transmission X-ray microscopy (STXM), which uses near-edge X-ray absorption spectroscopy (NEXAFS) as its contrast mechanism, can be further used to fully hydrate biological materials at a smaller scale (nanoscale).

Experimental Section

Biofilms Cultivation

Halothiobacillus HT1 was retrieved from −70 °C stock cultures by incubating on the LB agar plate at 28 °C for 48 h. Then, a single colony was suspended into 1 mL of sterile 0.9% NaCl. Zero-point-one grams of CuS (pretreated with 0.25 M EDTA, pH 8.0 for antioxidation and acetone for sulfide remover) and 1 mL sterile water were added in an EP tube to prepare the suspension. Then, 300 μL of this suspension was added on LB solid medium. Nuclepore ® polyester membranes (13 mm diameter, 0.1 μm pore, 6 μm thick, Whatman) were sterilized by immersion in 70% ethanol for 2 min individually, air dried and transferred with sterile forceps to the LB solid media surface. Three membranes were spaced in an equilateral triangular pattern and 3 μL of the Halothiobacillus HT1 suspension were transferred to the center of each membrane and then inoculated at 28 °C for 72 h. Control treatments without adding CuS were also prepared. Each treatment was prepared in four groups for staining, XRF analysis, sulfur XANES and Cu micro-XANES analysis, respectively.

Live/Dead Cells Distribution Using CLSM Image

Biofilms were stained with the Live/Dead BacLight Kit (Molecular Probes) to determine the distribution of Live/Dead cells. Three microliters of Syto-9 and 3 μL propidium iodide were suspended in 1 mL 0.9% saline to prepare the staining agents. The biofilms were placed on the staining agent for 30 min in the dark. Live cells of Halothiobacillus HT1 stain green, and dead cells train orange-red.

Then, the stained biofilms were cryoembeded and cryosected as follows: biofilms were placed on the chuck. Cuts perpendicular to the plane of biofilms were made using a low profile microtome blade (Paracu™, McCormick™ Scientific). Fifty micrometer-thick cross sections from the middle of each biofilms were placed on glass coverslips and examined using microscopy to confirm the thickness and structure of biofilms sections and then stored at −20 °C for analysis. The samples were examined using a Leica TCS SP5 confocal laser scanning microscope (CLSM) to detect the distribution of live/dead cells of Halothiobacillus HT1.

Distribution of Cu in the Biofilm by Using Synchrotron-Based X-Ray Fluorescence

Biofilms were sectioned as described above and placed on 3M adhesive tapes (Scotch, 810, 3M Company, St. Paul, MN, USA). X-ray fluorescence (XRF) analysis of the biofilms was performed at the XRF microprobe station (beam line 4W1B) of Beijing Synchrotron Radiation Facility (BSRF), Institute of High Energy Physics of China. The electron energy in the storage ring was 2.2 GeV, with a current range from 78 to 120 mA. The size of the exciting X-ray beam was 10 μm × 20 μm. XRF spectra were collected by a PGY Si (Li) solid detector, positioned at 90° to the beam line. The scanning points of the samples were selected and observed by a microscope. Spectra data were processed by the AXIL program to integrate the area of the element excited peak. Relative contents of elements were calculated by calibrating the peak area with electron current and normalized with Compton scattering intensity. PyMca [31] and origin 8.0 was used to analyze the distribution of the elements in the biofilms.

Copper Speciation Analysis Using Cu K-Edge Micro-XANES

Two points located in the edge and center regions of the Halothiobacillus HT1 biofilm grown on CuS (the size of exciting X-ray beam was 40 μm × 70 μm) were selected for Cu micro-XANES analysis. A 50 μm thick cross section was obtained using cryosectioning methods described above and placed on 3 M adhesive tape and then stored at −20 °C until analysis. The Cu K-edge micro-XANES spectra were measured at beam line 15 U of Shanghai Synchrotron Radiation Facility (SSRF). It was operated at 3.5 GeV with a current of 210-150 mA in the fluorescence mode for the energy range from 8.95 to 9.06 keV. The fluorescence XANES spectra were collected for 5 s dwell times and analyzed as described by the previous description [32].

