Single-molecule analysis of DNA base-stacking energetics using patterned DNA nanostructures

Simulating the Lyotropic Phase Behavior of a Partially Self-Complementary DNA Tetramer

DNA oligomers in solution have been found to develop liquid crystal phases via a hierarchical process that involves Watson-Crick base pairing, supramolecular assembly into columns of duplexes, and long-range ordering. The multiscale nature of this phenomenon makes it difficult to quantitatively describe and assess the importance of the various contributions, particularly for very short strands. We performed molecular dynamics simulations based on the coarse-grained oxDNA model, aiming to depict all of the assembly processes involved and the phase behavior of solutions of the DNA GCCG tetramers. We find good quantitative matching to experimental data at both levels of molecular association (thermal melting) and collective ordering (phase diagram). We characterize the isotropic state and the low-density nematic and high-density columnar liquid crystal phases in terms of molecular order, size of aggregates, and structure, together with their effects on diffusivity processes. We observe a cooperative aggregation mechanism in which the formation of dimers is less thermodynamically favored than the formation of longer aggregates.

MLDSPP: Bacterial Promoter Prediction Tool Using DNA Structural Properties with Machine Learning and Explainable AI

Bacterial promoters play a crucial role in gene expression by serving as docking sites for the transcription initiation machinery. However, accurately identifying promoter regions in bacterial genomes remains a challenge due to their diverse architecture and variations. In this study, we propose MLDSPP (Machine Learning and Duplex Stability based Promoter prediction in Prokaryotes), a machine learning-based promoter prediction tool, to comprehensively screen bacterial promoter regions in 12 diverse genomes. We leveraged biologically relevant and informative DNA structural properties, such as DNA duplex stability and base stacking, and state-of-the-art machine learning (ML) strategies to gain insights into promoter characteristics. We evaluated several machine learning models, including Support Vector Machines, Random Forests, and XGBoost, and assessed their performance using accuracy, precision, recall, specificity, F1 score, and MCC metrics. Our findings reveal that XGBoost outperformed other models and current state-of-the-art promoter prediction tools, namely Sigma70pred and iPromoter2L, achieving F1-scores >95% in most systems. Significantly, the use of one-hot encoding for representing nucleotide sequences complements these structural features, enhancing our XGBoost model's predictive capabilities. To address the challenge of model interpretability, we incorporated explainable AI techniques using Shapley values. This enhancement allows for a better understanding and interpretation of the predictions of our model. In conclusion, our study presents MLDSPP as a novel, generic tool for predicting promoter regions in bacteria, utilizing original downstream sequences as nonpromoter controls. This tool has the potential to significantly advance the field of bacterial genomics and contribute to our understanding of gene regulation in diverse bacterial systems.

Bacterial Patterning: A Promising Biofabrication Technique

Bacterial patterning has emerged as a pivotal biofabrication technique in the biomedical field. In the past 2 decades, a diverse array of bacterial patterning approaches have been developed to enable the precise manipulation of the spatial distribution of bacterial patterns for various applications. Despite the significance of these advancements, there is a deficiency of review articles providing an overview of bacterial patterning technologies. In this mini-review, we systematically summarize the progress of bacterial patterning over the past 2 decades. This review commences with an elucidation of the definition and fundamental principles of bacterial patterning. Subsequently, we introduce the established bacterial patterning strategies, accompanied by discussions about the advantages and limitations of each approach. Furthermore, we showcase the biomedical applications of these strategies, highlighting their efficacy in spatial control of biofilms, biosensing, and biointervention. Finally, this mini-review is concluded with a summary and an outlook on future challenges and opportunities. It is anticipated that this mini-review can serve as a concise guide for those who are interested in this exciting and rapidly evolving research area.

Cryosectioning-enabled super-resolution microscopy for studying nuclear architecture at the single protein level

DNA-PAINT combined with total Internal Reflection Fluorescence (TIRF) microscopy enables the highest localization precisions, down to single nanometers in thin biological samples, due to TIRF's unique method for optical sectioning and attaining high contrast. However, most cellular targets elude the accessible TIRF range close to the cover glass and thus require alternative imaging conditions, affecting resolution and image quality. Here, we address this limitation by applying ultrathin physical cryosectioning in combination with DNA-PAINT. With "tomographic & kinetically-enhanced" DNA-PAINT (tokPAINT), we demonstrate the imaging of nuclear proteins with sub-3 nanometer localization precision, advancing the quantitative study of nuclear organization within fixed cells and mouse tissues at the level of single antibodies. We believe that ultrathin sectioning combined with the versatility and multiplexing capabilities of DNA-PAINT will be a powerful addition to the toolbox of quantitative DNA-based super-resolution microscopy in intracellular structural analyses of proteins, RNA and DNA in situ.

Kinetic Referencing Allows Identification of Epigenetic Cytosine Modifications by Single-Molecule Hybridization Kinetics and Superresolution DNA-PAINT Microscopy

We develop a DNA origami-based internal kinetic referencing system with a colocalized reference and target molecule to provide increased sensitivity and robustness for transient binding kinetics. To showcase this, we investigate the subtle changes in binding strength of DNA oligonucleotide hybrids induced by cytosine modifications. These cytosine modifications, especially 5-methylcytosine but also its oxidized derivatives, have been increasingly studied in the context of epigenetics. Recently revealed correlations of epigenetic modifications and disease also render them interesting biomarkers for early diagnosis. Internal kinetic referencing allows us to probe and compare the influence of the different epigenetic cytosine modifications on the strengths of 7-nucleotide long DNA hybrids with one or two modified nucleotides by single-molecule imaging of their transient binding, revealing subtle differences in binding times. Interestingly, the influence of epigenetic modifications depends on their position in the DNA strand, and in the case of two modifications, effects are additive. The sensitivity of the assay indicates its potential for the direct detection of epigenetic disease markers.

Diversity of Self-Assembled RNA Complexes: From Nanoarchitecture to Nanomachines

New tool development for various nucleic acid applications is an essential task in RNA nanotechnology. Here, we determined the ability of self-limited complex formation by a pair of oligoribonucleotides carrying two pairwise complementary blocks connected by a linker of different lengths in each chain. The complexes were analyzed using UV melting, gel shift assay analysis, and molecular dynamics (MD) simulations. We have demonstrated the spontaneous formation of various self-limited and concatemer complexes. The linear concatemer complex is formed by a pair of oligomers without a linker in at least one of them. Longer linkers resulted in the formation of circular complexes. The self-limited complexes formation was confirmed using the toehold strand displacement. The MD simulations indicate the reliability of the complexes' structure and demonstrate their dynamics, which increase with the rise of complex size. The linearization of 2D circular complexes into 1D structures and a reverse cyclization process were demonstrated using a toehold-mediated approach. The approach proposed here for the construction and directed modification of the molecularity and shape of complexes will be a valuable tool in RNA nanotechnology, especially for the rational design of therapeutic nucleic acids with high target specificity and the programmable response of the immune system of organisms.