ieee sensors journal, vol. 8, no. 6, june 2008 1011 … · 2014. 4. 4. · ieee sensors journal,...

IEEE SENSORS JOURNAL, VOL. 8, NO. 6, JUNE 2008 1011

Towards De Novo Design of DeoxyribozymeBiosensors for GMO Detection

Elebeoba E. May, Member, IEEE, Patricia L. Dolan, Paul S. Crozier, Susan Brozik, and Monica Manginell

Abstract—Hybrid systems that provide a seamless interface be-tween nanoscale molecular events and microsystem technologiesenable the development of complex biological sensor systems thatnot only detect biomolecular threats, but also are able to determineand execute a programmed response to such threats. The chal-lenge is to move beyond the current paradigm of compartmental-izing detection, analysis, and interpretation into separate steps. Wepresent methods that will enable the de novo design and develop-ment of customizable biosensors that can exploit deoxyribozymecomputing (Stojanovic and Stefanovic, 2003) to concurrently per-form in vitro target detection, genetically modified organism detec-tion, and classification.

Index Terms—Avian influenza, biosensor, deoxyribozyme, errorcontrol codes, hybridization thermodynamics, molecular beacons,single nucleotide polymorphism.

I. INTRODUCTION

HYBRIDIZATION-BASED target recognition and dis-crimination is central to a wide variety of applications:

high throughput screening, distinguishing genetically modifiedorganisms (GMOs), molecular computing, differentiating bi-ological markers, fingerprinting a specific sensor response forcomplex systems, etc. The recognition substrate can exist insolution or be immobilized onto a transducer and hybridiza-tion events can be detected optically, electrochemically, orvia a mass-sensitive device [2]. The bioreceptor or probe iscritical to the specificity of the biosensor. Although severalsingle-stranded DNA sensor technologies, such as DNA mi-croarrays, are widely used, molecular beacon probes are highlysensitive and specific bioreceptors [2]–[4]. They can detectmutations in target sequences and can be multiplexed [3]; these

Manuscript received March 20, 2008; accepted March 21, 2008. Sandia is amultiprogram laboratory operated by Sandia Corporation, a Lockheed MartinCompany for the United States Department of Energys National Nuclear Secu-rity Administration under contract DEAC0494AL85000. This work was sup-ported by Sandia National Laboratories’ Laboratory Directed Research and De-velopment Program. E.E.M. and P.L.D. contributed equally to this work. Theassociate editor coordinating the review of this paper and approving it for pub-lication was Dr. Dennis Polla.

E. E. May is with the Department of Computational Biology, SandiaNational Laboratories, Albuquerque, NM 87185-1316 USA (e-mail:[email protected]).

P. L. Dolan, S. Brozik, and M. Manginell are with the Department ofBiosensors and Nanomaterials, Sandia National Laboratories, Albuquerque,NM 87185-0892 USA (e-mail: [email protected]; [email protected];[email protected]).

P. S. Crozier is with the Department of Multiscale Computational Materialsand Methods, Sandia National Laboratories, Albuquerque, NM 87185-1322USA (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSEN.2008.923945

properties make molecular beacons effective platforms fordetecting genetically modified targets in biodefense systems.

A. Deoxyribozyme Molecular Beacons

Molecular beacons are single-stranded oligonucleotideprobes that form stem-loop structures. The loop contains aprobe sequence that is complementary to a target sequence.Traditional molecular beacons contain a fluorophore andquencher on each arm of the stem of the beacon. Fluorescenceis achieved by separation of the fluorophore and quencher dueto a conformation change that takes place following targethybridization to the loop structure [5]. However, traditionalbeacons are limited by a 1:1 (target: signal) stoichiometry, andthe sensitivity of the detection is linked to the amount of targetpresent. Target amplification (PCR) is required to increase thelevel of sensitivity.

Though DNA serves primarily as a carrier of the geneticcode and no enzymes made of DNA have been found in nature,molecular beacons comprised of single-stranded DNAs canbe engineered to perform catalytic reactions similar to thoseof protein and RNA. Catalytic DNAs or deoxyribozymes aresynthesized in the laboratory via an in vitro iterative selectionprocess.

