A small-molecule chemical interface for molecular programs.

In vitro molecular circuits, based on DNA-programmable chemistries, can perform an increasing range of high-level functions, such as molecular level computation, image or chemical pattern recognition and pattern generation. Most reported demonstrations, however, can only accept nucleic acids as input signals. Real-world applications of these programmable chemistries critically depend on strategies to interface them with a variety of non-DNA inputs, in particular small biologically relevant chemicals. We introduce here a general strategy to interface DNA-based circuits with non-DNA signals, based on input-translating modules. These translating modules contain a DNA response part and an allosteric protein sensing part, and use a simple design that renders them fully tunable and modular. They can be repurposed to either transmit or invert the response associated with the presence of a given input. By combining these translating-modules with robust and leak-free amplification motifs, we build sensing circuits that provide a fluorescent quantitative time-response to the concentration of their small-molecule input, with good specificity and sensitivity. The programmability of the DNA layer can be leveraged to perform DNA based signal processing operations, which we demonstrate here with logical inversion, signal modulation and a classification task on two inputs. The DNA circuits are also compatible with standard biochemical conditions, and we show the one-pot detection of an enzyme through its native metabolic activity. We anticipate that this sensitive small-molecule-to-DNA conversion strategy will play a critical role in the future applications of molecular-level circuitry.

Advances in multiplexed techniques for the detection and quantification of microRNAs.

MicroRNA detection is currently a crucial analytical chemistry challenge: almost 2000 papers were referenced in PubMed in 2018 and 2019 for the keywords "miRNA detection method". MicroRNAs are potential biomarkers for multiple diseases including cancers, neurodegenerative and cardiovascular diseases. Since miRNAs are stably released in bodily fluids, they are of prime interest for the development of non-invasive diagnosis methods, such as liquid biopsies. Their detection is however challenging, as high levels of sensitivity, specificity and robustness are required. The analysis also needs to be quantitative, since the aim is to detect miRNA concentration changes. Moreover, a high multiplexing capability is also of crucial importance, since the clinical potential of miRNAs probably lays in our ability to perform parallel mapping of multiple miRNA concentrations and recognize typical disease signature from this profile. A plethora of biochemical innovative detection methods have been reported recently and some of them provide new solutions to the problem of sensitive multiplex detection. In this review, we propose to analyze in particular the new developments in multiplexed approaches to miRNA detection. The main aspects of these methods (including sensitivity and specificity) will be analyzed, with a particular focus on the demonstrated multiplexing capability and potential of each of these methods.

Streamlined digital bioassays with a 3D printed sample changer.

Droplet-based microfluidics has permeated many areas of life sciences including biochemistry, biology and medicine. Water-in-oil droplets act as independent femto- to nano-liter reservoirs, enabling the parallelization of (bio)chemical reactions with a minimum sample input. Among the range of applications spanned by droplet microfluidics, digital detection of biomolecules, using Poissonian isolation of single molecules in compartments, has gained considerable attention due to the high accuracy, sensitivity and robustness of these methods. However, while the droplet throughput can be very high, the sample throughput of these methods is poor in comparison to well plate-based assays. This limitation comes from the necessity to convert independently each sample into a monodisperse emulsion. In this paper, we report a versatile device that performs the quick sequential partitioning of up to 15 samples using a single microfluidic chip. A 3D printed sample rotor is loaded with all samples and connected to a pressure source. Simple magnetic actuation is then used to inject the samples in the microfluidic chip without pressure disruption. This procedure generates monodisperse droplets with high sample-to-sample consistency. We also describe a fluorescent barcoding strategy that allows all samples to be collected, incubated, imaged and analyzed simultaneously, thus decreasing significantly the time of the assay. As an example of application, we perform a droplet digital PCR assay for the quantification of a DNA amplicon from 8 samples in less than 2 hours. We further validate our approach demonstrating the parallel quantification of 11 microRNAs from a human sample using an isothermal nucleic acid amplification chemistry. As an off-chip device, the sample changer can be connected to a variety of microfluidic geometries and therefore, used for a wide range of applications.

Synthesis of programmable reaction-diffusion fronts using DNA catalyzers.

