работа «Биологические компьютеры» (стр. 8 из 9)

Molecular automata1, 2, 3 that combine sensing4, 5, 6, computation7, 8, 9, 10, 11, 12 and actuation13, 14 enable programmable manipulation of biological systems. We use RNA interference (RNAi)15 in human kidney cells to construct a molecular computing core that implements general Boolean logic1, 3, 8, 9, 10, 11, 12, 16 to make decisions based on endogenous molecular inputs. The state of an endogenous input is encoded by the presence or absence of 'mediator' small interfering RNAs (siRNAs). The encoding rules, combined with a specific arrangement of the siRNA targets in a synthetic gene network17, allow direct evaluation of any Boolean expression in standard forms using siRNAs and indirect evaluation using endogenous inputs. We demonstrate direct evaluation of expressions with up to five logic variables. Implementation of the encoding rules through sensory up- and down-regulatory links between the inputs and siRNA mediators will allow arbitrary Boolean decision-making using these inputs.

Introduction

A molecular automaton is an engineered molecular system coupled to a (bio)molecular environment by "flow of incoming messages and the actions of outgoing messages," where the incoming messages are processed by an "intermediate set of elements," that is, a computer18. Molecular automata may implement diverse models of computation (digital and analog circuits, state machines, neural networks) to perform a variety of tasks. We suggest a general-

purpose design framework for automata that uses logic evaluation to make certain types of decisions based on environmental molecular inputs.

Molecular logic evaluators have been demonstrated in vitro1, 2, 3, 9, 11, 12 and in live cells8, 10. Up until now, only in vitro systems1, 12, 19 have shown how to evaluate arbitrary logic expressions experimentally, although arbitrary evaluation in vivo using transcription factors has been considered theoretically20, 21. Demonstration that allosteric modulation of small RNAs22, including ribozymes1, 3, 16, riboswitches4, 5 and siRNA6, regulates gene expression prompted us to suggest that, much like transcription factors, small RNA molecules will enable molecular automata to make in vivo evaluations through mediation between endogenous inputs and the downstream molecular 'computing' network.

A logic evaluator operating in an intracellular molecular milieu can serve as a binary decision-making circuit23, that is, trigger one or two discrete processes in response to inputs from this milieu.

The capacity for in vivo decision making based on endogenous inputs could find applications in basic research and medicine, such as in the diagnosis of cancer2, 24. To address this issue, we (i) recast decision-making rules as a logic expression containing intracellular inputs as variables; and (ii) construct a molecular system that produces a molecular output when the expression is evaluated as True for the given input truth values (True when present and False when absent). We propose how to construct such a system for an arbitrary question represented by a logic expression. Although our design suggests separate sensor and evaluator modules, we demonstrate only the evaluator.

There are several theoretically equivalent, but practically different, ways to answer arbitrary logic questions. They generally involve breaking a complex question into a hierarchy of simpler ones. One possibility is to be very stringent with basic modules (e.g., the first input must be True, the second must be False), but connect these modules in a less stringent way where an overall positive result is achieved when any one module gives a positive answer. Another way is to be

less stringent within the basic modules (e.g., at least one input has to be in an expected state), but put stringent demands on the combinations of modules, by requiring all answers to be positive simultaneously to give an overall positive answer.

To construct an evaluator that embodies the first approach, we build a biological 'circuit' that comprises two or more mRNA species that encode the same protein, but have different noncoding regions. This protein is the system's output; a biologically active output may function as an actuator. If at least one mRNA species is translated, the resulting output will represent a logic True value, implementing an OR operation10, 12 (Fig. 1a). The levels of mRNA species and the output are determined by the presence or absence of the endogenous molecular inputs with the help of molecular mediators. siRNA molecules target untranslated regions and hence are natural candidates for such mediation. First, we fuse different sets of siRNA targets into the 3'-untranslated regions (UTR) of the mRNAs, rendering them susceptible to either of these siRNAs25. Next, we establish selective inhibitory links between endogenous inputs and these siRNAs. All inputs must be present at the same time to block all siRNAs and generate output from an mRNA, corresponding to a logic AND operation (Fig. 1b). Furthermore, if, for example, inputs A and B block siRNAs that target one mRNA and inputs X and Y block siRNAs that target another, the circuit will generate an output when both A and B are present or when both X and Y are present. This comprises the logic expression (A AND B) OR (X AND Y). If an activating link is established instead, the presence or absence of an input will block or enable output production from the mRNA, respectively. In logical terms, this amounts to a negation of the input 'truth value' (Fig. 1c). In the above example, input B activating its mediator siRNA turns the expression into (A AND NOT(B)) OR (X AND Y). The same input may block one siRNA and activate another, and thus appear in the expression both as itself and as its negation. This arrangement of input variables and their negations, known as literals, is called a disjunctive normal form (DNF) (Fig. 1d and Supplementary Fig. 1 online). Literals grouped

by the AND operation are called 'clauses' and we call the mRNA species modified in their 3'-UTR, as well as the genes that express them, 'clause molecules'.

