Modeling Arsenic Accumulation in Plants

Abul Mandal, Dan Lundh, Noor Nahar, Hoda Bentol, and Abdul Bari

Systems Biology Research Center University of Skvöde

Högskolevägen, Sweden abul.mandal@his.se

Sheila Johnson-Brousseau and Sibdas Ghosh Department of Natural Sciences and Mathematics

Dominican University of California San Rafael, U.S.A.

sibdas.ghosh@dominican.edu

Abstract— Rice growing regions plagued by arsenic-contaminated soils and irrigation water do not have a viable option for producing arsenic-free crops. For instance, in Bangladesh every year more than 30 million people are affected from rice-derived arsenic contamination that contributes to arsenic levels known to cause health-related illnesses. Our strategy is to genetically-modify molecular mechanisms involved in the localization of arsenic to divert it to the non-edible parts of the plant. To identify viable candidate genes, we employed data mining, an in silico analysis based on searching existing genomic databases and in the genetic model plant Arabidopsis thaliana. To assist our investigation, we constructed a kinetic model to outline strategies for developing genetically-modified plants exhibiting a significant reduction in arsenic concentration in the edible parts (straw and grain). This model contains equations for uptake, metabolism and sequestration of different types of arsenic (As (V), As (III,) MMAA and DMAA). The model was implemented using XPP and validated against existing data from the literature. From these analyses, we identified four candidate genes that are involved either in uptake, transport or cellular localization of arsenic in plants. But we found only one gene implicated in arsenic metabolism in rice. In parallel, we identified available T-DNA insertion mutants to determine the effects of these genes on arsenic accumulation. Results obtained from in silico data-mining, kinetic modeling, and assays with T-DNA insertion mutants will be used to design gene cloning experiments to study the target genes in yeast, E. coli, and Arabidopsis heterologous systems. Upon confirmation of the effectiveness of these candidates, vectors containing the target genes will be constructed for transformation into rice. The new rice varieties produced will be tested under field conditions to assess their effectiveness at reducing or eliminating arsenic from the edible parts of the rice plant.

Keywords-Arabidopsis, data-mining, rice, arsenic accumulation, arsenic metabolism, arsenic uptake

I. INTRODUCTION Arsenic is the 20th most abundant element in the earth`s

crust and found ubiquitously in nature. Arsenic is classified as a metalloid, having properties of both metals and non-metals, and can undergo a wide range of chemical interactions. Different forms of arsenic induce distinct types of cellular damage and the forms most prevalent in nature are as inorganic species of arsenic: Arsenite (As (III)) and Arsenate (As (V)). Long-term exposure to arsenic can lead to a variety of skin, neurological and peripheral vascular

disorders, as well as numerous types of cancers. In addition, health related illnesses linked with arsenic exposure include: diabetes, ischemic heart disease, reproductive defects, and impairment of liver function [1].

High levels of naturally-occurring arsenic are present in the underlying geology of regions of the world, such as Bangladesh [2]. Compounded by arsenic-contaminated water, approximately 30 million people in Bangladesh and 20 million in India are consuming arsenic-contaminated food daily, including rice, wheat and vegetables as well as meat, since rice straw is widely used as fodder for cattle, sheep, and pigs. Thereby, arsenic contamination of the food chain is prevalent and in these two countries, as well as others in South Asia, it is now a credible health concern. One possible measure to stem the health risk to human populations is to develop new crop varieties designed to metabolize arsenic or to prevent or reduce uptake of arsenic from arsenic-contaminated soil. Cultivated rice is one of the world’s staple foods, especially in South Asia. Therefore, varieties of cultivated rice designed to eliminate or reduce arsenic are needed and will serve to protect hundreds of millions of people world-wide from exposure to food-based arsenic.

