LETTER pubs.acs.org/NanoLett
Detection of Multiple Disease Indicators by an Autonomous Biomolecular Computer Binyamin Gil,†,§ Maya Kahan-Hanum,†,§ Natalia Skirtenko,† Rivka Adar,† and Ehud Shapiro*,†,‡ †
Department of Biological Chemistry and ‡Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel
bS Supporting Information ABSTRACT: The promise of biomolecular computers is their ability to interact with naturally occurring biomolecules, enabling in the future the development of context-dependent programmable drugs. Here we show a context-sensing mechanism of a biomolecular automaton that can simultaneously sense different types of molecules, allowing future integration of biomedical knowledge on a broad range of molecular disease symptoms in the decision of a biomolecular computer to release a drug molecule. KEYWORDS: Biomolecular computing, automata, programmable drug, aptamer, disease indicator
I
nitially, biomolecular computers have been developed to solve hard computational problems by combinatorially generating all of the possible solutions and extracting the correct one by biochemical means.18 It was believed that parallel biomolecular computing exhibiting high pace, low energy consumption and compact information storage capacity could outperform electronic computers. Later it became clear that the ability of biomolecular computers to interact with naturally occurring biomolecules is much more important than their computational power.916 By this approach biochemical processes were harnessed to implement programmable autonomous biomolecular computing devices that realize generic mathematical models of computation. It is hoped that the combination of programmable logic, nanometric size, autonomous operation, and the ability to interact with the biological environment may facilitate biomedical applications. Initial attempts at this field included conceptual and experimental realizations of simple Boolean logic gates,1723 finite automata,4,6,7,24 Turing machines,2527 logic programming,28 and logic circuits.10,29,30 Several publications have shown molecular computation in a biologically relevant context. This was done by designing single purpose2023,31 or generic4,9 computing devices that can compute in response to the presence of biological molecules. Generic computing devices can be adapted to meet different specifications without changing their core mechanism. Further developments in the field made possible autonomous molecular computation inside living environments. These include a RNAi based logic evaluator that operated in living cells11 and was later integrated with an mRNA sensing mechanism that was shown to operate in cell lysate,12 a molecular sensor for the detection of almost any small molecule, based on aptamer-modified shRNA,32 and others.3238 Currently, the most important challenge of molecular computing is to develop programmable, autonomous molecular systems to control drug release, based on the molecular composition of the r 2011 American Chemical Society
microenvironment in which the disease is manifested. These systems may have the ability to logically analyze the biological microenvironment and activate or administer the requisite drug upon positive diagnosis of a disease.38,39 Our lab has shown a molecular computer that performs these operations in vitro.9 It implements an autonomous and programmable two-state stochastic automaton (Figure 1a) made of DNA and a restriction enzyme. This biomolecular computer logically analyzes mRNA expression levels and mutations as disease indicators (DIs) and upon positive disease diagnosis releases, in vitro, an active single-stranded (ss) DNA molecule that can be used in living cells as a drug.9 The automaton has two internal states, termed Yes and No. The computation starts from a Yes state, and checks DIs one by one. If a DI is present, a positive transition (from Yes to Yes) is performed; otherwise a negative transition (from Yes to No) is performed. If the automaton moved to the No state, oblivious transitions will occur (from No to No). The automaton determines positive diagnosis if it ends the computation in a Yes state and negative diagnosis otherwise. The molecular implementation of the automaton is comprised of double-stranded (ds) DNA with 4-nucleotides (nt) overhangs and restriction enzyme FokI. One type of dsDNA molecule, called a state indicating molecule (SIM), represents the automaton’s state (Yes or No) by the frame in which it is restricted. Each of the transition types mentioned above: positive, negative, and oblivious, is implemented by a corresponding transition molecule. A transition molecule (TM) contains the recognition (binding) site of the restriction enzyme FokI and it facilitates the correct SIM restriction by positioning the restriction site of FokI in the proper frame. FokI is a classIIs restriction enzyme that Received: May 11, 2011 Revised: June 9, 2011 Published: June 14, 2011 2989
dx.doi.org/10.1021/nl2015872 | Nano Lett. 2011, 11, 2989–2996
Nano Letters
LETTER
Figure 1. Automaton operation. (a) Diagnostic automaton scheme. The automaton starts in the Yes state and one DI is examined in each computation step. If the DI is present, the automaton performs a Positive transition, thus remaining in the Yes state. If the DI is absent the automaton executes a Negative transition, i.e., it moves to the No state. In this state the automaton ignores the rest of the DIs and performs Oblivious transitions, which maintain the No state. When the computation ends, the last state indicates whether the diagnosis is positive or negative. (b) New transition molecules design and one step computation animation. Darklight coloring represents sequence complementary, brown blocks represent FokI binding site, blue blocks represent the sequence of computation step n, green blocks represent the sequence of computation step n + 1, orange blocks represent 2-nt arbitrary sequence and scissors represent FokI restriction site (+13-nt and +9-nt on the sense and antisense strands, downstream of the binding site, respectively). The left scheme describes the realization of a Positive transition: the presence of the orange 2-nt in the antisense strand of the positive TM facilitate greater distance between FokI recognition site and the SIM, resulting in a restriction that maintains the Yes state. The SIM’s toe hold represents the current state of the automaton. A TM with the complementary sticky end hybridizes to the SIM and consequentially FokI restricts. The reaction products are (from left to right): reusable positive TM, waste molecule which was restricted from the SIM, and a shorter SIM representing the Yes state, ready for the next computation step. The right panel describes the realization of a Negative transition. In this case the orange 2-nt in the antisense strand are absent, causing the complementary 2-nt be idle. Thus, the negative TM positions FokI binding site closer to the SIM, resulting in a restriction that would change the state from Yes to No. After the sticky ends hybridize FokI restricts and the reaction products are (from left to right): reusable negative TM, waste molecule (restricted from the SIM), and a shorter SIM representing the No state, ready for the next computation step. (c) Denaturating PAGE analysis of a representative one step computation. The leftmost lane contains only the SIM. The rest of the lanes contain also FokI and a composition of positive TM and negative TM in different concentrations as indicated above. The two possible (labeled) restriction products are indicated. (d) Quantification of the restriction products. Pixel counts of the Yes state and the No state were used to calculate the final output, as a percentage of total count. Output percentage represents the positive (green) and negative (red) diagnosis.
