Self-powered RNA nanomachine driven by metastable structure.

Many non-coding and regulatory RNA elements have evolved to exploit transient or metastable structures that emerge during transcription to control complex folding pathways or to encode dynamic functions. However, efforts to engineer synthetic RNA devices have mostly focused on the thermodynamically stable structures. Consequently, significant challenges and opportunities exist in engineering functional RNAs that explicitly take advantage of cotranscriptionally generated transient or metastable structures. In this work, we designed a short RNA sequence that adopts a robust metastable structure when transcribed by an RNA polymerase. Although the metastable structure persists for hours at low temperature, it refolds almost completely into the thermodynamically stable structure upon heat denaturation followed by cooling. The synthetic RNA was also equipped with the Broccoli aptamer so that it can bind its ligand and become fluorescent only in the thermodynamically stable structure. We further demonstrated that the relaxation to the thermodynamically stable and fluorescent structure can be catalyzed by a short trigger RNA in a sequence-specific manner. Finally, the RNA architecture was redesigned to sense and respond to microRNA sequences. In summary, we designed RNA nanomachines that can detect an RNA sequence, amplify signal and produce an optical output, all encoded in a single RNA transcript, self-powered by a metastable structure.

Programmable Artificial Cells Using Histamine-Responsive Synthetic Riboswitch.

Artificial cells that encapsulate DNA-programmable protein expression machinery are emerging as an attractive platform for studying fundamental cellular properties and applications in synthetic biology. However, interfacing these artificial cells with the complex and dynamic chemical environment remains a major and urgent challenge. We demonstrate that the repertoire of molecules that artificial cells respond to can be expanded by synthetic RNA-based gene switches, or riboswitches. We isolated an RNA aptamer that binds histamine with high affinity and specificity and used it to design robust riboswitches that activate protein expression in the presence of histamine. Finally, the riboswitches were incorporated in artificial cells to achieve controlled release of an encapsulated small molecule and to implement a self-destructive kill-switch. Synthetic riboswitches should serve as modular and versatile interfaces to link artificial cell phenotypes with the complex chemical environment.

The CCR4-NOT Deadenylase Complex Maintains Adipocyte Identity.

Shortening of poly(A) tails triggers mRNA degradation; hence, mRNA deadenylation regulates many biological events. In the present study, we generated mice lacking the Cnot1 gene, which encodes an essential scaffold subunit of the CCR4-NOT deadenylase complex in adipose tissues (Cnot1-AKO mice) and we examined the role of CCR4-NOT in adipocyte function. Cnot1-AKO mice showed reduced masses of white adipose tissue (WAT) and brown adipose tissue (BAT), indicating abnormal organization and function of those tissues. Indeed, Cnot1-AKO mice showed hyperinsulinemia, hyperglycemia, insulin resistance, and glucose intolerance and they could not maintain a normal body temperature during cold exposure. Muscle-like fibrous material appeared in both WAT and BAT of Cnot1-AKO mice, suggesting the acquisition of non-adipose tissue characteristics. Gene expression analysis using RNA-sequencing (RNA-seq) showed that the levels of adipose tissue-related mRNAs, including those of metabolic genes, decreased, whereas the levels of inflammatory response-related mRNAs increased. These data suggest that the CCR4-NOT complex ensures proper adipose tissue function by maintaining adipocyte-specific mRNAs at appropriate levels and by simultaneously suppressing mRNAs that would impair adipocyte function if overexpressed.

High-throughput assay and engineering of self-cleaving ribozymes by sequencing.

Self-cleaving ribozymes are found in all domains of life and are believed to play important roles in biology. Additionally, self-cleaving ribozymes have been the subject of extensive engineering efforts for applications in synthetic biology. These studies often involve laborious assays of multiple individual variants that are either designed rationally or discovered through selection or screening. However, these assays provide only a limited view of the large sequence space relevant to the ribozyme function. Here, we report a strategy that allows quantitative characterization of greater than 1000 ribozyme variants in a single experiment. We generated a library of predefined ribozyme variants that were converted to DNA and analyzed by high-throughput sequencing. By counting the number of cleaved and uncleaved reads of every variant in the library, we obtained a complete activity profile of the ribozyme pool which was used to both analyze and engineer allosteric ribozymes.