Here we report on a new assessment strategy to model arsenic accumulation which resulted in the identification of four target genes that are being evaluated in planta and in heterologous systems for their potential in generating crops designed to reduce or eliminate arsenic contamination from the edible, above-ground parts of plants. Rice cultivars developed from these efforts will be evaluated in field conditions in areas with naturally-occurring arsenic-contaminated soils, and if successful, this approach may serve as a general strategy for other above-ground crops of strategic importance.

II. METHODS To identify candidate genes of interest in arsenic

metabolism, we employed in silico analyses that (1) mined publically-available genomic data and (2) employed a systems biology model to reflect the kinetics of uptake, metabolism and sequestration of different types of arsenic, including As (V), As (III), MMAA and DMAA [3].

A. Data-mining We initially identified putative genes related to influx,

metabolism, and possible elimination of arsenic, searching different species ranging from yeast to humans. For these

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searches, we used both PubMed and the nucleotide search in Entrez (searching by key words/mesh terms). This step was performed without any prior settings of organism or cellular compartment/organelle. The outcome was a vast number of genes in different organisms varying widely in function. To assist in managing this data, we developed a number of programs in AWK.

From our initial set of genes, we identified possible homologous genes in plant species, i.e. Arabidopsis thaliana, using BLAST to detect similarities in gene sequence. If the gene was identified with high degree of similarity it was labeled as a putative candidate.

For the putative candidate genes we scanned known interactions of gene products using metabolic databases (e.g. KEGG, Enzyme) and scanning PubMed abstracts (looking for sentences with both genes present and an association between them; e.g. words like binds, interact associates, etc) to see possible interactions between genes. The AWK programs were used to rank associations based on the number of abstracts stating the relationship.

Finally, our model of interacting genes/gene products was cross verified with currently existing models in other species (yeast and human).

To obtain the kinetic parameters used for the kinetic model we also used data mining. We used reaction rates for Arabidopsis thaliana whenever possible, i.e. the same species as the derived model. If any data of a kinetic variable was missing we initially searched reaction rates for related plant species (direct values or derivable values). If no rate was found we adopted values primarily from other plants and secondarily from other organisms (homologous genes). Still if no value was found we estimated the value from available published data (curve fitting).

B. The Kinetic Model An illustration of arsenic uptake, transport, metabolism and sequestration is shown in Figure 1. To model these processes for the purpose of estimating arsenic accumulation in different parts of plants, we used a kinetic model based on Arabidopsis thaliana (Table 1) that was implemented in the software XPP. The XPP software is a tool for solving differential, difference and delay of stochastic functions. The model was validated against published data [4, 5].

Figure 1. Overview of arsenic transport, metabolism and sequestration in plants. Arsenics enter the plant via root cells. Arsenate As (V) is reduced to arsenite As (III) by ARSENATE REDUCTASE 2 (ACR2) [4]. As (III) can

be methylated first to monomethylarsonic acid MMAA and then to dimethylarsinic acid DMAA. As (III) is sequestered in the vacuoles as: (i) free As (III) or As-thiol complexes, (ii) As-PCn phytochelatins, with the

help of PHYTOCHELATIN SYNTHASE 1 (PCS1) using MULTI-DRUG RESISTANT PROTEIN 1 and 2 (MRP1/2). Arsenics are transported to the

shoots via the xylem.

TABLE I. KEY TO KINETIC MODEL EQUATIONS

Symbol Explanation (value)

As(V)root Free arsenate in the root

Vmax_As(V) Velocity (76 nM/h) for arsenate uptake [4, 5, 6]

Km_As(V) Michaelis-Menten constant (2.6 μM) for arsenate uptake [7]

As(V)extern External concentration of arsenate (exposure/soil concentration)

kred Association constant (0.07 h-1) representing the reductase of arsenate to arsenite [8]

ktrans_1 Represents the transport (0.0006 h-1) of free arsenate to the shoots/leaves, i.e. As(V)leaf . This was derived from published data [4, 9, 10, 11].

