Gene machines

Abstract:

DNA-based enzymes, also known as DNAzymes, have opened new opportunities for signal generation and amplification in several fields including biosensing. However, biosensor performance can be hampered by heterogeneity in the catalytic activity of such DNAzymes, especially when relying on a limited number of molecules to generate signal. In this regard, single-molecule studies are essential to discern the behavior among such heterogeneous molecules otherwise masked by ensemble measurements. This work presents a novel methodology to study the 10–23 RNA-cleaving DNAzyme at the single-molecule level. By means of measuring the distance-sensitive efficiency of Förster Resonance Energy Transfer using alternating-laser excitation on a superresolution microscope, we determined the kinetics of individual DNAzymes in terms of substrate turnover, rates of different reaction steps, and changes in performance over time. Our results revealed that, despite high concentrations of the reaction cofactor (i.e., Mg²⁺), a maximum of only 70% of the DNAzymes are actively cleaving multiple substrate sequences; the DNAzyme molecules also showed a wide range of substrate turnover rates. Our findings shed new light on the functional diversity of DNAzymes and the importance of exploring sequence modifications to improve their catalytic performance. Ultimately, this work presents a technique to obtain time-dependent information, which could be easily implemented to study other types of enzymes or biomolecular interactions.

Abstract:

Efficient DNA repair is essential for maintaining genome integrity and ensuring cell survival. In Escherichia coli, RecBCD plays a crucial role in processing DNA ends, following a DNA double-strand break (DSB), to initiate repair. While RecBCD has been extensively studied in vitro, less is known about how it contributes to rapid and efficient repair in living bacteria. Here, we use single-molecule microscopy to investigate DNA repair in real time in E. coli. We quantify RecB single-molecule mobility and monitor the induction of the DNA damage response (SOS response) in individual cells. We show that RecB binding to DNA ends caused by endogenous processes leads to efficient repair without SOS induction. In contrast, repair is less efficient in the presence of exogenous damage or in a mutant strain with modified RecB activities, leading to high SOS induction. Our findings reveal how subtle alterations in RecB activity profoundly impact the efficiency of DNA repair in E. coli.

Abstract:

Rapid antibiotic susceptibility tests (ASTs) are an increasingly important part of clinical care as antimicrobial resistance (AMR) becomes more common in bacterial infections. Here, we use the spatial distribution of fluorescently labelled ribosomes to detect intracellular changes associated with antibiotic susceptibility in E. coli cells using a convolutional neural network (CNN). By using ribosome-targeting probes, one fluorescence image provides data for cell segmentation and susceptibility phenotyping. Using 60,382 cells from an antibiotic-susceptible laboratory strain of E. coli, we showed that antibiotics with different mechanisms of action result in distinct ribosome phenotypes, which can be identified by a CNN with high accuracy (99%, 98%, 95%, and 99% for ciprofloxacin, gentamicin, chloramphenicol, and carbenicillin). With 6 E. coli strains isolated from bloodstream infections, we used 34,205 images of ribosome phenotypes to train a CNN that could classify susceptible cells with 91% accuracy and resistant cells with 99% accuracy. Such accuracies correspond to the ability to differentiate susceptible and resistant samples with 99% confidence with just 2 cells, meaning that this method could eliminate lengthy culturing steps and could determine susceptibility with 30 min of antibiotic treatment. The ribosome phenotype method should also be able to identify phenotypes in other strains and species.

Abstract:

Biosensors play key roles in medical research and diagnostics. However, the development of biosensors for new biomolecular targets of interest often involves tedious optimization steps to ensure a high signal response at the analyte concentration of interest. Here we show a modular nanosensor platform that facilitates these steps by offering ways to decouple and independently tune the signal output as well as the response window. Our approach utilizes a dynamic DNA origami nanostructure to engineer a high optical signal response based on fluorescence resonance energy transfer. We demonstrate mechanisms to tune the sensor's response window, specificity and cooperativity as well as highlight the modularity of the proposed platform by extending it to different biomolecular targets including more complex sensing schemes. This versatile nanosensor platform offers a promising starting point for the rapid development of biosensors with tailored properties.

Abstract:

The present study demonstrates the applicability of the Electrical Impedance Tomography (EIT) technique for real-time monitoring of inspiration and expiration behavior in a respiratory phantom device. The phantom device, which serves as a mechano-electrical simulator of the human respiratory system, is coupled to a real-time monitoring instrument operating based on the EIT technique. This study reveals that the whole system could act as a helpful apparatus for researchers and physicians in improving their ventilation maneuvers for patients. The phantom specifically helps in designing and examining the results of a larger number of experiments, setting up more qualified test environments, and finally more optimal tuning of ventilator devices. The device’s physical appearance and structure resemble the human’s chest cage, making it suitable to be used as a model of the human respiratory system. Experimental results support the applicability of the phantom and EIT system for real-time monitoring of local aerations in different experimental conditions. Additionally, several recorded and analyzed data leads us to better processing and understanding of the EIT technique and its capabilities in respiration studies. The current work could be considered as a proof of concept and a step towards automatically and intelligently suggesting ventilator settings for optimal adoption of treatment strategies and patient management in hospitals in the future.