High-Throughput Mutational Analysis of a Twister Ribozyme.

Recent discoveries of new classes of self-cleaving ribozymes in diverse organisms have triggered renewed interest in the chemistry and biology of ribozymes. Functional analysis and engineering of ribozymes often involve performing biochemical assays on multiple ribozyme mutants. However, because each ribozyme mutant must be individually prepared and assayed, the number and variety of mutants that can be studied are severely limited. All of the single and double mutants of a twister ribozyme (a total of 10 296 mutants) were generated and assayed for their self-cleaving activity by exploiting deep sequencing to count the numbers of cleaved and uncleaved sequences for every mutant. Interestingly, we found that the ribozyme is highly robust against mutations such that 71 % and 30 % of all single and double mutants, respectively, retain detectable activity under the assay conditions. It was also observed that the structural elements that comprise the ribozyme exhibit distinct sensitivity to mutations.

Kinetic analysis of aptazyme-regulated gene expression in a cell-free translation system: modeling of ligand-dependent and -independent expression.

Aptazymes are useful as RNA-based switches of gene expression responsive to several types of compounds. One of the most important properties of the switching ability is the signal/noise (S/N) ratio, i.e., the ratio of gene expression in the presence of ligand to that in the absence of ligand. The present study was performed to gain a quantitative understanding of how the aptazyme S/N ratio is determined by factors involved in gene expression, such as transcription, RNA self-cleavage, RNA degradation, protein translation, and their ligand dependencies. We performed switching of gene expression using two on-switch aptazymes with different properties in a cell-free translation system, and constructed a kinetic model that quantitatively describes the dynamics of RNA and protein species involved in switching. Both theoretical and experimental analyses consistently demonstrated that factors determining both the absolute value and the dynamics of the S/N ratio are highly dependent on the routes of translation in the absence of ligand: translation from the ligand-independently cleaved RNA or leaky translation from the noncleaved RNA. The model obtained here is useful to assess the factors that restrict the S/N ratio and to improve aptazymes more efficiently.

Phenolic signals for prehaustorium formation in Striga hermonthica.

Striga hermonthica is a root parasitic plant that causes considerable crop yield losses. To parasitize host plants, parasitic plants develop a specialized organ called the haustorium that functions in host invasion and nutrient absorption. The initiation of a prehaustorium, the primitive haustorium structure before host invasion, requires the perception of host-derived compounds, collectively called haustorium-inducing factors (HIFs). HIFs comprise quinones, phenolics, flavonoids and cytokinins for S. hermonthica; however, the signaling pathways from various HIFs leading to prehaustorium formation remain largely uncharacterized. It has been proposed that quinones serve as direct signaling molecules for prehaustorium induction and phenolic compounds originating from the host cell wall are the oxidative precursors, but the overlap and distinction of their downstream signaling remain unknown. Here we show that quinone and phenolic-triggered prehaustorium induction in S. hermonthica occurs through partially divergent signaling pathways. We found that ASBr, an inhibitor of acetosyringone in virulence gene induction in the soil bacterium Agrobacterium, compromised prehaustorium formation in S. hermonthica. In addition, LGR-991, a competitive inhibitor of cytokinin receptors, inhibited phenolic-triggered but not quinone-triggered prehaustorium formation, demonstrating divergent signaling pathways of phenolics and quinones for prehaustorium formation. Comparisons of genome-wide transcriptional activation in response to either phenolic or quinone-type HIFs revealed markedly distinct gene expression patterns specifically at the early initiation stage. While quinone DMBQ triggered rapid and massive transcriptional changes in genes at early stages, only limited numbers of genes were induced by phenolic syringic acid. The number of genes that are commonly upregulated by DMBQ and syringic acid is gradually increased, and many genes involved in oxidoreduction and cell wall modification are upregulated at the later stages by both HIFs. Our results show kinetic and signaling differences in quinone and phenolic HIFs, providing useful insights for understanding how parasitic plants interpret different host signals for successful parasitism.

Stochastic variational variable selection for high-dimensional microbiome data.