As(V)leaf Free arsenate in the leaf

As(III)root Amount of free arsenite in the root

Vmax_As(III) Velocity (120 nM/h) for arsenite uptake [7, 12]

Km_As(III) Michaelis-Menten constant (15.5 μM) for arsenite uptake [7]

As(III)extern External concentration of arsenite (treatment/soil concentration)

As(III)leaf Amount of free arsenite in the leaves

ktrans_2 Transport rate (0.00003 h-1) of arsenite to the shoots/leaves, derived from published data [4, 9, 10, 11].

kmet Association constant (0.000125 h-1) representing the methylation of arsenite to MMAA [8]

kseq_free Rate (0.00055 h-1) of sequestering free arsenite in vacuoles calculated from published data [5]

kseq_gs Rate (0.00255 h-1) of sequestering As-thiol/phytochelatins, i.e. As-PCn complexes calculated from published data [5]

As(III)root_seq Total amount of sequestered arsenite in roots

As(III)leaf_seq Total amount of sequestered arsenite in leaves

MMAAroot MMAA content in the root

Vmax_MMAA Maximal velocity (6.63 nM/h) of formation (methylation) of DMAA from MMAA [8]

Km_MMAA Michaelis-Menten constant (630 μM) for the formation of DMAAfrom MMAA [8]

Ki Noncompetitive inhibition constant (8.9 μM) of arsenite inhibition of MMAA methylation [8]

 

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Symbol Explanation (value)

Vmax MMAA_influx Maximal velocity (16 nM/h) of MMMA uptake from the environment MMAAextern [7]

MMAAextern External concentration of MMAA (exposure/soil concentration)

Km_MMAA_influx Michaelis-Menten constant (15 μM) for MMAA uptake [7]

ktrans_3 Association constant representing the transport (0.00005 h-1) of MMAA to the shoots/leaves, i.e. MMAAleaf derived from published data [4, 9, 10, 11]

MMAAleaf MMAA concentration in leaves

DMAAroot DMAA concentration in root

DMAAextern External concentration of DMAA (exposure/soil concentration)

DMAAleaf. DMAA concentration in leaves

ku The uptake rate (association) of DMAA (3 h-1) from the environment (DMAAextern) to the root, i.e. DMAAroot [7]

kefflux Rate of arsenite transport (0.0001094 h-1) out of the root cells (equation 11), derived from published data [13].

As(V)root Free arsenate in the root

The root influx, arsenate reductase and transport of arsenate were modeled according to equation 1:

roottransrootredexternVAsm

externVAsroot VAskVAskVAsK

VAsVdtVdAs )()(

)()()(

1_)(_

)(max_ −−+

=

The root influx, sequestration and transport of As (III),

together with reductase of As (V) were modeled according to equation 2:

roottransgsseqrootfreeseq

rootmetrootredeIIIAsm

externIIIAsroot

IIIAskIIIAskIIIAsk

IIIAskVAskIIIAsK

IIIAsVdtIIIdAs

)()()(

)()()(

)()(

2___

)(_

)(max_

−−−

−++

=

The sequestration of arsenite in the roots was modeled using equation 3 (see also reaction rates in equation 2):

rootgsseqrootfreeseqseqroot IIIAskIIIAsk

dtIIIdAs

)()()(

___ +=

The influx, transport, and methylation of As (III) to

MMAA in the root were modeled by using equation 4:

roottransexternluxMMAAm

externluxMMAA

i

rootrootMMAAm

rootMMAArootmet

root

MMAAkMMAAK

MMAAVKIIIAs

MMAAK

MMAAVIIIAsk

dtdMMAA

3_inf__

inf_max_

_

max_

))(

1()(

−+

+

++−=

The uptake, methylation and transport of DMAA in the root were modeled by using equation 5:

roottransexternu

i

rootrootMMAAm

rootMMAAroot

DMAAkDMAAkKIIIAs

MMAAK

MMAAVdt

dDMAA

3_

_

max_

))(

1(

−+

++=

In the leaves/shoots we assumed that the reactions

occurred similarly with the exception that there was no uptake of As (V), As (III), MMAA, or DMAA from external medium. That is, the leaf/shoot content of As (V) was modeled as equation 6:

leafredroottransleaf VAskVAsk

dtVdAs

)()()(

1_ −=

The leaf/shoot content of As(III) was modeled as

equation 7:

leafgsseqleaffreeseqleafmet

leafredroottransleaf

IIIAskIIIAskIIIAsk

VAskIIIAskdtIIIdAs

)()()(

)()()(

__

2_

−−−

+=

The sequestration of As (III) in the leaves/shoots was

modeled based on equation 8:

leafgsseqleaffreeseqseqleaf IIIAskIIIAsk

dtIIIdAs

)()()(

___ +=

The MMAA content in the leaves/shoots was modeled by

using equation 9:

roottrans

i

leafleafMMAAm

leafMMAAleafmet

leaf

MMAAkKIIIAs

MMAAK

MMAAVIIIAsk

dtdMMAA

3_

_

max_

))(

1()(

+

++−=

Finally, the DMAA content in the leaves/shoots was

modeled by using equation 10:

roottrans

i

leafleafMMAAm

leafMMAAleaf DMAAk

KIIIAs

MMAAK

MMAAVdt

dDMAA3_

_

max_

))(

1(+

++=

To estimate addition of arsenite efflux from the root (As(III)root) with the help of an extrusion pump for heavy metals, e.g. the ZntA efflux pump from Eschericha coli

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which pump Pb (II), Cd (II), and Zn (II) out of the cell [13], an additional term was added to equation 2, i.e. extending equation 2 with equation 11:

rooteffluxroot IIIAsk

dtIIIdAs

)()(

=−

The arsenic levels in edible parts are affected by the

concentration of different arsenic species in the soil; As (V) and As (III). Five different simulation setups were used in our investigations: 1) 100 μM As (V), 2) 25 μM As (V), 3) 100 μM As (III), 4) 25 μM As (III), and 5) 100 μM As (V) combined with 100 μM As (III). All of the outlined options for reducing arsenic content in edible parts were tested with these setups. The amount of arsenic in the respective compartment was defined as the sum of As (V), As (III); free and sequestered, MMAA, and DMAA.

The simulations (options) employed a time step of 0.1 hours. The period of exposure of Arabidopsis thaliana to all these simulated options was limited to three weeks.

Gene candidates identified from these analyses are being cloned and evaluated in heterologous systems. Planned and future research is diagrammed in Figure 2.

Figure 2. Work flow of planned and future research.

III. RESULTS Using an in silico approach, we have identified four

target genes that were further analyzed using our kinetic model to simulate gene silencing, over-expression, and arsenite sequestration we to evaluation their role in the accumulation of arsenics in Arabidopsis. The genes include ACR2, PCS1, MRP1 and MRP2. The results of these analyses are as follows:

When ACR2 was silenced in our kinetic model, plants were that were given a simulated dosage of of 100 μM As (V), 25 μM As (V), and 100 μM As (V)/100 μM As (III) for three weeks had increased levels of As in the plant shoots (350-400 μg/g d.wt) compared to wild-type controls (40-55μg/g d.wt). This was expected based on previous studies [4]. Uptake and transport of arsenite was unaffected by ACR2 silencing.

Overexpression of ACR2 had the opposite effect on accumulation of arsenics in shoots (μg/g d.wt). Plants given a simulated dosage of 100 μM As (V), 25 μM As (V), 100

μM As (III), 25 μM As (III), and 100 μM As (V)/100 μM As (III) for 3 weeks had increased sequestration in the roots and less accumulation of arsenic in the shoots as compared to wild-type controls. A 56% decrease in As accumulation in shoots was observed in simulations where ACR2 was overexpressed 10-fold, whereas, a 59% decrease in shoot arsenic accumulation was observed in simulations where ACR2 was overexpressed 20-fold compared to the wild-type control. The fold change was similar in root and shoots, reflected a tissue independent overexpression of the ACR2 gene. Thus increased sequestration of As occurs in the roots when ACR2 is overexpressed.