Figure 1: Design of the decision-making automaton that uses a DNF evaluator.

(a) A circuit that evaluates an OR operation between mRNA molecules. A truth table of an OR operation is shown. An upward arrow indicates the presence of the mRNA. (b) A circuit that evaluates an AND operation between the endogenous inputs A and B. Dotted blunt arrows indicate blocking sensory interactions and full blunt arrows indicate downregulation via RNAi. A truth table of the AND operation is shown. Similar circuits are constructed and substituted for each mRNA as in the OR gate in a. (c) A circuit that evaluates a negation (logic NOT operation) of an endogenous input A and a corresponding truth table. A pointed dotted arrow indicates activating sensory interaction. (d) An example of a circuit that, given the sensory links (dotted lines) between the endogenous inputs and the mediator siRNAs, evaluates an indicated DNF expression on these inputs. If, for example, inputs A, B and E are present, the siRNAs that mediate their presence will be inactivated, resulting in a high expression from the clause molecule 1. Even though the siRNA-NOT(A) will be active and will suppress the translation of the output protein ZsYellow from the clause molecule 2, the overall output protein level will qualify as a True result. CMV, human cytomegalovirus immediate-early promoter.

A biological circuit that enables the second approach comprises mRNA species that produce a transcription factor that represses an output-encoding gene. If the repressor obtained from one mRNA efficiently downregulates the output, all mRNAs must be removed to generate the output, thus implementing an AND logic operation (Fig. 2a). As before, we fuse sets of siRNA targets into the 3'-UTR

of the repressor mRNAs. However, contrary to the previous case, here endogenous inputs should activate the siRNAs rather than block them. At least one siRNA from each set must be activated to remove all repressor mRNAs and relieve the repression (Fig. 2b), corresponding to a logic OR operation (Fig. 2b). For example, if inputs A and B activate siRNAs that target one mRNA and inputs X and Y activate siRNAs that target another, we require that at least one of the inputs A and B, and at least one of the inputs X and Y, be present. In logic terms, this constitutes the expression (A OR B) AND (X OR Y). An input that blocks its mediator siRNA is negated in the expression (Fig. 2c). This is an example of a conjunctive normal form (CNF) expression and the circuit is a CNF evaluator (Fig. 2d, Supplementary Fig. 1). The DNF and CNF standard forms are particularly useful because any logic condition can be evaluated using a corresponding DNF or CNF representation, although one representation may be shorter than the other.

Figure 2: Design of the decision-making automaton that uses a CNF evaluator and automaton's input encoding rules.

(a) A circuit that evaluates an AND operation between mRNA molecules. A downward arrow in table indicates the absence of the mRNA. CAG, chicken

-actin promoter with CMV enhancer. LacO stands for two adjacent Lac operator sites. KRAB, Krueppel-associated box domain. (b) A circuit that evaluates an OR operation between the endogenous inputs A and B. Similar circuits are substituted for all mRNAs from the AND gate. (c) A circuit that evaluates a negation (logic NOT) of an endogenous input A. (d) A circuit that evaluates the indicated CNF expression. For example, if the inputs B and C are present, siRNA-B and siRNA-C will be activated, downregulating the levels of the LacI repressor translated from the clause molecules 1 and 2. Overall level of the LacI repressor will be low, relieving the suppression from the promoter of the output protein and resulting in a high output level, interpreted as a True evaluation result. (e) Rules that link the presence or absence of the endogenous inputs with the presence or absence of their mediator siRNAs, depending on the type of expression (DNF or CNF).

We experimentally implemented DNF and CNF evaluators in immortalized human embryonic kidney cells (293-H). We transfected the cells with the genes comprising the evaluator circuits; we also added, or withheld, mediator siRNA molecules to reflect the anticipated function of the sensory module in accordance with the presence or absence of inputs appearing as variables in expressions (Fig. 2e); and we assayed the output levels after 48 h. We chose derivatives of known siRNAs for the current implementation, and constructed five siRNA-target pairs based on published sequences from nonmammalian genes to represent up to five inputs (T1 and T2 from Renilla reniformis, FF3 and FF4 from firefly luciferases and SI4 from enhanced green fluorescent protein (eGFP); Supplementary Table 1 online). We modified the published sequences by sliding them along their parental genes to afford at least a pair of A/U bases on the 5'-end of the molecule and a pair of C/G bases on the 3'-end to ensure asymmetry in RNA-induced silencing complex assembly26.