Like traditional molecular beacons, catalytic molecularbeacons also contain a stem-loop structure that undergoes aconformational change following target hybridization to theloop region. Unlike traditional molecular beacons, catalyticmolecular beacons are modular molecules, containing a de-oxyribozyme appended to the stem-loop structure [6]. Catalyticactivity is initiated by a target DNA sequence binding to theloop region that is distinct from the enzymatic active site. Thisallosterically activates the deoxyribozyme complex to bind andcleave a labeled substrate oligonucleotide molecule, producinga detectable signal. Once activated, the catalytic molecularbeacon will continuously cleave labeled substrate molecules.Target amplification is not required since signal amplificationis obtained through repeated processing of excess substratemolecules due to the recognition and binding of a single target.Single and multi-receptor site configurations enable the de-oxyribozyme to function as YES, NOT, or multi-input ANDgates [1].

Current high-throughput DNA sensing systems rely heavilyon silicon-based computing for interpretation of molecularrecognition events. Complex bioinformatics algorithms aretasked with de-noising and processing of sensor output sig-nals. This approach is error prone and not easily integratedinto emerging nanoscale sensor architectures for lab-on-chipsystems. Molecular computation, although impractical for realtime computing needs, offers the ability for massively parallel

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1012 IEEE SENSORS JOURNAL, VOL. 8, NO. 6, JUNE 2008

Fig. 1. �� (E6) Gate (DNA mfold server used) http://frontend.bioinfo.rpi.edu/applications/mfold/cgi-bin/dna-form1.cgi.

computation and detection, and is a promising technologybase for the development of next generation hybrid molecularsensor systems. In vitro computation using deoxyribozymegates enables the design of complex biosensor systems andreduces reliance on classical computing for interpretation ofsensor response.

In this work, we present methods for the development of adeoxyribozyme-based computational biosensor. In Section II,we demonstrate the sensing capability of deoxyribozymes andpresent quantitative methods for predicting the response ofthe deoxyribozyme to fully complementary and geneticallymodified target probes. Results are presented and analyzed inSection III. We conclude by demonstrating the use of our sensorplatform in the de novo design and development of biosensorsystems for detecting genetic modifications in avian influenzafollowed by a summary discussion.

II. MATERIALS AND METHODS

A. Deoxyribozyme Construction Assays

We constructed the (E6) deoxyribozyme gate (Fig. 1;[6]) based upon a previously reported modular design [5] thatappended a 15 nucleotide molecular recognition loop to the E6deoxyribozyme [7]. This one-input gate contains the E6 de-oxyribozyme, a ribonucleic acid phosphodiesterase, and a stem-loop structure attached to the 5 end of the enzymatic domain.When the structure is in its inactive form, the stem is comple-mentary-bound to the substrate recognition region and inhibitshydrolysis of a dual-labeled substrate oligonucleotide. Upon hy-bridization of a target (input) oligonucleotide complementaryto the loop sequence, the stem opens up and permits bindingand cleavage of the substrate at the site of an embedded ribonu-cleic acid (Fig. 2). Our substrate molecule is end-labeled with atetramethylrhodamine fluorophore (TAMRA) at the 5 terminusand a Black-Hole 2 quencher at the 3 terminus. TAMRAemission is quenched by distance-dependent fluorescence reso-nance energy transfer (FRET) to and upon cleavage andseparation of the two products, fluorescence increases.

Fig. 2. Schematic of deoxyribozyme gate catalysis.

Fluorogenic assays to determine the detection and single nu-cleotide polymorphism (SNP) differentiation capability of the

gate were conducted with the FluoDia T70 MicroplateReader using black 384-well microplates. For the detectionlimit experiments, a 55 detection volume containing the

gate (100 nM), the TAMRA-labeled substrate (2.5 ),and varying concentrations of input DNA in reaction buffer(HEPES pH 7.0, 1 M NaCl, 2 mM ) was used. Forthe SNP experiments, a 55 detection volume containingthe gate (100 nM), the TAMRA-labeled substrate(1.0 ), and input DNA (0.2 ) in reaction buffer was used.The estimation of substrate turnover assays were conducted in55 reaction buffer with the: 1) E6 deoxyribozyme (100 nM)and TAMRA-labeled substrate (2.5 ) or 2) gate(100 nM), the TAMRA-labeled substrate (1.0 ), and inputDNA (0.2 ).

