Regulating lead bioremediation-related genes through a construct design

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Regulating lead bioremediation-‐related genes through a construct design

Manuela Vanegas Ferro

Centro de Investigaciones Microbiológicas (CIMIC), Universidad de los Andes

Laboratorio de Biofísica, Universidad de los Andes

Abstract

Lead is a major water contaminant. As a metal without a biological function, small doses can disrupt the normal functioning of the excretory, reproductive and nervous systems. In the local context, lead has been a contaminant of Bogotá River for many years, and it can be found in high levels in crops irrigated with the same river. As a solution, several genes found in different strains can be expressed in Escherichia coli in order to reduce the bioavailability of lead. Lysinibacillus sphaericus OT4b.31 produces an S-‐layer, which is capable of accumulating a fraction of the metal. Three S-‐layer monomer-‐encoding sequences were identified in its genome so they are candidates for cloning in E. coli. Additionally, Cupriavidus metallidurans CH34 harbors in its pMOL30 plasmid an operon (pbr) involved in specific resistance to lead. This project proposes a regulatable construct that would express the mentioned genes in a way that allows its usage as a bioremediation tool. Computational tools and synthetic biology techniques were deployed in order to establish the viability of the design and to start bringing it into reality. The realization of the project faced several issues: first, the model deployed in the simulation gave rise to an unexpected behavior; second, some technical difficulties hampered the generation of the proposed construct.

Introduction

Lead is a major water contaminant. Unlike essential metals as sodium, calcium, iron or copper, lead is a metal that has no known biological function, so small doses can be exceptionally poisonous (Lemire, Harrison, & Turner, 2013). In humans, lead interferes with calcium metabolism by directly replacing the metal and by interfering with vitamin D metabolism. It has also been shown that placental transfer begins early in gestation and that young children accumulate 4-‐5 times more lead than adults. In addition, lead affects both peripheral and central nervous systems. Epidemiological studies have shown a significant association between high levels of lead in blood and a 4-‐point reduction in intelligence quotient and that prenatal exposure may impair mental development. These properties place infants and pregnant women as the more susceptible to adverse health effects. In addition, experimental animals exposed to high concentrations of lead compounds in the diet have developed renal tumors (World Health Organization, 2006).

Exposure to lead occurs through inhaled air particles or through ingestion of contaminated water and food (“WHO | Water-‐related Diseases,” 2001). However, attention has shifted to contaminated water due to the decreasing use of lead-‐containing additives in fuels and hence the decline in its atmospheric emissions (World Health Organization, 2011). In the local context, lead has been a contaminant of Bogotá River for

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many years, and it can be found in high levels in crops irrigated with the same river (Miranda, Carranza, Rojas, Fischer, & Zurita, 2008). Likewise, according to Acosta and Montilla (2011), the concentration of lead in Subachoque River is ten times greater than the limit recommended by the World Health Organization for drinking water (World Health Organization, 2006).

A possible answer to this contamination problem is bioremediation. Several genes found in different species can be expressed in Escherichia coli in order to reduce the bioavailability of lead. Cupriavidus metallidurans CH34 (Mergeay et al., 1985) harbors in its pMOL30 plasmid an operon (pbr) involved in specific resistance to lead (Borremans, Hobman, Provoost, Brown, & van der Lelie, 2001). Some proteins of this operon are interesting under the light of this study. PbrR is a protein dimer that regulates the expression of the operon through a highly specific binding of lead ions (Wei et al., 2014). PbrD binds intracellular lead to protect the cell from toxicity by reducing the citoplasmic concentration of free ions, thus allowing a significant accumulation of this metal (Borremans et al., 2001). PbrT is a lead uptake protein, which allows the entry of lead ions through the inner membrane (Borremans et al., 2001). Besides this interesting operon, other genes may be useful for bioremediation of metals. Lysinibacillus sphaericus OT4b.31 is highly resistant to lead (MIC > 2mM;Peña-‐Montenegro, 2013), owing in part to the production of an S-‐layer, which is capable of accumulating a fraction of the metal (Bojacá, 2011). S-‐layers are common in prokaryotes. They constitute the outermost barrier from the environment and consist of a lattice of a single protein that self-‐assembles in a specific array (Sleytr et al., 2011). A single strain may exhibit several S-‐layer protein genes, which they can express alternatively to cope with changing environmental conditions (Lederer et al., 2013). In the genome of L. sphaericus, three putative monomer-‐encoding sequences were identified, which are homologous to the genes sllB, slp5 and slp6 (Peña-‐Montenegro, 2013).

