Biology subjects

Granik, N.

Publications and source records attributed to Granik, N..

9 recordsLinked to original sources

bioRxiv2026

High avidity phase-separated RNA-protein sialogranules sense lectins and inhibit influenza infection

We previously developed synthetic RNA-protein (sRNP) granules as stable, programmable, genetically encoded nanoparticles. Here, we adapt this platform into high-avidity sialogranules that inhibit influenza virus entry. Natural or synthetic 2,6-sialylated proteins and peptides were fused to RNA-binding domains and assembled with synthetic long non-coding RNA to form phase-separated glyco-nanoparticles. Using the sialic-acid-binding lectin Sambucus nigra agglutinin (SNA), we identified a selective transition from sialogranules to tri-component multiphasic biocondensates that occurs only for sialylated constructs. The structural features of this transition quantitatively correlate with predicted sialylation density, enabling extraction of an effective avidity constant. Enzymatic desialylation with neuraminidase or Endo H abolished SNA binding and restored native granule morphology. Guided by this assay, LAMP1-based sialogranules were selected and inhibited influenza entry by [~]50% in cell culture. These results establish sRNP sialogranules as programmable glyco-nanoparticles integrating glycan sensing with antiviral decoy activity.

synthetic biology↗

bioRxiv2026

Synthetic RNA-protein decoy granules to prevent SARS-CoV-2 infection

Synthetic receptor decoys offer a promising strategy to block viral entry but are often limited by instability and rapid clearance. Here we introduce AntiCoV decoy particles: phase-separated synthetic RNA-protein nanoparticles that display multivalent human ACE2 and act as robust decoy receptors for SARS-CoV-2. The granules remain structurally stable for at least two weeks at room temperature. Using confocal imaging and FRET, we show that the SARS-CoV-2 receptor-binding domain is absorbed into the granule matrix with nanometer-scale proximity to ACE2. In viral entry assays with Delta and Omicron (BA.1) variants, AntiCoV decoy particles achieve complete inhibition of infection at low micromolar concentrations and outperform soluble ACE2, which also exhibits infection enhancement at low doses. A minimal kinetic model suggests a multistep spike-priming mechanism that reconciles this biphasic response. AntiCoV decoy particles therefore provide a stable, modular, and pan-variant antiviral platform with potential for low-cost, room-temperature-stable prophylaxis.

synthetic biology↗

bioRxiv2024

Formation of polyphasic RNP granules by intrinsically disordered Qβ coat proteins and hairpin-containing RNA

RNA-protein (RNP) granules are fundamental components in mammalian cells where they perform multiple crucial functions. Many RNP granules form via phase separation driven by protein-protein, protein-RNA, and RNA-RNA interactions. Notably, associated proteins frequently contain intrinsically disordered regions (IDRs) which can associate with multiple partners. Previously we have shown that synthetic RNA molecules containing multiple hairpin coat-protein binding sites can phase separate, forming granules capable of selectively incorporating proteins inside. Here, we expand this platform by introducing a phage coat protein with a known IDR which facilitates protein-protein interactions. We show that the coat protein phase-separates on its own in vivo, and that introduction of hairpin-containing RNA molecules can lead to dissolvement of the protein granules. We further demonstrate via multiple assays that RNA valency, determined by the number of hairpins present on the RNA, leads to distinctly different phase behaviors, effectively forming a polyphasic programmable RNP granule. Moreover, by incorporating the gene for a blue fluorescent protein into the RNA, we demonstrate a phase-dependent boost to protein titer. These insights not only shed light on the behavior of natural granules, but also hold profound implications for the biotechnology field, offering a blueprint for engineering cellular compartments with tailored functionalities.

biophysics↗

bioRxiv2024

Phase Separation-based Antiviral Decoy Particles as Basis for Programmable Broad-spectrum Therapeutics

To gain access to cells, viruses employ host proteins as receptors. In soluble form, these receptors are used as decoys to inhibit infection. We fused candidate soluble receptors to an RNA-binding protein, and using synthetic long non-coding RNA (slncRNA) cassettes that can undergo phase-separation we scaffolded the receptor fusions to generate antiviral decoy particles. Using confocal microscopy, we screened antiviral protein candidates by observing changes in phase-separation morphology when incubated with viral-mimicking components. We demonstrated that ACE2 decoy particles bind strongly to the coronavirus RBD, facilitating FRET, while sufficiently sialylated decoy particles form agglutinated structures with RNA peripheries in the presence of a sialolectin. Infection assays show ACE2 decoy particles fully inhibit the Delta and Omicron BA.1 coronavirus variants, and LAMP1 and GYPA decoy particles significantly reduce influenza infection in-cellulo. This work establishes a foundation for broad-spectrum antiviral decoy particles, composed of multiple receptors targeting various viruses.