restricts between 9- and 13-nt downstream of its recognition site. Thus, upon TM and SIM hybridization via overhang complementarity, FokI can bind the TM and cleave within the SIM, enabling the next computation step to take place (Figure 1b,c). The result, indicated by the state of the SIM at the end of the computation, is retrieved from the final length of the SIM (Figure 1d). Detection of mRNA DIs was done by designing the TMs such that mRNA could regulate their concentrations. When the DI was an overexpressed mRNA (mRNAv) the system initially included active negative TMs and inactive positive TMs. Thus, with normal mRNA levels the automaton’s state would be
changed from Yes to No. In the presence of overexpressed mRNA, positive TMs were activated and negative TMs inactivated resulting in higher amount of positive TMs relative to negative TMs. In this case, the automaton would remain in the Yes state. When the DI was an underexpressed mRNA (mRNAV) the system initially included active positive TMs and inactive negative TMs. In the presence of the detected mRNA, negative TMs were activated and positive TMs inactivated, resulting in higher amounts of negative TMs compared to positive ones. This regulation mechanism was performed by two parallel strand displacement processes. For example, to detect mRNAv, two segments (tags) of the mRNA molecule, approximately 2990
dx.doi.org/10.1021/nl2015872 |Nano Lett. 2011, 11, 2989–2996
Nano Letters
LETTER
Figure 2. Transition regulation mechanisms. (a) DIv detection. Coloring scheme, as in Figure 1. DI could be either a mRNA molecule, miRNA molecule, or an aptamer target. When the DI is absent, only the negative TM is active. The negative TM antisense (lower) strand contains the regulatory segment (light purple) that has affinity to the DI. When the DI is present, it binds the regulatory segment and displaces the sense (upper) strand, resulting in negative TM inactivation. The released sense strand is then free to assemble the positive TM with its antisense strand, which is present in the solution. (b) DIV detection. A similar design, but with the regulatory segment on the positive TM, is used to detect DIV. Here the same displacement and assembly processes are designed to have the opposite effect. Namely, the positive TM is inactivated while the negative TM is activated when the DI is present. (c) Detection of miR21v and (d) detection of miR31V. Quantification of one step computation results. Increasing amounts of DI (miR21), or decreasing amounts of DI (miR31), affect computation result such that %Yes or %No is increasing in the range of 01 μM miR21 or miR31, respectively. (e, f) Control over the detection range of miR21v and miR31V, respectively. When an excess of the strand that contains the regulatory segment is applied, the automaton’s range of sensitivity to the detected DI is changing accordingly.