To assess whether overexpression of the ATP-driven pumps MRP1 and MRP2 contributes to increased sesquestration of As (III)-thiols in the vacuole, simulations were carried out exposing plants to 100 μM As (V), 25 μM As (V), 100 μM As (III), 25 μM As (III), and 100 μM As (V)/100 μM As (III) for three weeks under conditions of 5- , 10-, or 20- fold overexpression of MRP1 or MRP2. As (III)-thiol complexes formed by PCS1 were assumed to be regulated by arsenics as previously described [5]. At 5-fold overexpression, near 82% of arsenite was sequestered in the vacuoles of roots compared to 49% in wild-type controls. And of the arsenite present in the shoots, over 75% was sequestered in the shoot vacuoles compared to 38-43% in the wild-type control. The rate of sequestration in the roots increased as overexpression increased, and at 20-fold overexpression, our simulations demonstrated that 96% of arsenite was sequestered in the vacuoles of roots.

Since our simulations indicated that single genes could have a beneficial effect on the localization of arsenic accumulation in plants, we evaluated gene combinations to determine if there was an additive effect on arsenic accumulation. For these plant simulations, either a 5-fold, 10-fold, or 20-fold overexpression of ACR1 and PCS1 was evaluated when plants were given simulated doses of of 100 μM As (V), 25 μM As (V), 100 μM As (III), 25 μM As (III), and 100 μM As (V)/100 μM As (III) for 3 weeks (Figure 3).

Figure 3. Combined features of ACR2 and vacuole sequestrations As

accumulation in shoots (μg/g d.wt).

In simulations of a new variety of plant with both increase arsenate reductase activity and sequestration in the vacuoles, shoot arsenics were reduced 59-89% compared to wild-type. The highest reduction of 88-89% in shoot arsenics was achieved in simulations when there was a 10-fold

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arsenate reductase combined with a 20-fold increase in sequestration of As (III)-thiol complexes, demonstrating that gene combinations can further enhance the potential of reducing shoot arsenics in plants exposed to arsenic.

IV. CONCLUSIONS Previously, strategies for the reduction of arsenic content

in plants/environment have been identified [14, 15, 16, 17]. The approach we present here incorporates prior strategies but differs in that we have used a systems biology framework and a systems biology model (kinetic model) based on Arabidopsis thaliana that also allows us to assess the likely efficiency of each strategic approach. Based on our in silico

Previously, strategies for the reduction of arsenic content in plants/environment have been identified [14, 15, 16, 17]. The approach we present here incorporates prior strategies but differs in that we have used a systems biology framework and a systems biology model (kinetic model) based on Arabidopsis thaliana that also allows us to assess the likely efficiency of each strategic approach. Based on our in silico studies, we have identified four genes that can be used to reduce arsenic uptake – ACR1, PCS1, MRP1 and MRP2.

When exposed to high arsenic concentrations, our modeling experiments have demonstrated that by increasing or decreasing the expression level of these target genes in various combinations we can generate a hypothetical transgenic plant that has significantly lower accumulated arsenics in the shoots than wild-type control plants. Some of these genes contribute to “capturing” significant amounts of arsenics in the roots by sequestration into the vacuoles, thereby reducing transport to the shoots. Validation of these in silico results by in vivo experiments either with SALK insertion mutants or transgenic plants are in progress. These experiments lay the foundation for the development of a new rice cultivar that can be grown in arsenic-impacted regions of the world to limit and help mitigate human exposure to arsenic.

V. ACKNOWLEDGEMENTS We would like to thank the following institutions for their financial support to complete this project: University of Skvöde, Sweden (AM, DL, NN, and HB), Dominican University of California, U.S.A. (SJB and SG), and University of Rajshahi, Bangladesh (AB).

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