Taking into account the mentioned genes, this project proposes the construction of a fusion protein consisting of an S-‐layer monomer and a PbrR monomer, which would theoretically assemble in the cell surface of recombinant E. coli and provide a lead-‐sequestering area. A regulatable construct was designed in order to express the mentioned genes and the fusion protein in an optimal manner (Figure 1). The objective of the design is its employment as a bioremediation tool. As such, it is important that conditions used in massive cultivation of bacteria are still suitable for the modified ones. With this in mind, the regulating system was designed in order to repress the expression of bioremediation-‐related genes until a chemical stimulus is given. At this moment, the bacteria would start synthetizing the proposed fusion protein and PbrD, which would sequester lead, so they could begin being exposed to contaminated water. When the cells are reaching saturation of both molecules, they would start expressing PbrT, which would enhance lead accumulation within the cell and, at last, would favor cell toxicity and death.

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Figure 1: Proposed construct design

To achieve this, the interaction between inducible promoters and their repressors was taken into account. The design relies on the idea of the ‘genetic toggle switch’, proposed by Gardner, Cantor, and Collins (2000), in which, given the right parameters, the reciprocal repression of two promoters allows the stable expression of either of them, even long time after the stimulus was withdrawn. In this case, the expression of TetR, the tet operon repressor (Kamionka, Bogdanska-‐Urbaniak, Scholz, & Hillen, 2004), is driven by an hybrid Lacpromoter (Lutz & Bujard, 1997). Conversely, an hybrid Tet promoter (Lutz & Bujard, 1997) controls the expression of LacI. In addition, the design has implemented a delayed expression through a double derepression system. The activation of Lac promoter leads to the synthesis of CI, which deactivates PR promoter, which in turn in normal conditions would transcribe a iRNA targeting pbrT mRNA. With PR deactivation, iRNA are no longer transcribed, so PbrT can begin being synthetized.

The objective of this project is to propose the mentioned design as a tool for lead bioremediation and to use both synthetic biology techniques and computational simulations to study its viability and to start making a reality of it.

Methods

Methods are divided into a computational simulation and experimental implementation.

Simulation

The simulation was attempted through a deterministic model comprising a series of differential equations. The Runge-‐Kutta method was implemented in MatLab to solve the set of equations. The model was subjected to optimization of the RBS strength parameters in order to maximize a score that took into account the total lead removal and uptake and a lethal internal lead concentration, which would dramatically decrease the score should it be reached (Equation 1.) The ‘step’ function is equal to 0 when the substraction is less than 0 and 1 when it is more than 0.

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IPTG Equation

Taking into account the hypothetical employment of the modified bacteria, in which the addition of IPTG would trigger the expression of the proteins involved in bioremediation, Equation 2 describes the diffusion and transport of this molecule into the citoplasm. The equation was modified from the one proposed by , in which the number of permeases was assumed to be constant, so it took into account passive diffusion of IPTG through the membrane and the external concentration of the molecule was also fixed as a constant.

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mRNA Equations

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Protein Equations

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Lead Equations

Equation 17 describes the process of lead removal through sequestration by S-‐layer and PbrD monomers, and PbrR dimers. A dimerization constant for PbrR could not be found or calculated due to a lack of studies regarding the PbrR molecule so it was considered to be equal to 1. This value is conservative, since a predicted increase in dimerization efficiency, which would arise from the spatial proximity and

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movement constraint of PbrR monomers linked to the S-‐layer lattice, would reflect in a reaction constant greater than 1. Ferner-‐Ortner-‐Bleckmann, Gelbmann, Tesarz, Egelseer, & Sleytr (2013) demonstrated that linkage to S-‐layer monomers favored extremozyme multimerization, so this could be the case for PbrR dimers. Thus, the model is probably underestimating the effect of PbrR dimers on lead sequestration. Equation 18 describes the change of internal lead concentration, substracting the quantity sequestered by PbrD, since only free ions can cause damage to the cell.

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Table 1 lists the variables considered, while Table 2 and 3 show the constants found in literature and estimated from different data, respectively.