1) Equipment, Oligonucleotides, Chemicals: All experi-ments were conducted with the FluoDia T70 Microplate Reader(Photon Technology International, Inc.) using black 384-wellmicroplates (OptiPlate-384 F, Perkin-Elmer). Oligonucleotideswere custom synthesized by Integrated DNA Technologies andpurified and reconstituted as follows.

1) The E6 deoxyribozyme was HPLC purified and stored as100 stock solutions in RNase/DNase/Protease-freewater (Acros Organics) at . E6 sequence [8]:5-CTCTTCAGCGATGGCGAAGCCCACCCATGTTAGTGA -3.

2) The deoxyribozyme gate was purified by denaturingPAGE and stored as 100 stock solutions inRNase/DNase/Protease-free water or reaction buffer(HEPES pH 7.0, 1 M NaCl, 2 mM ) at .

(E6) gate [8]: 5 -CTGAAGAGTATCCGAT-TATAAGCCTCTTC-AGCGATGGCGAAGCCCACCCATGTTAGTGA-3 .

3) The substrate molecule was dual-labeled with a tetram-ethylrhodamine fluorophore donor (TAMRA) at the5 terminus and a Black-Hole 2 quencher ac-ceptor at the 3 terminus. It was HPLC purified andstored as 250 stock solutions in RNase/DNase/Pro-tease-free water at . TAMRA substrate [8]:

.4) Input sequences were desalted and stored as 1 mM stock

solutions in reaction buffer at .


MAY et al.: TOWARDS DE NOVO DESIGN OF DEOXYRIBOZYME BIOSENSORS FOR GMO DETECTION 1013

2) Estimation of Substrate Cleavage and Product Formation:The following formula [5] was used to estimate the productformed at time in

(1)

where is the fluorescence emission at , is thesubstrate concentration at in , is the fluorescenceemission at time . The formula was derived from

(2)

where is the maximum fluorescence increase based on100% turnover [5].

B. Computational Methods for Modeling and Analysis ofDeoxyribozyme Gates

Biological sensor systems for defense applications must bespecific for a predefined threat, the emergence of which is noteasy to forecast. Therefore, the time from threat identificationto the development and deployment of the threat-specificbiosensor must be minimal. Rapid design and development ofde novo biosensor systems require computational modelingand simulation to increase accuracy and reduce the need forexperimental testing of each gate used in the system.

1) Using Hybridization Thermodynamic Data as a Predictorfor Ribozyme Fluorescence Measurements: Since empiricallycharacterizing the fluorescence signature of each gate can beslow and expensive, it is desirable to computationally predictthe outcome of the experimental measurements. Even if im-perfect, computational predictions can be useful as qualitativeguides, and can quickly and inexpensively yield large data setsfor screening purposes.

For a simple YES gate, since an excess amount of substrateis used, let us assume that each ribozyme molecule is eitherfully active or fully inactive so that the measured fluorescenceat a given time is directly proportional to the number of acti-vated ribozymes. Let us further assume that ribozyme activa-tion is directly related to the thermodynamics of the hybridiza-tion between the input molecules and the YES gate. Followingthis logic, there should be an observable correlation between theinput/gate hybridization thermodynamics and the measured flu-orescence. We demonstrate that this is indeed the case.

We have created an interface to automatically harvest raw hy-bridization thermodynamics data from the HyTher web server[9]–[11]. Once collected, we compared hybridization free en-ergy and melting temperature data with measured fluorescencedata for the gate (Fig. 7) and found reasonable correla-tion, as shown in Fig. 3. Improved correlation was obtained byadding corrections for single nucleotide polymorphisms (SNPs)near the 3 and 5 ends of the hybridized sequences (Fig. 4).

Given a set of measured fluorescence data, an empirical rela-tionship between the HyTher melting temperature data and themeasured fluorescence data can be created and used to infer flu-orescence data that has not been measured. The empirical rela-tionship that we have used here is simple but effective. First, the

Fig. 3. Correlation between HyTher melting temperature data measured fluo-rescence data. SNPs near the 5 and 3 ends are outliers.

Fig. 4. By using an empirical correction factor for the SNPs at the 5 and 3ends, much a better correlation between HyTher melting temperatures and flu-orescence measurements is obtained.