20-nt long, were chosen. Complementary sequences were designed to be part of the TM structure such that displacement reactions could regulate TM concentrations. The initially active negative TM was designed to be inactivated by the displacement reaction with one of the mRNA tags, which removes one of its strands. The initially inactive positive TM was designed to be activated by self-assembly after the removal of an inhibitory strand by a displacement reaction with the second mRNA tag.9 Since molecular indicators of cancer and other diseases are diverse, we strived to expand the automaton detection capabilities to include other types of molecules. For instance, expression profiles for several miRNA species are related to cancer initiation and progression and were successfully shown to classify different types of tumors.4048 Furthermore, miRNA characteristics, namely, its simple secondary structure, high expression levels, and biological activity as single-stranded molecule that hybridizes with complementary sequences, make it more suitable as DI, compared to mRNA. Therefore, a new sensing mechanism that facilitates the detection of nucleic acid oligomers shorter than 40nt (the lower limit of the previous system) was integrated into the automaton’s previous design. By this design a single interaction with a DI is sufficient to regulate competing positive and negative TMs’ concentrations (Supplementary Figure 1 in the Supporting Information). This improved design requires fewer DNA strands
for its construction and involves fewer interactions with the disease-indicating molecules. Moreover, this design also enables the detection of proteins or small molecules for which aptamers exist. Aptamers are short synthetic ssDNA or RNA sequences selected in vitro for their ability to bind specific molecular targets by systematic evolution of ligands by exponential enrichment technique, i.e., SELEX.49 Aptamers were shown to bind a variety of targets ranging from small molecules through membrane and soluble proteins, to viruses, cells, and even whole organisms.50,51 As the activity level of a protein could be more significant than its concentration, in particular with DNA binding proteins (DBPs) like transcription factors (a subgroup of DBPs), we demonstrate here the manner by which our novel sensing mechanism can be used for detecting the activity level of DBPs. Results. The new DI detection mechanism, depicted in panels b and c of Figure 1, comprises positive and negative TMs that share a sense (upper) strand. However, positive and negative TMs differ in their antisense (lower) strand that facilitates their functionality. The positive TM’s lower strand contains two additional nucleotides that are absent in the negative TM. Their common upper strand contains a 2-nt sequence which is complementary to the 2-nt sequence in the positive TM, a segment which is idle in the negative TM (Figure 1b, right panel). Figure 1b depicts the realization of one computation step in 2991
dx.doi.org/10.1021/nl2015872 |Nano Lett. 2011, 11, 2989–2996
Nano Letters which a positive (left panel) or negative (right panel) transition is performed. In both cases, upon hybridization between the TM and a SIM, FokI restricts within the fluorescently labeled SIM. The specific restriction positions are determined by the TM structure: a positive TM that contains the additional 2-nt fragment will implement a positive transition (Yes to Yes, Figure 1b, left panel) while the negative TM will change the state to No (Figure 1b, right panel). The reaction products are shorter SIMs, ready for the next computation step, reusable TMs and FokI, and waste DNA molecules. To validate this design, a calibration experiment was performed and its results were analyzed by denaturating PAGE (Figure 1c,d). In this experiment the fluorescently labeled SIM was mixed with varying ratios of positive TM and negative TM (constructed by the new design). Upon the performance of one computation step, the results obtained were as expected. When only positive TM was added (second lane from the left), the SIM final length was 2-nt longer than the final length in the case where only negative TM was added (rightmost lane). Different ratios of positive TM and negative TM in the reaction mixtures were reflected by the computation output as demonstrated for our stochastic automaton.7 Detection of different DI types was made possible by allowing each DI to specifically regulate the ratio of its corresponding positive and negative TMs. The new TM design includes one “regulatory segment” that can bind the detected DI. This segment is located in the antisense strand of the negative TM for the detection of DIv (Figure 2a) and in the antisense strand of the positive TM for the detection of DIV (Figure 2b). For the detection of short nucleic acids or mRNAs, for which only a single 20-nt tag is required, the regulatory segment is composed of their complementary sequence. For the detection of proteins, small molecules, and other DIs for which an aptamer exists, the regulatory segment contains the sequence of the aptamer. In the regulatory mechanism illustrated in Figure 2a, the antisense strand of the negative TM contains the regulatory segment (pink), which can specifically and with high affinity bind the requested DI. The sense strand of this transition (purple) is only partially complementary to the regulatory segment; thus DI binding and strand displacement are favorable. The antisense strand of the opposing positive TM, which is initially inactive, is present in the computation reaction but cannot facilitate any restriction. In the presence of a DI, the antisense strand of the positive TM is displaced, thus inactivating it. The sense strand, which is common to negative TM and positive TM, is then free to anneal with the antisense strand of the positive TM, resulting in its activation. For the detection of DIV (Figure 2b), the same design principles apply. However, in this case, the antisense strand of the positive TM contains the regulatory segment and the opposing negative TM is initially inactive. Similar DI binding and displacement mechanisms inactivate the positive TM while enabling the self-assembly of the negative TM. miRNA Detection. To demonstrate the detection of miRNA DIs, two computers were designed for miR21v and miR31V. For each computation, increasing amounts of DI were added and the computational results were quantified. Negative diagnosis for miR21v (90% No) was detected at 0 μM, while positive diagnosis (90% Yes) was detected at 2 μM (Figure 2c). Similarly, positive diagnosis for miR31V (90% Yes) was detected at 0 μM, while negative diagnosis (85% No) was detected at 1 μM (Figure 2d). We note that the original automaton included a simple method for overcoming false positive and false negative results, by varying
