Rapid and precise genotyping of transgene zygosity in mice using an allele-specific method.

Precise determination of transgene zygosity is essential for use of transgenic mice in research. Because integration loci of transgenes are usually unknown due to their random insertion, assessment of transgene zygosity remains a challenge. Current zygosity genotyping methods (progeny testing, qPCR, and NGS-computational biology analysis) are time consuming, prone to error or technically challenging. Here, we developed a novel method to determine transgene zygosity requiring no knowledge of transgene insertion loci. This method applies allele-specific restriction enzyme digestion of PCR products (RE/PCR) to rapidly and reliably quantify transgene zygosity. We demonstrate the applicability of this method to three transgenic strains of mice (Atm TgC3001L, Nes-Cre, and Syn1-Cre) harboring a unique restriction enzyme site on either the transgene or its homologous sequence in the mouse genome. This method is as accurate as the gold standard of progeny testing but requires 2 d instead of a month or more. It is also exceedingly more accurate than the most commonly used approach of qPCR quantification. Our novel method represents a significant technical advance in determining transgene zygosities in mice.

A survey on molecular-scale learning systems with relevance to DNA computing.

DNA computing has emerged as a promising alternative to achieve programmable behaviors in chemistry by repurposing the nucleic acid molecules into chemical hardware upon which synthetic chemical programs can be executed. These chemical programs are capable of simulating diverse behaviors, including boolean logic computation, oscillations, and nanorobotics. Chemical environments such as the cell are marked by uncertainty and are prone to random fluctuations. For this reason, potential DNA-based molecular devices that aim to be deployed into such environments should be capable of adapting to the stochasticity inherent in them. In keeping with this goal, a new subfield has emerged within DNA computing, focusing on developing approaches that embed learning and inference into chemical reaction systems. If realized in biochemical contexts, such molecular machines can engender novel applications in fields such as biotechnology, synthetic biology, and medicine. Therefore, it would be beneficial to review how different ideas were conceived, how the progress has been so far, and what the emerging ideas are in this nascent field of 'molecular-scale learning'.

Automated design of 3D DNA origami with non-rasterized 2D curvature.

Improving the precision and function of encapsulating three-dimensional (3D) DNA nanostructures via curved geometries could have transformative impacts on areas such as molecular transport, drug delivery, and nanofabrication. However, the addition of non-rasterized curvature escalates design complexity without algorithmic regularity, and these challenges have limited the ad hoc development and usage of previously unknown shapes. In this work, we develop and automate the application of a set of previously unknown design principles that now includes a multilayer design for closed and curved DNA nanostructures to resolve past obstacles in shape selection, yield, mechanical rigidity, and accessibility. We design, analyze, and experimentally demonstrate a set of diverse 3D curved nanoarchitectures, showing planar asymmetry and examining partial multilayer designs. Our automated design tool implements a combined algorithmic and numerical approximation strategy for scaffold routing and crossover placement, which may enable wider applications of general DNA nanostructure design for nonregular or oblique shapes.

Programming DNA-Based Biomolecular Reaction Networks on Cancer Cell Membranes.

DNA is a highly programmable biomolecule and has been used to construct biological circuits for different purposes. An important development of DNA circuits is to process the information on receptors on cell membranes. In this Communication, we introduce an architecture to program localized DNA-based biomolecular reaction networks on cancer cell membranes. Based on our architecture, various types of reaction networks have been experimentally demonstrated, from simple linear cascades to reaction networks of complex structures. These localized DNA-based reaction networks can be used for medical applications such as cancer cell detection. Compared to prior work on DNA circuits for evaluating cell membrane receptors, the DNA circuits made by our architecture have several major advantages including simpler design, lower leak, lower cost, and higher signal-to-background ratio.

Fast and compact DNA logic circuits based on single-stranded gates using strand-displacing polymerase.

DNA is a reliable biomolecule with which to build molecular computation systems. In particular, DNA logic circuits (diffusion-based) have shown good performance regarding scalability and correctness of computation. However, previous architectures of DNA logic circuits have two limitations. First, the speed of computation is slow, often requiring hours to compute a simple function. Second, the circuits are of high complexity regarding the number of DNA strands. Here, we introduce an architecture of DNA logic circuits based on single-stranded logic gates using strand-displacing DNA polymerase. The logic gates consist of only single DNA strands, which largely reduces leakage reactions and signal restoration steps such that the circuits are improved in regard to both speed of computation and the number of DNA strands needed. Large-scale logic circuits can be constructed from the gates by simple cascading strategies. In particular, we have demonstrated a fast and compact logic circuit that computes the square-root function of four-bit input numbers.

DNA-Based Analog Computing.

The field of DNA computation makes use of DNA reactions to do molecular-scale computation. Most works in DNA computation execute digital computations such as evaluation of Boolean circuits. This chapter surveys novel DNA computation methods that execute analog computations, where the inputs and outputs are real values specified by the concentrations of particular DNA strands.

Orexinergic neurotransmission in temperature responses to methamphetamine and stress: mathematical modeling as a data assimilation approach.