Table 1: Variables used in the model

Symbol Description

I Internal concentration of IPTG

r1 mRNA under control of Tet promoter

r2 mRNA under control of Lac promoter

r3 mRNA under control of PR promoter

r4 mRNA under control of Anderson’s Constitutive J23014 promoter

LacI Concentration of LacI repressor

CS Concentration of S-‐layer monomer

PbrR Concentration of PbrR monomer

PbrD Concentration of PbrD molecules

TetR Concentration of TetR repressor

CI Concentration of CI repressor

GFP Concentration of GFP signaling protein

PbrT Concentration of PbrT molecules

mCh Concentration of mCherry signaling protein

PbExt External concentration of lead

PbR Removed (sequestered) lead

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Table 2: Constants used in the model, found in literature

Symbol Description Value Units References

nPerm Concentration of permease 50 x 10-‐3 _mM _{(Kalisky, Dekel, & Alon, 2007)}

VIin IPTG uptake rate 495.1 min-‐1 (van Hoek & Hogeweg, 2007)

VIout IPTG efflux rate 49.35 min-‐1 (van Hoek & Hogeweg, 2007)

VI Passive IPTG diffusion rate 1.35 min-‐1 _{(van Hoek & Hogeweg, 2007)}

IExt External IPTG concentration 1 mM (Pasotti, Politi, Zucca, Cusella

De Angelis, & Magni, 2012) KPermIn Saturation constant of permease (uptake) 0.42 mM (van Hoek & Hogeweg, 2007)

KPermOut Saturation constant of permease (efflux) 21 mM (van Hoek & Hogeweg, 2007)

γI Degradation rate of IPTG 0.0016 min-‐1 (Politi et al., 2014)

αTet Basal transcription rate of Tet promoter βTet/100 -‐ (Gardner et al., 2000)

βTet Active transcription rate of Tet promoter 6.39 mRNA/min (RPUs and reference PoPS

from Kelly et al., 2009) KTetR Dissociation constant of TetR and

operator region

1.78 x 10-‐7 _mM _{(Kamionka et al., 2004)}

nTetR Hill constant of TetR 2 -‐ (Braun, Basu, & Weiss, 2005)

γRNA Degradation rate of mRNA 0.693 min-‐1 (Cheng, Fournier, Relue, &

Schisler, 2001) αLac Basal transcription rate of Lac promoter βLac/620 -‐ (Lutz & Bujard, 1997)

βLac Active transcription rate of Lac promoter 5.76 mRNA/min (RPUs and reference PoPS

from Kelly et al., 2009) KLacI Dissociation constant of LacI and

operator region

8 x 10-‐4 _mM _{(Basu, Gerchman, Collins,}

Arnold, & Weiss, 2005)

nLacI Hill constant of LacI 1 . (Kalisky et al., 2007)

KI Dissociation constant of IPTG and LacI 5.5 x 10-‐4 mM (van Hoek & Hogeweg, 2007)

nI Hill constant of IPTG 2 -‐ (Kalisky et al., 2007)

αPR Basal transcription rate of PR promoter βPR/2670 -‐ (Braun et al., 2005)

βPR Active transcription rate of PR promoter 5.85 mRNA/min (RPUs from Pasotti et al.,

2012, reference PoPS from Kelly et al., 2009)

KCI Dissociation constant of CI and operator

region

55 x 10-‐6 _mM _{(Rosenfeld, Young, Alon,}

Swain, & Elowitz, 2005)

nCI Hill constant of CI 2 (Ackers, Johnson, & Shea,

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Kdimer Dimerization constant of CI monomers 2 x 10-‐5 mM (Ackers et al., 1982)

βCons Transcription rate of Anderson’s

Constitutive J23104 promoter

4.59 mRNA/min (RPUs and reference PoPS

from Kelly et al., 2009)

kLacI Translation rate of LacI 10 1/min* mRNA (W. Chen, Bailey, & Lee, 1991)

kGFP Translation rate of GFP 80 1/min* mRNA (Kelly et al., 2009)

kmCh Translation rate of mCherry 80 1/min* mRNA (Kelly et al., 2009)

γLVA Degradation rate of LVA-‐tagged proteins 0.0692 min-‐1 (Basu et al., 2005)

µ Protein decay rate due to cellular growth 0.0333 min-‐1 _{(Cheng et al., 2001)}

PbExI Initial external lead concentration 5 x 10-‐4 mM (Acosta & Montilla, 2011)

KdimPbrR Association constant of PbrR dimer and

lead

5000 mM-‐1 _{(P. Chen et al., 2005)}

Table 3: Constants used in the model, estimated from different data in literature

Symbol Description Value (Units) References / Notes

kCS Translation rate of S-‐layer

monomer

3.32 (1/min*mRNA) Assumed translation rate to be inversely proportional to mRNA length. (kLacI from W.