HyTher melting temperature is adjusted by empirically fit pa-rameters if the SNP is one or two base pairs from either the 3or 5 end

(3)

where if a SNP is present at the last base pair from the5 end and otherwise; if a SNP is present at thesecond-to-last base pair from the 5 end and 0, otherwise;if a SNP is present at the last base pair from the 3 end and 0,otherwise; if a SNP is present at the second-to-last basepair from the 3 end and 0, otherwise. The values areempirically fit parameters that are optimally chosen to give thebest fit between predicted and measured fluorescence outputsfor the given data set.

Initial guesses for the parameters are supplied to the algo-rithm for the first iteration of the fitting procedure. A leastsquares algorithm is then used to produce a straight line with



optimal correlation between the modified melt temperaturesand the measured fluorescence data. The slope and intercept ofthe drawn line become additional fit parameters in the followingequation:

(4)

such that the following difference is minimized:

(5)

After selection of the optimal slope and intercept, the first it-eration is complete, new parameters are chosen for calculationof the modified melting temperature calculation, and the seconditeration begins. Iteration continues until a convergence criteriais satisfied.

III. RESULTS AND ANALYSIS

A. Deoxyribozyme Gates as Biosensor Elements

To determine the viability of using deoxyribozyme gates asprobes for a sensor system and to demonstrate computationalelements based on deoxyribozymes, we constructed the(E6) deoxyribozyme gate (Fig. 1). The E6 deoxyribozymecleaves the phosphodiester bond of a chimeric substrateat the site of a single ribonucleotide (rA) embedded in themiddle of a deoxyribonucleotide molecule. It has a reported

of 13 and a turnover rate of 0.039 forhydrolysis of a 15-mer oligonucleotide substrate containing asingle ribonucleotide [7].

Stojanovic et al. [5], [12] demonstrated that when a single-stranded oligonucleotide substrate was dual-end labeled with afluorescein donor and a TAMRA acceptor, cleavage by the E6deoxyribozyme resulted in a tenfold increase (estimated sub-strate turnover) in fluorescein emission at 520 nm. The authorsfound that the degree of substrate turnover varied between 2 and30 with different substrates depending on their structures, aswell as variation due to interbatch differences within the samesubstrates [8], [12]. We performed assays of the E6 enzyme thatwere based on the hydrolysis of dual-labeled substrate molecule(2.5 ) with a tetramethylrhodamine donor (TAMRA) at the5 terminus and a Black-Hole 2 quencher acceptor at the3 terminus. Cleavage of substrate resulted in a 14-fold increasein TAMRA fluorescence at 590 nm (excitation at 530 nm) dueto separation of the fluorophore from the quencher (Fig. 5).

To determine the effect of adding a stem-loop structure to theE6 deoxyribozyme, we estimated the substrate turnover of the

(E6) deoxyribozyme gate (using (1)) with a full com-plement input sequence (Input A), SNPs 9, 23, and 36, andTAMRA-labeled substrate (1.0 ). The degree of substrateturnover decreased significantly, as shown in Table I and Fig. 5.The kinetics of this modulated bi-reactant (input and substrate)system warrants further investigation.

To verify the detection capability of the deoxyribozymegates, we performed fluorogenic assays with the(E6) gate. Using a 55 detection volume containing the

gate (100 nM) and the TAMRA-labeled substrate(2.5 ), our detection limit, resolvable after 3 min, wasbetween 1.25 femtomoles ( ; 22.7 pM) and

Fig. 5. Estimation of substrate turnover. [�� ; (E6deoxyribozyme (100 nM) and TAMRA substrate (2.5 ��)) or (�� gate(100 nM), TAMRA substrate (1.0 ��), Input A and SNPs (0.2 ��)).]

TABLE IESTIMATE OF SUBSTRATE TURNOVER

�� .� � �� at � � �.�� at � � � in ��.� � �� at time �.

Fig. 6. Fluorescence intensity of varying amounts of target DNA[�� , �� gate (100 nM), TAMRA substrate(2.5��)]. Detection limit of 500.5 attomoles to 1.25 femtomoles (9.1 22.7 pM).

500.5 attomoles ( ; 9.1 pM) of target DNA( ), as shown in Fig. 6. To determinehow effective the (E6) gate is with regard to its abilityto differentiate a perfectly complementary target sequence



Fig. 7. Fluorescence Intensity of 45 SNP sequences vs. full complement targetsequence [�� , �� gate (100 nM), TAMRA sub-strate (1.0 ��), Input A and SNPs (0.2 ��)]. Differences in fluorescence in-tensity between SNPs is statistically significant,� � �� (single-factorANOVA).