LETTER
Figure 3. ATPv detection. (a) All reactions contain FokI, one step SIM and TMs (active negative TM and inactive positive TM, as illustrated in Figure 2a). Increasing ATP concentrations (as indicated above) is correlated in the computation results (Yes/No ratio). The right gel demonstrates the selectivity of the aptamer, by comparing the ability of the computer to detect ATP versus its ability to detect dNTPs, as depicted above each lane. TDA50 is a ssDNA oligo complementary to the aptamer. Strong affinity of the aptamer to its complementary sequence is reflected by the positive diagnosis that occurred when TDA50 was added (left lane). No affinity of the aptamer to dCTP, dGTP, and dTTP is reflected by negative diagnosis when these molecules are added (three rightmost lanes), while moderate affinity to dATP, which is similar in structure to ATP, could be seen (dATP lane). (b) Quantification of the results. Almost 20% false positive and false negative are observed, probably due to biochemicaly imperfect reactions. The automaton mechanism could cope with such results by technical means.9
the ratio between the drug carrying SIM and the drug-suppressor carrying SIM.9 Therefore, a significant difference between positive and negative diagnosis is sufficient to produce the correct molecular output, in the complete system.9 In this work healthy or disease-indicating concentrations of DIv or DIV, respectively, were set to be zero. However, frequently the actual DI basal expression levels are higher. To cope with this difficulty, we have utilized a mechanism enabling the automaton to refer to the DIs basal expression levels as zero. This was demonstrated for miRNA DIs by adding an excess of the antisense strand of the positive TM for miRNAV or negative TM for miRNAv (Figure 2e,f). For example, when 2 μM excess of the negative TM antisense strand was used for the detection of miR21v, the detection range in which the computational output changes from 90% No to 90% Yes, changed from 02 μM miR21 to 1.54 μM miR21 (Figure 2e). ATP Detection. To demonstrate the detection of small molecules, a one-step computation for the detection of ATPv is shown in Figure 3. The computation mixture contains SIM, restriction enzyme FokI, active negative TM, inactive positive 2992
dx.doi.org/10.1021/nl2015872 |Nano Lett. 2011, 11, 2989–2996
Nano Letters
LETTER
Figure 4. (a) DBP detection mechanism. Coloring scheme for the negative TM and positive TM is as depicted in Figures 1 and 2. The additional stem loop molecule contains a HgaI binding site (brown blocks surrounded by red square) and DBP binding site (purple blocks surrounded by red square). The HgaI restriction site is overlapping the DBP binding site, thus restriction is possible only in the absence of active DBP (otherwise a steric hindrance inhibit the restriction). Upon stem cleavage a ssDNA strand (purple blocks) is released and is free to interact with the positive TM’s regulatory segment, resulting in strand displacement, positive TM inactivation, and negative TM activation. (b) Detection of p50v. The presence of p50 is sensed when 4.6 gsu are added, but not in the presence of 2.3 gsu or less (gsu stands for gel shift units, reflecting the active portion of the protein). The presence of p50 was also simulated by excluding the restriction enzyme HgaI from the reaction (leftmost lane).
TM, and varying amounts of ATP. The regulatory segment, in this case, has the sequence of an aptamer that binds ATP; thus, ATP may regulate TMs’ concentrations. Additionally, the specificity of this detection mechanism was examined by the addition of dNTPs or ssDNA molecule that has the aptamer’s complementary sequence (TDA50) instead of ATP. The automaton’s output, analyzed by denaturating PAGE was as expected (Figure 3a). Quantification of PAGE results (Figure 3b), shows approximately 75% positive diagnosis in the presence of 2.5 mM ATP, or 2.5 μM ssDNA complementary to the aptamer sequence. Approximately 60% Yes was observed with 2.5 mM dATP and less than 20% Yes was observed in the presence of 2.5 mM dCTP, dGTP, and dTTP. In agreement with previous observations,52 our findings demonstrate that the aptamer (and hence the automaton) is highly specific to ATP and to some extent to dATP, but not to other dNTPs. DBP detection. Occasionally, detection of protein activity might be much more important than its sheer expression level. For example, the activity of DBPs and especially transcription factors might present a better indication of the cell’s condition than their expression levels. For this purpose an additional detection mechanism was developed. This detection system is based on the same molecular design described above and on the “DNA footprinting” principle, by which restriction of DNA could be inhibited by DBP, as shown in Figure 4a. Here, an additional molecule, ssDNA with a stem-loop structure is introduced into the system. The stem part of this molecule contains the binding site of a second class IIs restriction enzyme HgaI (red square around the brown blocks), and the DBP binding site (red square around the purple blocks). HgaI’s restriction site is designed to overlap with DBP binding site. The loop segment (purple) contains a sequence that, upon stem restriction, regulates opposing TMs’ concentrations, acting as a DI (Figure 4a). When the tested DBP is present, it binds to its recognition sequence on the stem and sterically hinders HgaI restriction. Since the stem segment can be restricted only in the absence of the target DBP, this detection mechanism actually realizes a translator that converts DBP lack of activity to an arbitrary ssDNA molecule, which acts as a DI. After loop length and relative position of the stem components had been calibrated, this mechanism was evaluated for the