We introduce a DNA-based reaction-diffusion (RD) system in which reaction and diffusion terms can be precisely and independently controlled. The effective diffusion coefficient of an individual reaction component, as we demonstrate on a traveling wave, can be reduced up to 2.7-fold using a self-assembled hydrodynamic drag. The intrinsic programmability of this RD system allows us to engineer, for the first time, orthogonal autocatalysts that counterpropagate with minimal interaction. Our results are in excellent quantitative agreement with predictions of the Fisher-Kolmogorov-Petrovskii-Piscunov model. These advances open the way for the rational engineering of pattern formation in pure chemical RD systems.

Dynamic DNA-toolbox reaction circuits: a walkthrough.

In living organisms, the integration of signals from the environment and the molecular computing leading to a cellular response are orchestrated by Gene Regulatory Networks (GRN). However, the molecular complexity of in vivo genetic regulation makes it next to impossible to describe in a quantitative manner. Reproducing, in vitro, reaction networks that could mimic the architecture and behavior of in vivo networks, yet lend themselves to mathematical modeling, represents a useful strategy to understand, and even predict, the function of GRN. In this paper, we define a set of in vitro, DNA-based molecular transformations that can be linked to each other in such a way that the product of one transformation can activate or inhibit the production of one or several other DNA compounds. Therefore, these reactions can be wired in arbitrary networks. This approach provides an experimental way to reproduce the dynamic features of genetic regulation in a test tube. We introduce the rules to design the necessary DNA species, a guide to implement the chemical reactions and ways to optimize the experimental conditions. We finally show how this framework, or "DNA toolbox", can be used to generate an inversion module, though many other behaviors, including oscillators and bistable switches, can be implemented.

Bottom-up construction of in vitro switchable memories.

Reaction networks displaying bistability provide a chemical mechanism for long-term memory storage in cells, as exemplified by many epigenetic switches. These biological systems are not only bistable but switchable, in the sense that they can be flipped from one state to the other by application of specific molecular stimuli. We have reproduced such functions through the rational assembly of dynamic reaction networks based on basic DNA biochemistry. Rather than rewiring genetic systems as synthetic biology does in vivo, our strategy consists of building simplified dynamic analogs in vitro, in an artificial, well-controlled milieu. We report successively a bistable system, a two-input switchable memory element, and a single-input push-push memory circuit. These results suggest that it is possible to build complex time-responsive molecular circuits by following a modular approach to the design of dynamic in vitro behaviors. Our approach thus provides an unmatched opportunity to study topology/function relationships within dynamic reaction networks.

Quencher-free multiplexed monitoring of DNA reaction circuits.

We present a simple yet efficient technique to monitor the dynamics of DNA-based reaction circuits. This technique relies on the labeling of DNA oligonucleotides with a single fluorescent modification. In this quencher-free setup, the signal is modulated by the interaction of the 3'-terminus fluorophore with the nucleobases themselves. Depending on the nature of the fluorophore's nearest base pair, fluorescence intensity is decreased or increased upon hybridization. By tuning the 3'-terminal nucleotides, it is possible to obtain opposite changes in fluorescence intensity for oligonucleotides whose hybridization site is shifted by a single base. Quenching by nucleobases provides a highly sequence-specific monitoring technique, which presents a high sensitivity even for small oligonucleotides. Compared with other sequence-specific detection methods, it is relatively non-invasive and compatible with the complex dynamics of DNA reaction circuits. As an application, we show the implementation of nucleobase quenching to monitor a DNA-based chemical oscillator, allowing us to follow in real time and quantitatively the dephased oscillations of the components of the network. This cost-effective monitoring technique should be widely implementable to other DNA-based reaction systems.

Coupling Exponential to Linear Amplification for Endpoint Quantitative Analysis.

Exponential DNA amplification techniques are fundamental in ultrasensitive molecular diagnostics. These systems offer a wide dynamic range, but the quantification requires real-time monitoring of the amplification reaction. Linear amplification schemes, despite their limited sensitivity, can achieve quantitative measurement from a single end-point readout, suitable for low-cost, point-of-care, or massive testing. Reconciling the sensitivity of exponential amplification with the simplicity of end-point readout would thus break through a major design dilemma and open a route to a new generation of massively scalable quantitative bioassays. Here a hybrid nucleic acid-based circuit design is introduced to compute a logarithmic function, therefore providing a wide dynamic range based on a single end-point measurement. CELIA (Coupling Exponential amplification reaction to LInear Amplification) exploits a versatile biochemical circuit architecture to couple a tunable linear amplification stage - optionally embedding an inverter function - downstream of an exponential module in a one-pot format. Applied to the detection of microRNAs, CELIA provides a limit of detection in the femtomolar range and a dynamic range of six decades. This isothermal approach bypasses thermocyclers without compromising sensitivity, thereby opening the way to applications in various diagnostic assays, and providing a simplified, cost-efficient, and high throughput solution for quantitative nucleic acid analysis.