Fig. 8. Comparison between the predicted and measured output fluorescencevalues for InputA and 45 SNP-containing target sequences.

from sequences containing single base mutations, we compiledand compared experimental data for 45 single nucleotidepolymorphism (SNP)-containing target sequences (Fig. 7). The45 sequences represent all possible single base mutations foreach of the 15 positions in the target sequence. We were able todistinguish target from mismatched sequences within 3–5 min.The differences in fluorescence intensity between the SNPsis statistically significant, i.e., (single-factorANOVA).

1) Comparison of Predicted and Experimental FluorescenceMeasurements: Using methods discussed in Section II-B1, wederived an optimal parameter set for use in thermodynamics-based fluorescence prediction. For each of the 45 SNP-con-taining target sequences, we predicted an output fluorescencesignal. Fig. 8 shows a comparison between the predicted andmeasured fluorescence values. Optimization of empirical fit pa-rameters yielded further improvement in prediction capabilityup to a correlation coefficient of 0.8 when comparing predictedand measured fluorescence. Our simulation capability will aidin our de novo design process by filtering out in silico gates withundesired and nonrobust functionality.

B. Quantitative Analysis of Gate Response

The accuracy of hybridization-based biosensors depends onhow well we are able to determine if the “target” or a modi-fied form of the “target” is encountered based on the fluores-cence signature; where the terminology “target” is used in ageneral sense to include actual target organisms, as well as clas-sification categories. There are many statistical and non-sta-tistical quantitative tools and algorithms for de-noising, pro-cessing, and analyzing the output of nucleic acid sensors using aclassical computing platform. However, our objective is to limitthe amount of in silico postprocessing and perform concurrentdetection and classification of targets. We investigate alterna-tive analysis methods that produce algorithms realizable usingdeoxyribozyme gates.

As a point of reference, we consider the problem of classi-fying the 45 SNPs in the gate experiment (previouslydiscussed in Sections II-A and III-A). Using a traditional statis-tical approach, we constructed a two and three class Bayesianclassifier based on the output fluorescence data at an optimaltime point, . For the two class system, we desig-nate an output fluorescence as either belonging to a SNP in theupstream region (position 1–7) or the downstream region (po-sitions 8–15). The correct classification rate for this classifierwas 71.1%. The three class system designates a fluorescence asbelonging to the 5 end (base position 1 to base position 5), themiddle (base position 6 to base position 10), or the 3 end (baseposition 11 to base position 15). The correct classification ratefor this classification system was 62.2%. If we use the averagedifference between the full complement and SNP sequence asour classification variable, the correct classification rate for thetwo class system increases slightly to 73.3%. The correct clas-sification rate for SNPs in the middle and 3 ends increase forthe three class system to 66.7% and 93.3%, respectively, but theoverall correct classification rate decreases slightly to 60%. Al-though we are confident that better statistical classifiers can beconstructed, it would not result in an algorithm we can easilyimplement in vitro.

An alternative view of this classification problem is: howcan we algorithmically group sequences into correct or muta-tion/error-containing sets. Using ideas from the field of codingtheory, we can draw parallels between detection and classifi-cation of mishybridizations and detection and classification oferror vectors for error control codes [13].

1) Error Control Codes (ECCs) and Syndrome Generation:Error control codes (ECCs) are algorithms that recognize a setof target signals (binary bit streams in digital communications)and specific variations (errors) of the reference signal set [14].The reference signal set is called a codebook, , and is a matrixwhere each row corresponds to a codeword vector. The ECC isusually represented by a generator matrix, , which has a corre-sponding dual matrix , referred to as the parity check matrix.The relationship between the parity check matrix and the code-book is: , where represents multiplication,is the transpose of the parity check matrix, is the set of syn-drome values, and equals zero if there are no errors in thesequences in . Each vector, , in represents an error vector.Unique errors in codewords should produce unique error vec-tors until the number of errors exceeds the error detection limit



of the code. The transpose of is our syndrome generation al-gorithm; this can be used to detect and classify variations in . Ifthe codebook, , represents the sequence group we want to rec-ognize, then our objective is to develop methods for finding .