Chen et al., 1991) kPbrR Translation rate of PbrR

monomer

3.32 (1/min*mRNA) Dictated by the lowest rate between kCS and kPbrR

kPbrD Translation rate of PbrD 15.88 (1/min*mRNA) As kCS

kTetR Translation rate of TetR 16.83 (1/min*mRNA) As kCS

kCI Translation rate of CI 14.88 (1/min*mRNA) As kCS

kPbrT Translation rate of PbrT 5.98 (1/min*mRNA) As kCS

CCS Constant of affinity between S-‐

layer monomer and lead

1.99 x 10-‐6 _{(fraction of}

removed lead/S-‐layer monomer)

Bojacá (2011) reports the accumulation of lead per periplasmatic protein weight. Using S-‐layer monomer molecular mass and assuming a volume of surface interaction of 1 fL, the fraction of sequestered versus total lead was calculated. CPbrD Constant of affinity between

PbrD and lead

9.86 x 10-‐5 _{(fraction of}

removed lead/PbrD molecule)

Borremans et al. (2001) report the shift in lead accumulation due to PbrD expression. Assuming that internal concentration of free lead ions does not change (PbLethal for C. metallidurans) and that

the change is due entirely to PbrD presence, it is possible to calculate the fraction of PbrD-‐bound lead versus total citoplasmic lead.

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passive diffusion or non-‐ specific transport of lead through plasma membrane

metallidurans and the MIC of a C. metallidurans

strain lacking pbr operon.

PbLethal Lethal internal lead

concentration

1.54 (mM) Kumar & Upreti (2000) report lead

concentration in E. coli dry weight and 95% belongs to the membrane fraction. The remaining 5% is assumed to be the highest concentration the cell could tolerate.

Experimental implementation

The experimental implementation comprised two objectives: the PCR amplification of genes of interest from L. sphaericus and C. metallidurans and the creation of a regulatable construct according to the proposed design.

Regarding the S-‐layer-‐encoding genes, the target amplification consisted of truncated versions of the complete sequences. This is due to the presence of S-‐layer Homology (SLH) domains in the N-‐terminal portion of these proteins, which have the function of anchorage to the cell wall of Gram-‐Positive cells (Sleytr et al., 2011). However, this domain has little use in Gram-‐Negative cells and a truncated form of SllB, which did not form inclusion bodies, was successfully expressed in E. coli (Lederer et al., 2010). Therefore, the three S-‐layer sequences were analyzed through CD-‐Search (Marchler-‐Bauer et al., 2015) and SignalP (Petersen, Brunak, von Heijne, & Nielsen, 2011) in order to find the specific site of truncation. In order to verify the integrity of protein folding, which could be disturbed by the truncation, the sequences were analyzed with FoldIndex (Prilusky et al., 2005). Primers were designed taking into account the codon relative adaptiveness calculated through Graphic Codon Usage Analyzer (Fuhrmann et al., 2004). If poorly adapted codons were within 20 nucleotides from the ends, the corresponding mutations were introduced in the primers.

PrimerQuest (Owczarzy et al., 2008) was employed in order to design primers for sequences of both C. metallidurans and L. sphaericus. Having in mind the BioBricks standard (Phillips & Silver, 2006), 5’ tails, comprising restriction sites for EcoRI and XbaI or SpeI and PstI, were added to forward and reverse primers respectively. Finally, possible hairpins, self-‐dimers or hetero-‐dimers were analyzed through OligoAnalyzer (Owczarzy et al., 2008). PCR, gel extraction and PCR purification protocols were implemented according to manufacturer’s instructions. Due to a lack of specificity of the designed primers, Touchdown and temperature gradient PCR protocols were included. In addition, it was necessary to purify gel bands and repeat the PCR in order to sequence the presumptive target genes.

In order to generate the designed construct, BioBricks present in the 2014’s iGem Distribution were employed. The ‘3A Assembly’ protocol (iGem, 2014) was implemented: successive cycles of plasmid extraction, double digestion, ligation and transformation proceeded according to each manufacturer’s instructions.