detection of p50 (subunit of transcription factor NFk-B) activity. One step computation for the detection of p50 activity (p50v) is shown in Figure 4b. The computational result is Yes when active p50 is present and No when it is absent. Positive diagnosis occurred as well upon HgaI depletion, which simulates p50 maximal activity (Figure 4b, leftmost lane). Mechanism specificity was also tested with a stem-loop molecule containing mutations in the p50 binding site; in this negative control the stem was restricted and TMs were regulated accordingly regardless of p50 presence, as expected (Supplementary Figure 2 in the Supporting Information). Multiple DIs. The ability of our molecular computer to detect different DI types was evaluated by designing an automaton that checks a single DI for each of the following: ATP, human clothing factor R-thrombin (TB), NFk-B subunit (p50), mRNA molecule (Xef, data not shown), and several miRNA species. To demonstrate the modularity of this system, several automata were constructed for the detection of multiple input species. Specifically, Figure 5 presents the computational results of the molecular automata that check the following combined conditions on DIs: miR21v&miR200bV&miR31V&Let7av, TBV&ATPV and XefV&miR31V&TBV. An illustration of the four step molecular computation process (Figure 5a) appears in Supplementary Figure S3 (Supporting Information). The output quantifications confirmed that the computation process significantly evaluated the presence/absence of the diagnosed disease, represented by the DIs’ combination (Pv < 0.001). Discussion. Over the last two decades, data about molecular signatures of diseases are constantly expanding. These molecular signatures include genetic and epigenetic markers, changes in gene expression, and proteins and metabolites profiles. This knowledge demands new genomic, proteomic, and metabolic tools able to detect such molecular signatures and identify the most accurate combination for the detection of the specific disease. Thus far most of the diagnostic tools are either high throughput devices not applicable for in vivo operation,5356 or molecular devices that can detect only a single type of DI but might have the ability to operate in vivo.57 Here we show a novel detection mechanism for a variety of DIs, which was integrated into our previously described biomolecular computer. Whereas the previous automaton could only detect mRNA DIs, the improved version presented here can 2993
dx.doi.org/10.1021/nl2015872 |Nano Lett. 2011, 11, 2989–2996
Nano Letters
LETTER
Figure 5. Diagnostic computation with a variety of inputs. Each diagnostic computation was performed with all DIs present (lane 1, positive diagnosis) or with one or more DIs excluded (remaining lanes) as indicated above each gel. The percentages of the Yes outputs (out of the total output) were quantified and are presented in the graphs below each gel. Standard deviation errors from at least three independent experiments are shown (bars). Results that are significantly different (Pv < 0.001) from the Yes result (*) are marked (**). (a) Four-step computation for the detection of four miRNA species (miR21v&miR200bV&miR31V&Let7av). b) Two-steps computation for the detection of a small molecule and a protein (ATPV& TBV). c) Threesteps computation for the detection of mRNA, miRNA and a protein (XefV&miR31V&TBV).
detect mRNAs, miRNAs, proteins, and small molecules such as ATP. Moreover, the new detection design is simpler; i.e., it comprises fewer components and requires fewer interactions with the target DI. Although simple aptamer-based DI detectors have been reported,52,58 and techniques for single miRNA detection were shown,57 each could detect only one target type at a time, usually producing fluorescence increase as the output. Although identification of each component separately is of great importance to research and medical applications, sensing a combination of several DIs is even much more important since it allows better accuracy and greater sensitivity to differences between diseases. Relying on several DI types may be less prone to mistakes and mechanism-related biases or artifacts. For example, in the case of thyroid cancer,59,60 detecting of thyroglobulinv (protein) and calcitoninv (hormone) may be much more accurate and reliable than identifying only one type of DI. Another related approach suggests that a “single purpose” computer2023 could be designed and constructed for each combination of DIs. Although this approach is not generic and may require an investment in implementation and calibration for each device, the resulting devices may sometimes be simpler and/or more efficient than generic devices. Our recent study is an advance toward the long-term vision of programmable drugs.3538 Yet, for ultimate application many obstacles, such as delivery of the drug and its biodegradability,
should be overcome. Fields of study capable of assisting in these tasks include protein engineering, development of new delivery reagents, nucleic acid modifications, and drug discovery. Nevertheless, even before in vivo applications are developed, further development of such an integrative system to operate ex vivo could establish an outstanding research tool. For example, it could be harnessed to study relations and regulation of different levels of expression. Materials and Methods. Design of the automata components: The SIM design was taken from a previous work, where it was named “diagnostic molecule”.9 There, a computer program generated all the sequences for the molecules comprising the automaton. In short, the program generated random sequences of 7-nt each (symbols), one for every DI. The set of symbol sequences was then improved using a genetic algorithm. Each 4-nt sticky end was constrained to have 75% CG content. The algorithm rendered sequences with minimal partial complementarity between nonrelated sticky ends. A similar algorithm was used to produce sticky ends with only 50% CG content for some of the computations (Supplementary Table 1, Supporting Information). In this work, TM design was done manually. For each symbol (one computation step in the SIM), a set of two competing TMs (positive and negative) were constructed out of three strands: a common sense strand and two antisense strands correlating to positive TM and negative TM. The sequences of all DNA and RNA molecules involved in the computation can be found in 2994
dx.doi.org/10.1021/nl2015872 |Nano Lett. 2011, 11, 2989–2996