2) Syndrome Generator for SNP Classification: Using thesequence data from Fig. 7, we reverse-engineered an ECC syn-drome generation algorithm that can identify a target and SNPsof the target using computed syndromes. We pose the problemas follows: Find such that

where• is composed of the correct sequence and all SNP

variations of this sequence.• is the transpose of the dual matrix in systematic

form [14], [15].• The optimal maximizes the number of zeros in

, while minimizing the number of zeros in[14], [15].

We convert the input DNA sequence set (InputA plus 45SNPs) into their binary equivalence using a chemical ac-tivity-based mapping proposed by MacDonaill [16]. Onceconverted the binary coding rate is ( , ), whereeach DNA base is represented by a four bit binary sequence.This coding rate is equivalent to a rate 1/3 code, similar tothe degeneracy of the genetic code. The binary sequence setserves as inputs to a genetic algorithm based error control codeinverter for linear block codes [17]. The resulting syndromegeneration algorithm, , has a fitness of 0.8609, where1.0 is the maximum fitness. Using this code, we calculate thesyndrome for each of the 46 input sequences in Matlab.

The non-SNP input (InputA) produced an all zero syndrome,which in the coding theoretic sense indicates the absence of er-rors. The sequences with SNPs near the middle through 3 endsproduced unique syndrome vectors (Fig. 9 middle and bottomplots, corresponding to SNPs in position 6 to 15). SNPs in the5 end (Fig. 9, top plot) appear to have unique syndrome pat-terns, but they contain significantly more nonzero syndromevalues. Syndrome patterns for the middle and 3 groups sepa-rate into distinct groups, consistent with the experimentally ob-served clustering in Fig. 7. Also, consistent with these observa-tions, the syndrome patterns for sequences in the 5 SNP groupare dispersed throughout the syndrome vector window.

We analyzed the syndrome vectors based on their Hammingdistance. SNPs in the middle and 3 ends, in general, clusteredaround SNP base locations. If we omit the non-SNP sequence,the minimum hamming distance values for these SNPs corre-spond to SNPs that are co-located. Conversely, SNPs in positionone to five do not cluster according to SNP location.

The syndrome generation algorithm specified bycan be implemented using simple AND-NOT Boolean logicgates (combined to implement an exclusive-OR operation).Hence, it is feasible to implement using deoxyribozyme gatestructures. Syndrome vectors can be used to uniquely identifythe SNPs in the middle to 3 end and possibly the type ofmutation that occurred (usually the transition mutations havemore ones in their syndrome than transversion mutations).Given the consistency between experimental observations and

Fig. 9. Unique syndrome pattern can be used to identify location and type ofgenetic modification. (Top to bottom) Syndromes from SNPs in the 5 , middle,and 3 ends of the input probe sequence.

calculated syndrome vectors, this analysis can potentially beused to determine a minimal set of target SNP mutants wecan use to identify region-specific mutations, where regionindicates the location of the SNP (5 , middle, or 3 ). Methodsto directly implement for mutation classification arebeing explored. We have used this syndrome-based approach



Fig. 10. H5N1 �� (8-17) gate.

to develop a deoxyribozyme-compliant algorithm for in vitrotarget detection and classification of avian influenza into geo-graphical subtypes [18].

IV. TOWARDS De novo DESIGN OF DEOXYRIBOZYME

BIOSENSORS FOR GMO DETECTION

Using the methods described herein, we demonstrate the denovo design and implementation of a deoxyribozyme biosensorfor detection of key mutations in avian influenza.

A. Detecting Mutations in the HA and PB2 Proteins of AvianInfluenza

Since the outbreak of H5N1 avian influenza A in humans(Hong Kong, 1997), 319 confirmed cases and 192 deaths havebeen reported to World Health Organization (WHO) [19]. It iswidely held that the virulence of influenza A virus in differentorganisms is caused by multiple genes [20]. The genome of theH5N1 influenza virus consists of eight single-stranded RNAmolecules which encode ten proteins [21]. Two proteins thatare implicated in human virulence of H5N1 viruses are hemag-glutinin (HA) and polymerase Basic Protein 2 (PB2). Moni-toring HA and PB2 for the occurrence of mutations that increasehuman susceptibility to H5N1 is critical in the race to avert apandemic.