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Results and discussion

The computational simulation did not seem to reach any acceptable behavior. Although the synthesis of different proteins seemed to be somewhat adequately modeled, it was not possible to achieve a reasonable model for the behavior of internal, removed, or external lead concentration. Probable causes of this failure are inadequate estimation of parameters or simply a highly parameter-‐dependent model. In either case, the ideal step would be to run a sensibility analysis for each constant, especially those that were estimated. Since the model failed to describe the behavior of the construct, it was not reliable regarding the optimization of the RBS strength values, so they were not taken into account during the assembly of BioBricks.

Figure 2: Intermediate parts that were constructed by 3A Assembly and their visualization in agarose gels.

Regarding the experimental implementation, some advances were made. As shown in Figure 2, some parts of the construct were attained through the 3A Assembly method. However, the continuity of this work was hampered by the lack of efficiency of the process: digestion, ligation and transformation have low success rates and therefore any of them can fail in a given experiment. Other methods have been proposed, as the ‘amplified insert assembly’ (Speer & Richard, 2011), which remediates the inefficiency of digestion and ligation, or the ‘Gibson assembly’, which relays on far more efficient reaction: the PCR. It would be ideal to test these ‘assemblies’ in order to find the most suitable method.

The amplification of target genes from C. metallidurans and L. sphaericus was unfruitful. As shown in Figure 3 he reaction for pbrR yielded a band of the expected size. However, sequencing data revealed that this did not correspond to the target sequence. The presumptive pbrT band marked in Figure 3 could not be isolated and sequenced due to the proximity and concentration of the subsequent band.

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Figure 3: pbrR and pbrT amplifications in gradient PCR. The white rectangles demarcate the bands corresponding with the expected length of the amplicon, as stated at the bottom of the images

After several unsuccessful attempts on amplifying any of the S-‐layer genes, new primers were designed, which did not contain the 5’ tail previously added. Figure 4 pictures the bands of expected size for both sllB and slp5. These bands are promising, but have not been sequenced yet. These results demonstrate the important role of the 5’ tail of primers during PCR. Although it should not modify the primer specificity, a 5’ tail comprising two restriction sites is long enough to provide new and stable hairpins. In addition, restriction sites are palindromic and therefore cause the creation of stable self-‐dimers. These obstacles could be surpassed using techniques as touchdown PCR or adjuvants like DMSO, but the experience here presented suggests it is a better strategy to use traditional primers for initial isolation of the target sequence and only then start using modified primers.

Figure 4: sllB and slp5 amplifications in gradient PCR. The white rectangles demarcate the bands corresponding with the expected length of the amplicon, as stated at the bottom of the images

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Conclusions

This project proposed a construct design with potential application in lead bioremediation, aimed to study its simulated behavior and viability, and started generating it. However, the computational model failed in describing the theoretical behavior of the system. While protein synthesis seemed to follow a Hill function, internal, removed, and external lead concentration did not behave in a reasonable manner. According to the way in which external lead concentration was modeled, with a constant initial concentration from which removed and intracellular lead values were substracted, it was expected that it would decrease, instead of increasing, through time. Additionally, this decrease should have responded to the increase of removed or intracellular lead concentration, but these values had an unexpected behavior, which could not be related to the change in external lead concentration. Since the objective of the model was to simulate the process of bioremediation by a hypothetical cell harboring the construct here proposed, these three variables constituted the focus of the model. This departure from a theoretically reasonable behavior was unacceptable. It is therefore necessary to examine the model sensibility to parameters to establish the cause of this failure.

Regarding the experimental implementation, the assembly of BioBricks via 3A Assembly was found to be inefficient. It would be a valuable effort to put to the test the advantages offered by alternative methods like Gibson Assembly. On the other hand, PCR protocols based on modified primers were unsuccessful in amplifying the target sequences, whereas traditional primers provided promising results. Therefore, it may be more effective to use traditional primers for initial isolation of the target sequence and then switch to modified primers to introduce the desired characteristics to the fragment.

The present study faced several difficulties that delayed the acquisition of results, both simulated and physical, but these can be obtained in the future. Regarding the simulation, the first step will be to perform a sensibility test, primarily on the estimated parameters, to check if they lay on a reasonably stable range or small variations destabilize the model. On the other hand, amplified insert or Gibson’s assembly may be implemented to increase the efficiency of BioBrick assembly. In addition, new primers have to be designed in order to improve the likelihood of amplifying the target sequences from L. sphaericus and C. metallidurans. However, the results here exposed are not complete enough to suggest an outcome for the proposed construct design.

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