Functional analysis of single enzymes combining programmable molecular circuits with droplet-based microfluidics.

The analysis of proteins at the single-molecule level reveals heterogeneous behaviours that are masked in ensemble-averaged techniques. The digital quantification of enzymes traditionally involves the observation and counting of single molecules partitioned into microcompartments via the conversion of a profluorescent substrate. This strategy, based on linear signal amplification, is limited to a few enzymes with sufficiently high turnover rate. Here we show that combining the sensitivity of an exponential molecular amplifier with the modularity of DNA-enzyme circuits and droplet readout makes it possible to specifically detect, at the single-molecule level, virtually any D(R)NA-related enzymatic activity. This strategy, denoted digital PUMA (Programmable Ultrasensitive Molecular Amplifier), is validated for more than a dozen different enzymes, including many with slow catalytic rate, and down to the extreme limit of apparent single turnover for Streptococcus pyogenes Cas9. Digital counting uniquely yields absolute molar quantification and reveals a large fraction of inactive catalysts in all tested commercial preparations. By monitoring the amplification reaction from single enzyme molecules in real time, we also extract the distribution of activity among the catalyst population, revealing alternative inactivation pathways under various stresses. Our approach dramatically expands the number of enzymes that can benefit from quantification and functional analysis at single-molecule resolution. We anticipate digital PUMA will serve as a versatile framework for accurate enzyme quantification in diagnosis or biotechnological applications. These digital assays may also be utilized to study the origin of protein functional heterogeneity.

In Vitro Enzyme Self-Selection Using Molecular Programs.

Directed evolution provides a powerful route for in vitro enzyme engineering. State-of-the-art techniques functionally screen up to millions of enzyme variants using high throughput microfluidic sorters, whose operation remains technically challenging. Alternatively, in vitro self-selection methods, analogous to in vivo complementation strategies, open the way to even higher throughputs, but have been demonstrated only for a few specific activities. Here, we leverage synthetic molecular networks to generalize in vitro compartmentalized self-selection processes. We introduce a programmable circuit architecture that can link an arbitrary target enzymatic activity to the replication of its encoding gene. Microencapsulation of a bacterial expression library with this autonomous selection circuit results in the single-step and screening-free enrichment of genetic sequences coding for programmed enzymatic phenotypes. We demonstrate the potential of this approach for the nicking enzyme Nt.BstNBI (NBI). We applied autonomous selection conditions to enrich for thermostability or catalytic efficiency, manipulating up to 107 microcompartments and 5 × 105 variants at once. Full gene reads of the libraries using nanopore sequencing revealed detailed mutational activity landscapes, suggesting a key role of electrostatic interactions with DNA in the enzyme's turnover. The most beneficial mutations, identified after a single round of self-selection, provided variants with, respectively, 20 times and 3 °C increased activity and thermostability. Based on a modular molecular programming architecture, this approach does not require complex instrumentation and can be repurposed for other enzymes, including those that are not related to DNA chemistry.

Time-encoded electrical detection of trace RNA biomarker by integrating programmable molecular amplifier on chip.