Nano Letters Supplementary Table 1 (Supporting Information). For DIV symbols, the positive TM’s antisense strand contained (30 f 50 ): a regulatory segment (ssDNA sequence with high affinity to the target DI, e.g., aptamer sequence), FokI binding site, 2-nt (arbitrary sequence), and 4-nt sticky end complementary to the Yes state of the corresponding symbol in the SIM. The negative TM antisense strand was designed to be analogous, but with two significant differences: (a) the sequence complementary to the target DI is truncated in the 30 and (b) the two arbitrary nucleotides were removed. For DIv symbols, the negative TM antisense strand contained (30 f 50 ): a regulatory segment, FokI binding site and 4-nt sticky end complementary to the Yes state of the corresponding symbol. The positive TM antisense strand was designed to be analogous, but with two significant differences: (a) the sequence complementary to the target DI is truncated in the 30 and (b) 2-nt (arbitrary sequence) were added between FokI binding site and the 4-nt sticky end. Practically, the 2-nt arbitrary sequence had one constrain: not to be complementary to the transitions’ sticky end. For DBP detection the regulatory segment was designed to be complementary to the loop part of an additional stemloop molecule. The stem part contains the binding sites of the DBP and of a class IIs restriction enzyme (HgaI), such that it is restriction site is within the DBP binding site. Automata components were constructed from HPLC purified, labeled and nonlabeled deoxyribonucleotides (oligos) and ribonucleotides, ordered from Integrated DNA Technologies, Inc. (IDT). Each oligonucleotide was dissolved in 10 mM TrisEDTA pH = 8.0 (TE) to a final concentration of 1 mM. Double-stranded components were annealed by mixing 1000 pmol of each single strand in 10 μL of TE containing 50 mM NaCl and heating to 99 °C for 5 min and then slowly cooling down to 10 °C in a PCR machine block (Supplementary Table 1, Supporting Information). Synthetic ribonucleotides were used to realize mature miRNAs. HgaI (2 u/μL) and FokI stock (60 u/μL = 54 μM) were obtained from New England Biolabs. ATP (adenosine 50 -triphosphate disodium salt hydrate, BioXtra, g99%, from microbial) and dNTPs (DNTP100, deoxynucleotide set, 100 mM) were purchased from Sigma-Genosys. Human Thrombin (HCT-0020, 8.9 mg/mL) was ordered from Haematologic Technologies. NF-kB subunit, p50 (rhNF-kappaB, E3770, 4.6 gsu/μL) was purchased from Promega. The given p50 concentration, gsu (gel shift units), represents the active portion of the protein while the actual protein concentration is 0.23 mg/mL. Xef mRNA was transcribed from the pTRI-Xef 1 (∼1900 bp) DNA template, which is the control DNA supplied with MEGAScript T7 kit (Ambion, AM1333). Calibration for one symbol automaton was performed by preparing a reaction mixture that contains all computer components except restriction enzyme FokI (New England Biolabs) in 8 μL of NEB4 reaction buffer (New England Biolabs) as described before.9 The reaction mixture contains competing TMs in varying ratios at a total concentration of 1 μM, at 23 °C. Computation is initiated by adding 2 μL of FokI enzyme (diluted in double distilled water to a final concentration of 1 μM) and further incubating for 10 min. The reaction is terminated by the addition of 10 μL of formamide (ABI) and heating to 95 °C for at least 5 min. Samples were analyzed by 20% PAGE in denaturating conditions (7 M urea). The gel was scanned with a fluorescent scanner (Typhoon 9700 scanner, Amersham Pharmacia Biosciences) and quantified using the IMAGEQUANT V5.0 software (Molecular Dynamics). In this assay,
LETTER
Yes and No outputs are represented by 23-nt and 21-nt bands, respectively. The percentage of positive diagnosis is the percentage of Yes state out of the entire output bands in the end of the computation. The two upper bands are attributed to the SIM before it was cleaved, probably due to impurity of the molecule. Diagnostic computations consist of three steps: (1) mixing the automata component; (2) incubating each automaton with the model DIs; and (3) initiating computation by adding the computer’s hardware (restriction enzyme FokI). Specific compositions and incubation temperatures for the three combination experiments (Figure 5) can be found in Supplementary Tables 24 (Supporting Information) (miR21v&miR200bV&miR31V&Let7av diagnosis, ATPV&ThrombinV diagnosis and XefV&miR31V &ThrombinV diagnosis, respectively). In general, experiments were performed as follows: Initially all components, excluding FokI, are mixed in NEB4 reaction buffer to a total volume of 8 μL. Each DI was added at either zero (normal state for DIv and disease state for DIV) or 2.5 μM for miRNA, 1 μM for mRNA, 1 mM for ATP, or 0.09 mg/mL for Thrombin (normal state for DIV and disease state for DIv). The reaction mixture is thoroughly mixed and incubated as indicated in the supplementary tables (Supporting Information). Although calibration experiments were done at 23 °C, different temperatures were sometimes used to control the automata operation, i.e., to prevent undesired interactions between transition molecules with inadequate symbols or to increase the yield, respectively. Following incubation with the DIs, the computation is initiated by adding 2 μL of FokI enzyme (New England Biolabs) diluted in double distilled water to obtain a final reaction concentration equal to the total concentration of active TMs. A typical reaction proceeded for 15 min (unless otherwise mentioned in the supplementary tables (Supporting Information)). Reaction termination and analysis were performed as described above. In this assays, Yes and No outputs are represented by 23-nt and 21-nt bands, respectively, for the one symbol SIM, 18-nt and 16-nt bands, respectively, for the four step SIM, 25-nt and 23-nt bands, respectively, for the three step SIM, 20-nt and 18-nt bands, respectively, for the two step SIM. For statistical analysis, the Student’s t test was used (JMP software) to detect differences between Yes and No results. All statistically significant differences were tested at the P e 0.001 level.
’ ASSOCIATED CONTENT
bS
Supporting Information. Additional figures showing the single step automaton scheme, the specificity test for p50 detection, and molecular interpretation of a four disease indicator compuation gel and tables listing automata sequences, reaction details for various DIs combinations. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected]. Author Contributions §
These authors contributed equally to this work.