Hemagglutinin (HA) is the major surface protein of the viruswhich binds to sialic acid-containing receptors on host cells.Avian influenza A viruses recognize host receptors with sac-charides terminating in sialic acid 2,6-galactose (SAa2,6Gal),while human influenza viruses recognize host receptors termi-nating in SA 2,3-galactose (SAa2,3Gal) [11]. Any mutationthat might enable an avian H5N1 virus to bind to human-typereceptors would allow H5N1 viruses to replicate efficiently inhumans. Mutations at positions 182, i.e., asparagine (Asn) tolysine (Lys), and 192, i.e., glutamine (Gln) to arginine (Arg), inhemagglutinin independently convert host-cell recognition fromthe avian receptor to the human receptor [22].

Fig. 11. Hemagglutinin (HA) and polymerase PB2 target sequences.

Polymerase Basic Protein 2 (PB2) is one of three proteinscomprising the polymerase that replicates each of the eight viralgenomic RNAs in infected cells. A mutation at position 627, achange from glutamic acid (Glu) to lysine (Lys), allows efficientreplication in mammalian cells and is implicated in human vir-ulence [23].

We constructed a H5N1 AND (8-17) gate (a two-inputgate), (8-17), by appending two DNA recognitionloops to the 8-17 deoxyribozyme (Fig. 10) [24], [25]. The5 -end recognition loop binds a 15-base HA target sequencecontaining the implicated position 192 arginine codon, whilethe 3 -end recognition loop binds a 15-base PB2 target se-quence containing the implicated position 627 glutamic acidcodon (Fig. 11). Both the HA and PB2 target sequences wereobtained from the Influenza Sequence Database (ISD), H5N1strain A/Viet Nam/HN30408/2005 and H5N1 strain A/HongKong/156/97, respectively [26].



Fig. 12. Normalized fluorescence output for the H5N1 �� (8-17)gate.

To determine if the (8-17) gate could dif-ferentiate perfectly complementary normal target sequencesfrom sequences containing the HA and thePB2 mutations, either independently or con-currently, we compared experimental data for the followingsequence pairs.

• Normal HA/Normal PB2 Targets (Inputs HA/PB2).• Normal HA/Mutated PB2 Targets (HA/PB2 SNP23).• Normal PB2/Mutated HA Targets (PB2/HA SNP1).• Mutated HA/Mutated PB2 Targets (HA SNP1/PB2

SNP23).• Normal HA Target Only (Input HA Only).• Normal PB2 Target Only (Input PB2 Only).Background subtracted fluorescence rates were normalized

using the formula: , where isthe slope of the fluorescence intensities from 10–20 min (themost linear portion of the curve) for each experimental sampleand is the slope of fluorescence intensitiesof the fully complement HA and PB2 target sequences. Thus,the for the fully complement targetsequences is equal to 1. Results are shown in Fig. 12.

We were able to distinguish normal targets from mismatchedsequences, i.e., the greater the total number of mismatches, thelower the normalized fluorescence rates. When only one targetsequence is present, fluorescence rates are significantly lowerthan either normal or mismatched targets. We noted that the nor-malized fluorescence rate for the HA SNP1/normal PB2 targetpair was high, i.e., 0.94, compared with the rate for the PB2SNP23/normal HA target pair, i.e., 0.55. We are conducting fur-ther experiments to determine the reason for this.

V. CONCLUSION

We have developed methods that enable the de novo designand realization of a computational biosensor based on deoxyri-bozyme logic gates.Thebiosensorsystemisable todetectgeneticmodifications in the target and perform complex detection tasks,reducing reliance on classical computing for output interpreta-tion. The ability to compute differentiates this system from othermolecular beacon based systems. By increasing the complexityof the algorithm (or incorporating more computational wells),

we can potentially increase the sensitivity and specificity of thebiosensor, hence increasing accuracy by increasing computation.

We are investigating immobilization methods for electro-chemical detection of deoxyribozyme gate activity. The electro-chemical detection platform will produce differential currentsto indicate positive or negative algorithm outcomes. This willobviate our need for a fluorescence reader, thus permitting thedevelopment of a field deployable rapid detection system.

ACKNOWLEDGMENT

The authors would like to thank M. Finley (Sandia NationalLaboratories) for insights on avian influenza surveillance;M. Stojanovic (Columbia University), D. Stefanovic (Univer-sity of New Mexico), and J. Macdonald (Columbia University)for insights on deoxyribozyme gates; M. T. Lee (Universityof Pennsylvania School of Medicine) for contributions in thecomputational modeling aspects of this work.