One of the serious challenges facing modern point-of-care (PoC) molecular diagnostic platforms relate to reliable detection of low concentration biomarkers such as nucleic acids or proteins in biological samples. Non-specific analyte-receptor interactions due to competitive binding in the presence of abundant molecules, inefficient mass transport and very low number of analyte molecules in sample volume, in general pose critical hurdles for successful implementation of such PoC platforms for clinical use. Focusing on these specific challenges, this work reports a unique PoC biosensor that combines the advantages of nanoscale biologically-sensitive field-effect transistor arrays (BioFET-arrays) realized in a wafer-scale top-down nanofabrication as high sensitivity electrical transducers with that of sophisticated molecular programs (MPs) customized for selective recognition of analyte miRNAs and amplification resulting in an overall augmentation of signal transduction strategy. The MPs realize a programmable universal molecular amplifier (PUMA) in fluidic matrix on chip and provide a biomarker-triggered exponential release of small nucleic acid sequences easily detected by receptor-modified BioFETs. A common miRNA biomarker LET7a was selected for successful demonstration of this novel biosensor, achieving limit of detection (LoD) down to 10 fM and wide dynamic ranges (10 pM-10 nM) in complex physiological solutions. As the determination of biomarker concentration is implemented by following the electrical signal related to analyte-triggered PUMA in time-domain instead of measuring the threshold shifts of BioFETs, and circumvents direct hybridization of biomarkers at transducer surface, this new strategy also allows for multiple usage (>3 times) of the biosensor platform suggesting exceptional cost-effectiveness for practical use.

Spatial waves in synthetic biochemical networks.

We report the experimental observation of traveling concentration waves and spirals in a chemical reaction network built from the bottom up. The mechanism of the network is an oscillator of the predator-prey type, and this is the first time that predator-prey waves have been observed in the laboratory. The molecular encoding of the nonequilibrium behavior relies on small DNA oligonucleotides that enforce the network connectivity and three purified enzymes that control the reactivity. Wave velocities in the range 80-400 µm min(-1) were measured. A reaction-diffusion model in quantitative agreement with the experiments is proposed. Three fundamental parameters are easy to tune in nucleic acid reaction networks: the topology of the network, the rate constants of the individual reactions, and the diffusion coefficients of the individual species. For this reason, we expect such networks to bring unprecedented opportunities for assaying the principles of spatiotemporal order formation in chemistry.

Programmable Ultrasensitive Molecular Amplifier for Digital and Multiplex MicroRNA Quantification.

Digital bioassays, popularized by digital PCR, provide some of the most robust and accurate methods for nucleic acid quantification. In this chapter, we detail a protocol for digital, isothermal, and multiplex detection of microRNAs, which relies on a recently developed amplification method. Our approach uses programmable ultrasensitive molecular amplifiers (PUMAs) to reveal the presence of target microRNAs randomly isolated in picoliter-size microfluidic droplets. Nonspecific amplification in droplets that do not contain a target is eliminated by an active threshold mechanism. Multiple circuits can be assembled for the multiplex digital detection of up to three targets. We finally present the option of using fluorescent dropcodes to streamline the assay and analyze more than a dozen samples in parallel.

Molecular Computation for Molecular Classification.

DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or inspired by natural computation, molecular programming has gained attention for diagnosis applications. Of particular interest for this review, molecular classifiers have shown promising results for disease pattern recognition and sample classification. Because both input integration and computation are performed in a single tube, at the molecular level, this low-cost approach may come as a complementary tool to molecular profiling strategies, where all biomarkers are quantified independently using high-tech instrumentation. After introducing the elementary components of molecular classifiers, some of their experimental implementations are discussed either using digital Boolean logic or analog neural network architectures.

Silicon chambers for enhanced incubation and imaging of microfluidic droplets.

Droplet microfluidics has become a powerful tool in life sciences, underlying digital assays, single-cell sequencing or directed evolution, and it is making foray in physical sciences as well. Imaging and incubation of droplets are crucial, yet they are encumbered by the poor optical, thermal and mechanical properties of PDMS, a material commonly used in microfluidics labs. Here we show that Si is an ideal material for droplet chambers. Si chambers pack droplets in a crystalline and immobile monolayer, are immune to evaporation or sagging, boost the number of collected photons, and tightly control the temperature field sensed by droplets. We use the mechanical and optical benefits of Si chambers to image ≈1 million of droplets from a multiplexed digital assay - with an acquisition rate similar to the best in-line methods. Lastly, we demonstrate their applicability with a demanding assay that maps the thermal dependence of Michaelis-Menten constants with an array of ≈150 000 droplets. The design of the Si chambers is streamlined to avoid complicated fabrication and improve reproducibility, which makes Si a complementary material to PDMS in the toolbox of droplet microfluidics.

Programming an in vitro DNA oscillator using a molecular networking strategy.