Multi-siRNA systems may exhibit undesired crosstalk between individual molecules. We measured this crosstalk, using ZsYellow derivatives with single targets cloned into their 3'-UTR and applying all siRNA molecules at the saturation concentration, one at a time, to each derivative. Crosstalk was negligible for this set of siRNAs (Supplementary Fig. 2 online), except for a possible minor (

20%) reduction in the ZsYellow level when SI4 siRNA was applied to the FF4 target; this was further reduced to 10% when the FF4 target was a part of a clause molecule (Fig. 3). We then built and tested a number of mRNA clauses for DNF evaluators, fusing the siRNA targets into the 3'-UTR of the ZsYellow output (Fig. 3). The results show that complete downregulation is achieved separately by any of the cognate siRNAs but not by the others, as required by the construction (Fig. 3). Initially, one of the constructs (ZsYellow-T1-SI4-FF4) showed incomplete repression by two out of three siRNAs. We performed RNA-folding analysis of the clause sequence with permuted order of targets, and found that an arrangement selected for its low folding energy operates substantially better than the original (Supplementary Fig. 3 online).

Figure 3: Testing individual DNF clause molecules.

(a) Two expressions in DNF form are evaluated for all possible variable assignments as indicated in the figure. 2.5 pmol of each input siRNA (or 2.5 pmol of the negative control siRNA in the case of an absent input siRNA) were cotransfected with 100 ng of each clause molecule and 100 ng of the pAmCyan-C1 transfection control into 293-H cells and assayed after 48 h. The quantitative results corresponding to the images that were obtained using FACS are shown on the right (see Methods). Red pseudocolor represents the transfection control protein AmCyan and the green color represents the output protein ZsYellow. (b) An evaluation of two CNF expressions. In C1 evaluation experiments using LacI, 10 pmol of each siRNA, 50 ng of the CMV-LacI-FF3-FF4 clause molecule, 200 ng of CAGOP-dsRed-monomer reporter and 100 ng of pAmCyan-C1 transfection control were cotransfected into 293-H cells and assayed after 48 h. The expression levels of the reporter obtained by FACS are given relative to the control experiments where active siRNA was replaced with the same level of nonsense siRNA (first row of images). In C1 evaluation experiments using LacI-KRAB, 5 pmol of each siRNA, 5 ng of the CMV-LacI-KRAB-FF3x3-FF4x3 clause molecule, 200 ng of CAGOP-dsRed-monomer reporter and 100 ng of pAmCyan-C1 transfection control were cotransfected into 293-H cells and imaged after 48 h. The expression levels of the reporter given in the figure were obtained by FACS using 100 ng of pZsYellow-C1 transfection control instead of pAmCyan-C1 and they are given relative to the control experiments where active siRNA was replaced with the same level of nonsense siRNA (first row of images). In C2

evaluation experiments, 5 pmol of each siRNA, 50 ng of CMV-LacI-FF3x3 and CMV-LacI-FF4x3 clause molecules, 200 ng of CAGOP-dsRed-monomer reporter and 100 ng of pAmCyan-C1 transfection control plasmids were cotransfected into 293-H cells and assayed after 48 h. It was quantified similarly to the C1 experiments with LacI. Blue pseudocolor represents the transfection control protein AmCyan and the red color represents the reporter protein dsRed-monomer. (c) A demonstration of anticorrelated evaluation results provided by two circuits operating in parallel. 10 pmol of siRNA (or nonsense siRNA), 100 ng of pZsYellow-F3x3 and 50 ng of CMV-LacI-F3x3 clause molecules and 200 ng of CAGOP-dsRed-monomer reporter were cotransfected into 293-H cells and assayed after 48 h. Each reporter (ZsYellow and dsRed) was quantified independently and given relative to their respective True expression levels.

The constructs and their common sequence motif that includes a stop codon (top) are shown to the left. We cotransfected 10 pmol of each indicated siRNA (columns) with 100 ng of the indicated clause molecule (rows) and 100 ng of the transfection control plasmid pAmCyan-C1 into 293-H cells and assayed after 48 h. The images combine the fluorescent signal from the AmCyan transfection control (red pseudocolor) and the signal from the ZsYellow protein expressed from the clause molecules (green pseudocolor). Low levels of ZsYellow result in red images whereas coexpression of both proteins results in mostly green and yellow spots. Negative control is a nonsense siRNA provided in the same amount as the active siRNAs. The quantitative results that correspond to the images, obtained by FACS measurements and normalized to the negative control for each construct, are shown on the right.