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[18] E. E. May, P. L. Dolan, P. S. Crozier, S. M. Brozik, M. Manginell,R. Polsky, J. C. Harper, J. Rawlings, and M. T. Lee, DeoxyribozymeBiosensor: DNA-Based Intelligent Microsensors for Genetically Mod-ified Organisms (GMO). Albuquerque, NM: Sandia National Labo-ratories, Jan. 2008.

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Elebeoba E. May (S’99–M’02) received the Ph.D.degree in computer engineering from North CarolinaState University, Raleigh.

She joined Sandia National Laboratories’ Com-putational Biology Department in April 2002. Herresearch interests include the use and applicationof information theory, coding theory, and signalprocessing to the analysis of genetic regulatorymechanisms, the design and development of intel-ligent biosensors, and large-scale simulation andanalysis of biological pathways and systems.

Dr. May has served as an Associate Editor and Reviewer for the IEEETRANSACTIONS ON INFORMATION TECHNOLOGY in Biomedicine and as aGuest Editor for the IEEE Engineering in Medicine and Biology Magazine forthe Special Issue on Communication Theory, Coding Theory, and MolecularBiology. She is the recipient of a 2005 NIH/NHLBI Mentored QuantitativeResearch Career Development Award and the 2003 Women of Color ResearchSciences and Technology Awards for Outstanding Young Scientist or Engineer.Organizational memberships include the IEEE Engineering in Medicine andBiology Society, the IEEE Signal Processing Society, and the IEEE InformationTheory Society.

Patricia L. Dolan received the Ph.D. degree inbiology from the University of New Mexico,Albuquerque.

She joined Sandia National Laboratories’ Biosen-sors and Nanomaterials Department in January 2003.Her research interests include development of chem-ical and biological microsensors, biofilm detectionand control in membrane-based water treatment sys-tems, and protein engineering and biocatalysis for en-hanced enzyme function. As a member of a multidis-ciplinary team, she engineered a more active glucose

oxidase enzyme for use in a glucose-fueled bio micro fuel cell.Dr. Dolan was the recipient of a 2005 Employee Recognition Award for Ex-

ceptional Teamwork and Creativity in developing new technologies for sustain-able micro scale power sources powered by carbohydrate fuels. She is a memberof the American Society for Microbiology.

Paul S. Crozier received the Ph.D. degree in chem-ical engineering from Brigham Young University,Provo, UT, in 2001, his dissertation on slab geometrymolecular dynamics simulations.

For the past seven years, he has been working onmolecular dynamics and related code developmentand applications projects at Sandia National Lab-oratories, where he is currently a Senior Memberof Technical Staff. He is one of the developers ofthe open-source parallel molecular dynamics code,LAMMPS, and served as the principal investigator of

a code development project aimed at acceleration of biomolecular simulation.His research interests include biomolecular and materials science simulationapplications, as well as molecular simulation methods development.

Susan Brozik received the Ph.D. degree in analytical chemistry from Wash-ington State University, Pullman, WA, in 1994.

She held postdoctoral research positions in biochemistry at Emory Universityand Los Alamos National Laboratory before joining Sandia National Laborato-ries in 1999, where she is currently a Principal Member of Technical Staff. Hercurrent research interests include development of sensitive chemical and bio-logical sensors applicable to CW/BW agent detection and medical diagnostics.Sensor technologies include electrochemical, acoustic, optical, and cell-basedsensing. This work encompasses development of nano and microelectrode ar-rays, genetic engineering of reporter cell lines, and development of microflu-idic measurement platforms. Other activities include the generation of novelbio-inspired materials through gene engineering, assembly of chaperonin reac-tors for synthesis of nanomaterials, and development of catalytic nanoparticlesfor signal amplification and label free detection.

Monica Manginell received the B.S. degree in biology from the University ofNew Mexico, Albuquerque, in 1994.

She joined Sandia National Laboratories in 1997. Her research interestsinclude genetic engineering of cell lines for detection of chemical and biolog-ical agents and design of novel in vivo cellular recognition and amplificationschemes. Other activities include biocatalysis and gene engineering for en-hanced enzyme function, and development of miniaturized high throughputmicrofluidic platforms for protein assays.


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