BACKGROUND: The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the approach of the existing methods, the computational burden of identifying a small number of representative species from a large number of observed species remains a challenge. RESULTS: We propose a novel approach to improve the performance of the widely used DMM approach by combining three ideas: (i) we propose an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters; (ii) to address the computational burden of high-dimensional microbiome data, we propose a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation; and (iii) we extend the finite DMM model to an infinite case by considering Dirichlet process mixtures and estimating the number of clusters as a variational parameter. Using the proposed method, stochastic variational variable selection (SVVS), we analyzed the root microbiome data collected in our soybean field experiment, the human gut microbiome data from three published datasets of large-scale case-control studies and the healthy human microbiome data from the Human Microbiome Project. CONCLUSIONS: SVVS demonstrates a better performance and significantly faster computation than those of the existing methods in all cases of testing datasets. In particular, SVVS is the only method that can analyze massive high-dimensional microbial data with more than 50,000 microbial species and 1000 samples. Furthermore, a core set of representative microbial species is identified using SVVS that can improve the interpretability of Bayesian mixture models for a wide range of microbiome studies. Video Abstract.

High throughput method of 16S rRNA gene sequencing library preparation for plant root microbial community profiling.

Microbiota are a major component of agroecosystems. Root microbiota, which inhabit the inside and surface of plant roots, play a significant role in plant growth and health. As next-generation sequencing technology allows the capture of microbial profiles without culturing the microbes, profiling of plant microbiota has become a staple tool in plant science and agriculture. Here, we have increased sample handling efficiency in a two-step PCR amplification protocol for 16S rRNA gene sequencing of plant root microbiota, improving DNA extraction using AMPure XP magnetic beads and PCR purification using exonuclease. These modifications reduce sample handling and capture microbial diversity comparable to that obtained by the manual method. We found a buffer with AMPure XP magnetic beads enabled efficient extraction of microbial DNA directly from plant roots. We also demonstrated that purification using exonuclease before the second PCR step enabled the capture of higher degrees of microbial diversity, thus allowing for the detection of minor bacteria compared with the purification using magnetic beads in this step. In addition, our method generated comparable microbiome profile data in plant roots and soils to that of using common commercially available DNA extraction kits, such as DNeasy PowerSoil Pro Kit and FastDNA SPIN Kit for Soil. Our method offers a simple and high-throughput option for maintaining the quality of plant root microbial community profiling.

The CCR4-NOT complex maintains liver homeostasis through mRNA deadenylation.

The biological significance of deadenylation in global gene expression is not fully understood. Here, we show that the CCR4-NOT deadenylase complex maintains expression of mRNAs, such as those encoding transcription factors, cell cycle regulators, DNA damage response-related proteins, and metabolic enzymes, at appropriate levels in the liver. Liver-specific disruption of Cnot1, encoding a scaffold subunit of the CCR4-NOT complex, leads to increased levels of mRNAs for transcription factors, cell cycle regulators, and DNA damage response-related proteins because of reduced deadenylation and stabilization of these mRNAs. CNOT1 suppression also results in an increase of immature, unspliced mRNAs (pre-mRNAs) for apoptosis-related and inflammation-related genes and promotes RNA polymerase II loading on their promoter regions. In contrast, mRNAs encoding metabolic enzymes become less abundant, concomitant with decreased levels of these pre-mRNAs. Lethal hepatitis develops concomitantly with abnormal mRNA expression. Mechanistically, the CCR4-NOT complex targets and destabilizes mRNAs mainly through its association with Argonaute 2 (AGO2) and butyrate response factor 1 (BRF1) in the liver. Therefore, the CCR4-NOT complex contributes to liver homeostasis by modulating the liver transcriptome through mRNA deadenylation.

Analyzing and Tuning Ribozyme Activity by Deep Sequencing To Modulate Gene Expression Level in Mammalian Cells.

Self-cleaving ribozymes, in combination with aptamers and various classes of RNAs, have been heavily engineered to create RNA devices to control gene expression. Although understanding of sequence-function relationships of ribozymes is critical for such efforts, our current knowledge of self-cleaving ribozymes is mostly limited to the results from small scale mutational studies performed under different conditions, or qualitative results of mutate-and-select experiments that may contain experimental biases. Here, we applied our strategy based on deep sequencing to comprehensively assay a large number of mutants to systematically examine the effect of the P4 stem sequence on the activity of an HDV-like ribozyme. We discovered that the ribozyme activity is highly sensitive to the sequence and the apparent stability of the varied positions. Furthermore, we demonstrated that the collection of the ribozyme variants with different activities can be used as a convenient device to fine-tune the level of gene expression in mammalian cells.

