Biology subjects

Cohn, D.

Publications and source records attributed to Cohn, D..

3 recordsLinked to original sources

bioRxiv2024

RNA Chemical Probing Reagents and Protein Amino Acids: a Double-Edged Sword

RNA chemical probing experiments are a broadly used method for revealing the structure of RNA, as well as for identifying protein binding sites. This is beneficial for expanding our understanding of biological processes governed by protein-RNA complex interactions, as well as facilitating the identification of complex inhibiting molecules. The reagents commonly used in chemical probing experiments are highly reactive, methylating or acylating flexible RNA nucleotides. The highly reactive nature of the chemical probes means that they can also react with nucleophilic amino acid side chains, and subsequently affect protein-RNA binding events. We combine molecular dynamics (MD) simulations, matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS), and nuclear magnetic resonance (NMR) experiments to show that commonly used RNA chemical probes react with protein amino acids, and demonstrate that this effect alters protein-RNA binding interactions through binding shift assays. We discuss the implications of this phenomenon in elucidating the protein-RNA interaction interface using chemical probing experiments. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/586119v2_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@13378baorg.highwire.dtl.DTLVardef@3b0114org.highwire.dtl.DTLVardef@1c3f967org.highwire.dtl.DTLVardef@12afaec_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2020

Clusters of bacterial RNA polymerase are biomolecular condensates that assemble through liquid-liquid phase separation

Once described as mere "bags of enzymes", bacterial cells are in fact highly organized, with many macromolecules exhibiting non-uniform localization patterns. Yet the physical and biochemical mechanisms that govern this spatial heterogeneity remain largely unknown. Here, we identify liquid-liquid phase separation (LLPS) as a mechanism for organizing clusters of RNA polymerase (RNAP) in E. coli. Using fluorescence imaging, we show that RNAP quickly transitions from a dispersed to clustered localization pattern as cells enter log phase in nutrient-rich media. RNAP clusters are sensitive to hexanediol, a chemical that dissolves liquid-like compartments in eukaryotic cells. In addition, we find that the transcription antitermination factor NusA forms droplets in vitro and in vivo, suggesting that it may nucleate RNAP clusters. Finally, we use single-molecule tracking to characterize the dynamics of cluster components. Our results indicate that RNAP and NusA molecules move inside clusters, with mobilities faster than a DNA locus but slower than bulk diffusion through the nucleoid. We conclude that RNAP clusters are biomolecular condensates that assemble through LLPS. This work provides direct evidence for LLPS in bacteria and suggests that this process serves as a universal mechanism for intracellular organization across the tree of life. SignificanceBacterial cells are small and were long thought to have little to no internal structure. However, advances in microscopy have revealed that bacteria do indeed contain subcellular compartments. But how these compartments form has remained a mystery. Recent progress in larger, more complex eukaryotic cells has identified a novel mechanism for intracellular organization known as liquid-liquid phase separation. This process causes certain types of molecules to concentrate within distinct compartments inside the cell. Here, we demonstrate that the same process also occurs in bacteria. This work, together with a growing body of literature, suggests that liquid-liquid phase separation is a universal mechanism for intracellular organization that extends across the tree of life.

cell biology↗

bioRxiv2018

Enhancer Identification using Transfer and Adversarial Deep Learning of DNA Sequences

Enhancer sequences regulate the expression of genes from afar by providing a binding platform for transcription factors, often in a tissue-specific or context-specific manner. Despite their importance in health and disease, our understanding of these DNA sequences, and their regulatory grammar, is limited. This impairs our ability to identify new enhancers along the genome, or to understand the effect of enhancer mutations and their role in genetic diseases.\n\nWe trained deep Convolutional Neural Networks (CNN) to identify enhancer sequences in multiple species. We used multiple biological datasets, including simulated sequences, in vivo binding data of single transcription factors and genome-wide chromatin maps of active enhancers in 17 mammalian species. Our deep networks obtained high classification accuracy by combining two training strategies: First, training on enhancers vs. non-enhancer background sequences, we identified short (1-4bp) low-complexity motifs. Second, by replacing the negative training set by adversarial k-order random shuffles of enhancer sequences (thus maintaining base composition while shuttering longer motifs, including transcription factor binding sites), we identified a set of biologically meaningful motifs, unique to enhancers. In addition, classification performance improved when combining positive data from all species together, showing a shared mammalian regulatory architecture.\n\nOur results demonstrate that design of adversarial training data, and transfer of learned parameters between networks trained on different species/datasets improve the overall performance and capture biologically meaningful information in the parameters of the learned network.\n\nContact: or.zuk@mail.huji.ac.il, tommy@cs.huji.ac.il

bioinformatics↗