Sulfur Speciation Analysis Using Sulfur K-Edge XANES

Each of the biofilms were freeze dried, mixed and ground for XANES analysis. Sulfur speciation in the biofilms was analyzed by X-ray Absorption Spectroscopy at Beijing Synchrotron Radiation Facility (BSRF). The biofilms samples were laid on 3M tape for analysis. The storage ring was operated at the energy of 2.5 GeV with Si (111) double crystals. Spectra were recorded at 4B7A beamline (medium X-ray beamline, 2100-6000 eV) and scanned at step widths of 0.3 eV in the region between 2420 and 2520 eV. Reference compounds were ferrous sulfide, elemental sulfur, potassium persulfate, sodium thiosulfate, reduced glutathione, cystine, oxidized glutathione, cysteine, dimethyl sulfone, sodium diphenylamine sulfonate, sodium dodecyl sulfate (SDS), sodium sulfite and sodium sulfate. The X-ray energy was calibrated with reference to the spectrum of the highest resonance energy peak of Na 2 SO 4 at 2,480.4 eV. The data were analyzed as described by the previous description [33].

Conclusions

Our results indicated that biofilm formation of Halothiobacillus HT1 gave rise to distinct chemical and redox gradients, leading to diverse niches in biofilms. There were two active zones in the biofilm of HT1: the air-biofilm interface and the membrane-biofilm interface. Copper was mainly distributed near the membrane-biofilm interface. Halothiobacillus HT1 biofilm oxidized CuS. Cysteine was synthesized for tolerance against Cu. The Cu-cysteine analog was involved in chelation within strain HT1 when it interacted with Cu to survive heavy metals stress.

Figure 1 .
1Spatial distribution of live and dead cells in Halothiobacillus HT1 biofilm reacted with CuS after dyeing and CLSM. (A) Light microscope images of Halothiobacillus HT1 biofilm; (B) a composite of live and dead cells; (C) live cells; (D) dead cells. Bars = 250 μm.

Figure 2 .
2X-ray fluorescence microscopy (XRF) maps of qualitative spatial distributions and concentration gradients of elements in the sections of Halothiobacillus HT1 biofilms grown on LB medium. (Left) Halothiobacillus HT1 biofilm reacted with CuS; (Right) Halothiobacillus HT1 biofilm.

Figure 3 .
3Comparison of copper K-edge XANES spectra of reference compounds and Halothiobacillus HT1 biofilm after being reacted with CuS. Black solid lines: data. Dashed lines: fits with data sets of reference compounds.

sulfonate (R-SO 2 -O-X) in minor proportions. While in the absence of CuS (the control sample), no oxidized sulfur was detected.

Figure 4 .
4Comparison of sulfur K-edge XANES in Halothiobacillus HT1 biofilm (CK), Halothiobacillus HT1 biofilm reacted with CuS and reference compounds.

2.460 2.465 2.470 2.475 2.480 2.485 2.490Absorption

Energy (KeV)

CK

biofilm grown onCuS

potassium persulfate

thiosulfate
reduced glutathione

oxidized glutathione

cytine

S
cyteine

sodium sulfite

sodium sulfate

ferrous sulfide

Table 2 .
2Percentage of different S species in the biofilms as assessed by S K-edge XANES (atom% S).Treatments
Sulfur
Cysteine
Sulfite
Sulfonate
CK
100
0
0
0
CuS
0
77.3
3.8
18.9

© 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
AcknowledgmentsThis work was supported by the National Natural Science Foundation of China (11179025, 21177109, 41071302) and the Zhejiang Provincial Natural Science Foundation of China (R5110031). We gratefully thank the staff at the Beijing Synchrotron Radiation Facility (BSRF) and Shanghai Synchrotron Radiation Facility (SSRF) for their generous help.Conflict of InterestThe authors declare no conflict of interest.
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