Large Scale Mutational and Kinetic Analysis of a Self-Hydrolyzing Deoxyribozyme.

Deoxyribozymes are catalytic DNA sequences whose atomic structures are generally difficult to elucidate. Mutational analysis remains a principal approach for understanding and engineering deoxyribozymes with diverse catalytic activities. However, laborious preparation and biochemical characterization of individual sequences severely limit the number of mutants that can be studied biochemically. Here, we applied deep sequencing to directly measure the activities of self-hydrolyzing deoxyribozyme sequences in high throughput. First, all single and double mutants within the 15-base catalytic core of the deoxyribozyme I-R3 were assayed to unambiguously determine the tolerated and untolerated mutations at each position. Subsequently, 4096 deoxyribozyme variants with tolerated base substitutions at seven positions were kinetically assayed in parallel. We identified 533 active mutants whose first-order rate constants and activation energies were determined. The results indicate an isolated and narrow peak in the deoxyribozyme sequence space and provide a quantitative view of the effects of multiple mutations on the deoxyribozyme activity for the first time.

Deep Sequencing Analysis of Aptazyme Variants Based on a Pistol Ribozyme.

Chemically regulated self-cleaving ribozymes, or aptazymes, are emerging as a promising class of genetic devices that allow dynamic control of gene expression in synthetic biology. However, further expansion of the limited repertoire of ribozymes and aptamers, and development of new strategies to couple the RNA elements to engineer functional aptazymes are highly desirable for synthetic biology applications. Here, we report aptazymes based on the recently identified self-cleaving pistol ribozyme class using a guanine aptamer as the molecular sensing element. Two aptazyme architectures were studied by constructing and assaying 17 728 mutants by deep sequencing. Although one of the architectures did not yield functional aptazymes, a novel aptazyme design in which the aptamer and the ribozyme were placed in tandem yielded a number of guanine-inhibited ribozymes. Detailed analysis of the extensive sequence-function data suggests a mechanism that involves a competition between two mutually exclusive RNA structures reminiscent of natural bacterial riboswitches.

Simple identification of two causes of noise in an aptazyme system by monitoring cell-free transcription.

Aptazymes are artificially synthesized ribozymes that catalyze reactions in response to ligand binding. Certain types of aptazymes can be utilized as RNA-based regulators of gene expression. These aptazymes contain a sequestered ribosome-binding site (rbs) and release the rbs through self-cleavage in response to ligand binding, inducing the expression of the downstream gene. One of the most important properties of aptazymes as gene expression regulators is their signal-to-noise ratio (S/N ratio), the ratio of target expression in the presence of ligand to that in the absence of ligand. One strategy to improve the S/N ratio is to decrease the noise (expression in the absence of ligand) due to leaky translation without rbs release or ligand-independent rbs release. In this chapter, we describe an easy method to identify the main cause of noise using a cell-free reconstituted transcription-translation system, an ideal platform for the quantitative understanding of biochemical reactions because researchers can strictly control the experimental conditions and the concentrations of all components. This knowledge would be useful for designing aptazymes with high S/N ratios.

A controllable gene expression system in liposomes that includes a positive feedback loop.

We introduced a positive feedback loop into a LacI-dependent gene expression system in lipid vesicles, producing a cell-like system that senses and responds to an external signal with a high signal-to-noise ratio. This fully reconstituted system will be a useful tool in future applications in in vitro synthetic biology.

A target site for spontaneous insertion of IS10 element in pUC19 DNA located within intrinsically bent DNA.

Residual insertion sequence elements (IS elements) in Escherichia coli strains that are commonly used for DNA cloning are known to cause cloning artifacts by transposing themselves into the recombinant DNA fragments. In such cases, chance insertion of IS elements may occur at integration sites in the cloning targets, which in the case of the IS10 element is a 9-bp consensus sequence. We report here that the integration of IS10-related DNA sequences into the pUC19 cloning vector and its derivative occurred with considerable frequency in E. coli strains JM107 and DH10B, with duplication of a 9-bp segment (TCTAAAGTA). Notably, native polyacrylamide gel electrophoresis revealed that intrinsically bent DNA flanks the insertion site.