Dataset Information

Synthetic RNA-based logic computation in mammalian cells.

ABSTRACT: Synthetic biological circuits are designed to regulate gene expressions to control cell function. To date, these circuits often use DNA-delivery methods, which may lead to random genomic integration. To lower this risk, an all RNA system, in which the circuit and delivery method are constituted of RNA components, is preferred. However, the construction of complexed circuits using RNA-delivered devices in living cells has remained a challenge. Here we show synthetic mRNA-delivered circuits with RNA-binding proteins for logic computation in mammalian cells. We create a set of logic circuits (AND, OR, NAND, NOR, and XOR gates) using microRNA (miRNA)- and protein-responsive mRNAs as decision-making controllers that are used to express transgenes in response to intracellular inputs. Importantly, we demonstrate that an apoptosis-regulatory AND gate that senses two miRNAs can selectively eliminate target cells. Thus, our synthetic RNA circuits with logic operation could provide a powerful tool for future therapeutic applications.

SUBMITTER: Matsuura S

PROVIDER: S-EPMC6242901 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

Synthetic RNA-based logic computation in mammalian cells.

Matsuura Satoshi S Ono Hiroki H Kawasaki Shunsuke S Kuang Yi Y Fujita Yoshihiko Y Saito Hirohide H

Nature communications 20181119 1

Synthetic biological circuits are designed to regulate gene expressions to control cell function. To date, these circuits often use DNA-delivery methods, which may lead to random genomic integration. To lower this risk, an all RNA system, in which the circuit and delivery method are constituted of RNA components, is preferred. However, the construction of complexed circuits using RNA-delivered devices in living cells has remained a challenge. Here we show synthetic mRNA-delivered circuits with R ...[more]

PMID: 30451868

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Project description:There is considerable scientific interest in understanding how cell assemblies-the long-presumed computational motif-are organized so that the brain can generate intelligent cognition and flexible behavior. The Theory of Connectivity proposes that the origin of intelligence is rooted in a power-of-two-based permutation logic (N = 2 i -1), producing specific-to-general cell-assembly architecture capable of generating specific perceptions and memories, as well as generalized knowledge and flexible actions. We show that this power-of-two-based permutation logic is widely used in cortical and subcortical circuits across animal species and is conserved for the processing of a variety of cognitive modalities including appetitive, emotional and social information. However, modulatory neurons, such as dopaminergic (DA) neurons, use a simpler logic despite their distinct subtypes. Interestingly, this specific-to-general permutation logic remained largely intact although NMDA receptors-the synaptic switch for learning and memory-were deleted throughout adulthood, suggesting that the logic is developmentally pre-configured. Moreover, this computational logic is implemented in the cortex via combining a random-connectivity strategy in superficial layers 2/3 with nonrandom organizations in deep layers 5/6. This randomness of layers 2/3 cliques-which preferentially encode specific and low-combinatorial features and project inter-cortically-is ideal for maximizing cross-modality novel pattern-extraction, pattern-discrimination and pattern-categorization using sparse code, consequently explaining why it requires hippocampal offline-consolidation. In contrast, the nonrandomness in layers 5/6-which consists of few specific cliques but a higher portion of more general cliques projecting mostly to subcortical systems-is ideal for feedback-control of motivation, emotion, consciousness and behaviors. These observations suggest that the brain's basic computational algorithm is indeed organized by the power-of-two-based permutation logic. This simple mathematical logic can account for brain computation across the entire evolutionary spectrum, ranging from the simplest neural networks to the most complex.

Dataset Information

Synthetic RNA-based logic computation in mammalian cells.

Publications

Synthetic RNA-based logic computation in mammalian cells.

Similar Datasets

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