’ ACKNOWLEDGMENT We thank L. Gil for the statistical analysis and K. Katzav for the prompt and excellent preparation and design of figures. The 2995
dx.doi.org/10.1021/nl2015872 |Nano Lett. 2011, 11, 2989–2996
Nano Letters research was supported by The Israel Science Foundation (Grant No 285/02 and 306/05), by the European Union FP7-ERCAdG, and by the Miel de Botton Aynsley and Paul Sparr Foundations. M.K. was supported by The ISF Converging Technologies (Grant No 1694/07). Ehud Shapiro is the Incumbent of The Harry Weinrebe Professorial Chair of Computer Science and Biology.
’ REFERENCES (1) Bennett. Int. J. Theor. Phys. 1982, 21, 905–940. (2) Adleman, L. Science 1994, 266 (5187), 1021–1024. (3) Lipton, R. Science 1995, 268 (5210), 542–545. (4) Benenson, Y.; Paz-Elizur, T.; Adar, R.; Keinan, E.; Livneh, Z.; Shapiro, E. Nature 2001, 414 (6862), 430–434. (5) Braich, R.; Chelyapov, N.; Johnson, C.; Rothemund, P.; Adleman, L. Science 2002, 296 (5567), 499–502. (6) Benenson, Y.; Adar, R.; Paz-Elizur, T.; Livneh, Z.; Shapiro, E. Proc. Natl. Acad. Sci. U.S.A. 2003, 100 (5), 2191–2196. (7) Adar, R.; Benenson, Y.; Linshiz, G.; Rosner, A.; Tishby, N.; Shapiro, E. Proc. Natl. Acad. Sci. U.S.A. 2004, 101 (27), 9960–9965. (8) Macdonald, J.; Li, Y.; Sutovic, M.; Lederman, H.; Pendri, K.; Lu, W.; Andrews, B.; Stefanovic, D.; Stojanovic, M. Nano Lett. 2006, 6 (11), 2598–2603. (9) Benenson, Y.; Gil, B.; Ben-Dor, U.; Adar, R.; Shapiro, E. Nature 2004, 429 (6990), 423–429. (10) Seelig, G.; Soloveichik, D.; Zhang, D.; Winfree, E. Science 2006, 314 (5805), 1585–1588. (11) Rinaudo, K.; Bleris, L.; Maddamsetti, R.; Subramanian, S.; Weiss, R.; Benenson, Y. Nat. Biotechnol. 2007, 25 (7), 795–801. (12) Xie, Z.; Liu, S.; Bleris, L.; Benenson, Y. Nucleic Acids Res. 2010, 38 (8), 2692–2701. (13) Benenson, Y. Mol. BioSyst. 2009, 5 (7), 675–685. (14) Win, M.; Smolke, C. Science 2008, 322 (5900), 456–460. (15) Manesh, K. M.; Halamek, J.; Pita, M.; Zhou, J.; Tam, T. K.; Santhosh, P.; Chuang, M. C.; Windmiller, J. R.; Abidin, D.; Katz, E.; Wang, J. Biosens. Bioelectron. 2009, 24 (12), 3569–3574. (16) Wang, J. Biosens. Bioelectron. 2006, 21 (10), 1887–1892. (17) Macdonald, J.; Stefanovic, D.; Stojanovic, M. Methods Mol. Biol. 2006, 335, 343–363. (18) Penchovsky, R.; Breaker, R. Nat. Biotechnol. 2005, 23 (11), 1424–1433. (19) de Silva, A.; McClenaghan, N. Chemistry 2004, 10 (3), 574–586. (20) Baron, R.; Lioubashevski, O.; Katz, E.; Niazov, T.; Willner, I. J. Phys. Chem. A 2006, 110 (27), 8548–8553. (21) Strack, G.; Pita, M.; Ornatska, M.; Katz, E. ChemBioChem 2008, 9 (8), 1260–1266. (22) Baron, R.; Lioubashevski, O.; Katz, E.; Niazov, T.; Willner, I. Angew. Chem., Int. Ed. 2006, 45 (10), 1572–1576. (23) Zhou, J.; Arugula, M. A.; Halamek, J.; Pita, M.; Katz, E. J. Phys. Chem. B 2009, 113 (49), 16065–16070. (24) Parker, J. EMBO Rep. 2003, 4 (1), 7–10. (25) Rothemund, P.; Papadakis, N.; Winfree, E. PLoS Biol. 2004, 2 (12), e424. (26) Mao, C.; LaBean, T.; Relf, J.; Seeman, N. Nature 2000, 407 (6803), 493–496. (27) Winfree, E.; Liu, F.; Wenzler, L.; Seeman, N. Nature 1998, 394 (6693), 539–544. (28) Ran, T.; Kaplan, S.; Shapiro, E. Nat. Nanotechnol. 2009, 4 (10), 642–648. (29) Beisel, C. L.; Bayer, T. S.; Hoff, K. G.; Smolke, C. D. Mol. Syst. Biol. 2008, 4, 224. (30) Zhang, D.; Winfree, E. Nucleic Acids Res. 2010, 38, 4182–4197. (31) Baron, R.; Lioubashevski, O.; Katz, E.; Niazov, T.; Willner, I. Org. Biomol. Chem. 2006, 4 (6), 989–991. (32) An, C.-I.; Trinh, V. B.; Yokobayashi, Y. RNA 2006, 12 (5), 710–716.