Living organisms perform and control complex behaviours by using webs of chemical reactions organized in precise networks. This powerful system concept, which is at the very core of biology, has recently become a new foundation for bioengineering. Remarkably, however, it is still extremely difficult to rationally create such network architectures in artificial, non-living and well-controlled settings. We introduce here a method for such a purpose, on the basis of standard DNA biochemistry. This approach is demonstrated by assembling de novo an efficient chemical oscillator: we encode the wiring of the corresponding network in the sequence of small DNA templates and obtain the predicted dynamics. Our results show that the rational cascading of standard elements opens the possibility to implement complex behaviours in vitro. Because of the simple and well-controlled environment, the corresponding chemical network is easily amenable to quantitative mathematical analysis. These synthetic systems may thus accelerate our understanding of the underlying principles of biological dynamic modules.

Competition for catalytic resources alters biological network dynamics.

Genetic regulation networks orchestrate many complex cellular behaviors. Dynamic operations that take place within cells are thus dependent on the gene expression machinery, enabled by powerful enzymes such as polymerases, ribosomes, or nucleases. These generalist enzymes typically process many different substrates, potentially leading to competitive situations: by saturating the common enzyme, one substrate may down-regulate its competitors. However, most theoretical or experimental models simply omit these effects, focusing on the pattern of genetic regulatory interactions as the main determinant of network function. We show here that competition effects have important outcomes, which can be spotted within the global dynamics of experimental systems. Further we demonstrate that enzyme saturation creates a layer of cross couplings that may foster, but also hamper, the expected behavior of synthetic biology constructs.

Computing with competition in biochemical networks.

Cells rely on limited resources such as enzymes or transcription factors to process signals and make decisions. However, independent cellular pathways often compete for a common molecular resource. Competition is difficult to analyze because of its nonlinear global nature, and its role remains unclear. Here we show how decision pathways such as transcription networks may exploit competition to process information. Competition for one resource leads to the recognition of convex sets of patterns, whereas competition for several resources (overlapping or cascaded regulons) allows even more general pattern recognition. Competition also generates surprising couplings, such as correlating species that share no resource but a common competitor. The mechanism we propose relies on three primitives that are ubiquitous in cells: multiinput motifs, competition for a resource, and positive feedback loops.

Lessons from Biomass Valorization for Improving Plastic-Recycling Enzymes.

Synthetic polymers such as plastics exhibit numerous advantageous properties that have made them essential components of our daily lives, with plastic production doubling every 15 years. The relatively low cost of petroleum-based polymers encourages their single use and overconsumption. Synthetic plastics are recalcitrant to biodegradation, and mismanagement of plastic waste leads to their accumulation in the ecosystem, resulting in a disastrous environmental footprint. Enzymes capable of depolymerizing plastics have been reported recently that may provide a starting point for eco-friendly plastic recycling routes. However, some questions remain about the mechanisms by which enzymes can digest insoluble solid substrates. We review the characterization and engineering of plastic-eating enzymes and provide some comparisons with the field of lignocellulosic biomass valorization.

Accurate gene consensus at low nanopore coverage.

BACKGROUND: Nanopore technologies allow high-throughput sequencing of long strands of DNA at the cost of a relatively large error rate. This limits its use in the reading of amplicon libraries in which there are only a few mutations per variant and therefore they are easily confused with the sequencing noise. Consensus calling strategies reduce the error but sacrifice part of the throughput on reading typically 30 to 100 times each member of the library. FINDINGS: In this work, we introduce SINGLe (SNPs In Nanopore reads of Gene Libraries), an error correction method to reduce the noise in nanopore reads of amplicons containing point variations. SINGLe exploits that in an amplicon library, all reads are very similar to a wild-type sequence from which it is possible to experimentally characterize the position-specific systematic sequencing error pattern. Then, it uses this information to reweight the confidence given to nucleotides that do not match the wild-type in individual variant reads and incorporates it on the consensus calculation. CONCLUSIONS: We tested SINGLe in a mutagenic library of the KlenTaq polymerase gene, where the true mutation rate was below the sequencing noise. We observed that contrary to other methods, SINGLe compensates for the systematic errors made by the basecallers. Consequently, SINGLe converges to the true sequence using as little as 5 reads per variant, fewer than the other available methods.