EXPERIMENTAL DATA: Orexinergic neurotransmission is involved in mediating temperature responses to methamphetamine (Meth). In experiments in rats, SB-334867 (SB), an antagonist of orexin receptors (OX1R), at a dose of 10 mg/kg decreases late temperature responses (t > 60 min) to an intermediate dose of Meth (5 mg/kg). A higher dose of SB (30 mg/kg) attenuates temperature responses to low dose (1 mg/kg) of Meth and to stress. In contrast, it significantly exaggerates early responses (t < 60 min) to intermediate and high doses (5 and 10 mg/kg) of Meth. As pretreatment with SB also inhibits temperature response to the stress of injection, traditional statistical analysis of temperature responses is difficult. MATHEMATICAL MODELING: We have developed a mathematical model that explains the complexity of temperature responses to Meth as the interplay between excitatory and inhibitory nodes. We have extended the developed model to include the stress of manipulations and the effects of SB. Stress is synergistic with Meth on the action on excitatory node. Orexin receptors mediate an activation of on both excitatory and inhibitory nodes by low doses of Meth, but not on the node activated by high doses (HD). Exaggeration of early responses to high doses of Meth involves disinhibition: low dose of SB decreases tonic inhibition of HD and lowers the activation threshold, while the higher dose suppresses the inhibitory component. Using a modeling approach to data assimilation appears efficient in separating individual components of complex response with statistical analysis unachievable by traditional data processing methods.

Chaos and robustness in a single family of genetic oscillatory networks.

Genetic oscillatory networks can be mathematically modeled with delay differential equations (DDEs). Interpreting genetic networks with DDEs gives a more intuitive understanding from a biological standpoint. However, it presents a problem mathematically, for DDEs are by construction infinitely-dimensional and thus cannot be analyzed using methods common for systems of ordinary differential equations (ODEs). In our study, we address this problem by developing a method for reducing infinitely-dimensional DDEs to two- and three-dimensional systems of ODEs. We find that the three-dimensional reductions provide qualitative improvements over the two-dimensional reductions. We find that the reducibility of a DDE corresponds to its robustness. For non-robust DDEs that exhibit high-dimensional dynamics, we calculate analytic dimension lines to predict the dependence of the DDEs' correlation dimension on parameters. From these lines, we deduce that the correlation dimension of non-robust DDEs grows linearly with the delay. On the other hand, for robust DDEs, we find that the period of oscillation grows linearly with delay. We find that DDEs with exclusively negative feedback are robust, whereas DDEs with feedback that changes its sign are not robust. We find that non-saturable degradation damps oscillations and narrows the range of parameter values for which oscillations exist. Finally, we deduce that natural genetic oscillators with highly-regular periods likely have solely negative feedback.

"Creature-101": A Serious Game to Promote Energy Balance-Related Behaviors Among Middle School Adolescents.

BACKGROUND: Considering adolescents' heavy use of media, serious videogames may provide an engaging and innovative way to achieve positive impact on adolescents' diet and physical activity. The objective of this study was to evaluate the efficacy of playing a serious game, "Creature-101" (developed by Teachers College, Columbia University [New York, NY] and Stottler Henke Inc. [San Mateo, CA]), at promoting energy balance-related behaviors (EBRBs) such as increasing fruits and vegetables intake, water intake, and physical activity and decreasing processed snacks intake, sweetened beverages intake, and recreational screen time. SUBJECTS AND METHODS: This pre-post intervention-control study (n=590) was conducted in New York City low-income public middle schools. The students (11-13 years of age, 51.6 percent male) played "Creature-101" online in science/health education classes (seven sessions, 30 minutes each session for 1 month). "Creature-101" used behavioral theories as the framework for "creature care" in a world "Tween." Students were provided with scientific evidence that promoted energy balance through minigames, educational videos, and slideshows and were motivated with interactive dialogues with game characters. Students also assessed their own behaviors, created their own "real life" food and activity goals, and reported their progress. A self-reported, validated, online instrument that measured frequency and amount of targeted behaviors was administered at baseline and post-intervention. RESULTS: Analysis of covariance compared post-test means between groups with pretest scores as covariates. Intervention students reported significant decreases in frequency and amount of consumption of sweetened beverages and processed snacks compared with the controls. No changes were observed for the other behaviors. CONCLUSIONS: "Creature-101" was effective at reducing consumption of sweetened beverages and processed snacks, which are related to obesity risks, indicating that the game shows promise at promoting EBRBs.

Target prediction for small, noncoding RNAs in bacteria.

Many small, noncoding RNAs in bacteria act as post-transcriptional regulators by basepairing with target mRNAs. While the number of characterized small RNAs (sRNAs) has steadily increased, only a limited number of the corresponding mRNA targets have been identified. Here we present a program, TargetRNA, that predicts the targets of these bacterial RNA regulators. The program was evaluated by assessing whether previously known targets could be identified. The program was then used to predict targets for the Escherichia coli RNAs RyhB, OmrA, OmrB and OxyS, and the predictions were compared with changes in whole genome expression patterns observed upon expression of the sRNAs. Our results show that TargetRNA is a useful tool for finding mRNA targets of sRNAs, although its rate of success varies between sRNAs.