LETTER
(33) Teller, C.; Shimron, S.; Willner, I. Anal. Chem. 2009, 81 (21), 9114–9119. (34) Zhou, M.; Du, Y.; Chen, C.; Li, B.; Wen, D.; Dong, S.; Wang, E. J. Am. Chem. Soc. 2010, 132 (7), 2172–2174. (35) Simmel, F. Nanomedicine (London, U.K.) 2007, 2 (6), 817–830. (36) Shi, J.; Votruba, A. R.; Farokhzad, O. C.; Langer, R. Nano Lett. 2010, 10 (9), 3223–3230. (37) Chen, Y. Y.; Jensen, M. C.; Smolke, C. D. Proc. Natl. Acad. Sci. U.S.A. 2010, 107 (19), 8531–8536. (38) Culler, S. J.; Hoff, K. G.; Smolke, C. D. Science 2010, 330 (6008), 1251–1255. (39) Venkataraman, S.; Dirks, R. M.; Ueda, C. T.; Pierce, N. A. Proc. Natl. Acad. Sci. U.S.A. 2010, 107 (39), 16777–16782. (40) Asangani, I.; Rasheed, S.; Nikolova, D.; Leupold, J.; Colburn, N.; Post, S.; Allgayer, H. Oncogene 2008, 27 (15), 2128–2136. (41) Blenkiron, C.; Goldstein, L.; Thorne, N.; Spiteri, I.; Chin, S.; Dunning, M.; Barbosa-Morais, N.; Teschendorff, A.; Green, A.; Ellis, I.; Tavare, S.; Caldas, C.; Miska, E. GenomeBiology 2007, 8 (10), R214. (42) Chan, J.; Krichevsky, A.; Kosik, K. Cancer Res. 2005, 65 (14), 6029–6033. (43) Garzon, R.; Volinia, S.; Liu, C.; Fernandez-Cymering, C.; Palumbo, T.; Pichiorri, F.; Fabbri, M.; Coombes, K.; Alder, H.; Nakamura, T.; Flomenberg, N.; Marcucci, G.; Calin, G.; Kornblau, S.; Kantarjian, H.; Bloomfield, C.; Andreeff, M.; Croce, C. Blood 2008, 111 (6), 3183–3189. (44) Heneghan, H.; Miller, N.; Lowery, A.; Sweeney, K.; Kerin, M. J. Oncol. 2009, 2009, 950201. (45) Lin, S.; Chang, D.; Chang-Lin, S.; Lin, C.; Wu, D.; Chen, D.; Ying, S. RNA 2008, 14 (10), 2115–2124. (46) Lu, J.; Getz, G.; Miska, E.; Alvarez-Saavedra, E.; Lamb, J.; Peck, D.; Sweet-Cordero, A.; Ebert, B.; Mak, R.; Ferrando, A.; Downing, J.; Jacks, T.; Horvitz, H.; Golub, T. Nature 2005, 435 (7043), 834–838. (47) Yanaihara, N.; Caplen, N.; Bowman, E.; Seike, M.; Kumamoto, K.; Yi, M.; Stephens, R.; Okamoto, A.; Yokota, J.; Tanaka, T.; Calin, G.; Liu, C.; Croce, C.; Harris, C. Cancer Cell 2006, 9 (3), 189–198. (48) DeSano, J.; Xu, L. AAPS J. 2009, 11, 682–692. (49) Gopinath, S. Anal. Bioanal. Chem. 2007, 387 (1), 171–182. (50) Pestourie, C.; Tavitian, B.; Duconge, F. Biochimie 2005, 87 (910), 921–930. € (51) Mairal, T.; Cengiz Ozalp, V.; Lozano Sanchez, P.; Mir, M.; Katakis, I.; O’Sullivan, C. Anal. Bioanal. Chem. 2008, 390 (4), 989–1007. (52) Nutiu, R.; Li, Y. J. Am. Chem. Soc. 2003, 125 (16), 4771–4778. (53) Nelson, P.; Baldwin, D.; Scearce, L.; Oberholtzer, J.; Tobias, J.; Mourelatos, Z. Nat. Methods 2004, 1 (2), 155–161. (54) Thomson, J.; Parker, J.; Perou, C.; Hammond, S. Nat. Methods 2004, 1 (1), 47–53. (55) Zhu, H.; Snyder, M. Curr. Opin. Chem. Biol. 2003, 7 (1), 55–63. (56) Shi, R.; Chiang, V. L. Biotechniques 2005, 39 (4), 519–525. (57) Kang, W. J.; Cho, Y. L.; Chae, J. R.; Lee, J. D.; Choi, K. J.; Kim, S. Biomaterials 2011, 32 (7), 1915–1922. (58) Nutiu, R.; Yu, J.; Li, Y. ChemBioChem 2004, 5 (8), 1139–1144. (59) Abraham, T.; Sch€oder, H. Semin. Nucl. Med. 2011, 41 (2), 121–138. (60) Yerly, S.; Triponez, F.; Meyer, P.; Kumar, N.; Bongiovanni, M. Acta Cytol. 2010, 54 (5 Suppl), 911–917.
2996
dx.doi.org/10.1021/nl2015872 |Nano Lett. 2011, 11, 2989–2996