synthetic biology↗

bioRxiv2024

GelMetrics: An algorithm for analyzing the dynamics of gel-like phase separated condensates

Cellular compartments and organelles are essential for the spatial organization of biological matter. Recently, membraneless organelles like paraspeckles, stress granules, and Cajal bodies have garnered significant scientific interest due to their lack of membrane boundaries and crucial cellular functions. These organelles self-assemble through phase separation, a process in which a homogeneous solution separates into distinct phases. The phases most commonly encountered in cells are liquids and gels. Although various microscopy techniques exist to study phase-separated compartments, they are often inadequate for investigating the dynamics of gel-like condensates, where molecular motion occurs over tens of minutes rather than seconds. Here, we introduce the GelMetrics algorithm to quantitatively measure the dynamics of gel-like phase-separated structures by tracking their fluorescence signals over extended durations. First, we show using simulations that this method can identify molecular motion amidst measurement noise and estimate biophysical parameters such as the number of molecules and the average fluorescence of a single molecule exiting a condensate, providing insights into the dynamics of phase-separated organelles. Second, we validate our approach on data from synthetic RNA-protein (sRNP) granules in E. coli and in vitro, establishing its applicability both in vitro and in vivo. Finally, we demonstrate the biological meaning of our algorithms output by quantitatively determining how the presence of an intrinsically disordered region causes sRNP granules to exhibit a more brittle phase-behavior. Thus, not only does the proposed method fill a gap in analysis methods for gel-like condensates, but it also elucidates biophysical mechanisms, improving our understanding of both synthetic and natural systems.

bioinformatics↗

bioRxiv2021

A cell-free assay for rapid screening of inhibitors of hACE2-receptor - SARS-CoV-2-Spike binding

We present a cell-free assay for rapid screening of candidate inhibitors of protein binding, focusing on inhibition of the interaction between the SARS-CoV-2 Spike receptor binding domain (RBD) and human angiotensin-converting enzyme 2 (hACE2). The assay has two components: fluorescent polystyrene particles covalently coated with RBD, termed virion-particles (v-particles), and fluorescently-labeled hACE2 (hACE2F) that binds the v-particles. When incubated with an inhibitor, v-particle - hACE2F binding is diminished, resulting in a reduction in the fluorescent signal of bound hACE2F relative to the non-inhibitor control, which can be measured via flow cytometry or fluorescence microscopy. We determine the amount of RBD needed for v-particle preparation, v-particle incubation time with hACE2F, hACE2F detection limit, and specificity of v-particle binding to hACE2F. We measure the dose response of the v-particles to a known inhibitor. Finally, we demonstrate that RNA-hACE2F granules trap v-particles effectively, providing a basis for potential RNA-hACE2F therapeutics.

synthetic biology↗

bioRxiv2019

Live dynamical tracking of slncRNA speckles in single E. coli cells reveals bursts of fluorescence degradation

Liquid-solid transition, also known as gelation, is a specific form of phase separation in which molecules cross-link to form a highly interconnected compartment with solid - like dynamical properties. Here, we utilize RNA hairpin coat-protein binding sites to form synthetic RNA based gel-like granules via liquid-solid phase transition. We show both in-vitro and in-vivo that hairpin containing synthetic long non-coding RNA (slncRNA) molecules granulate into bright localized puncta. We further demonstrate that upon introduction of the coat-proteins, less-condensed gel-like granules form with the RNA creating an outer shell with the proteins mostly present inside the granule. Moreover, by tracking puncta fluorescence signals over time, we detected addition or shedding events of slncRNA-CP nucleoprotein complexes. Consequently, our granules constitute a genetically encoded storage compartment for protein and RNA with a programmable controlled release profile that is determined by the number of hairpins encoded into the RNA. Our findings have important implications for the potential regulatory role of naturally occurring granules and for the broader biotechnology field.

biophysics↗

bioRxiv2019

Single particle diffusion characterization by deep learning

Diffusion plays a crucial role in many biological processes including signaling, cellular organization, transport mechanisms, and more. Direct observation of molecular movement by single-particle tracking experiments has contributed to a growing body of evidence that many cellular systems do not exhibit classical Brownian motion, but rather anomalous diffusion. Despite this evidence, characterization of the physical process underlying anomalous diffusion remains a challenging problem for several reasons. First, different physical processes can exist simultaneously in a system. Second, commonly used tools to distinguish between these processes are based on asymptotic behavior, which is inaccessible experimentally in most cases. Finally, an accurate analysis of the diffusion model requires the calculation of many observables, since different transport modes can result in the same diffusion power-law , that is obtained from the commonly used mean-squared-displacement (MSD) in its various forms. The outstanding challenge in the field is to develop a method to extract an accurate assessment of the diffusion process using many short trajectories with a simple scheme that is applicable at the non-expert level.\n\nHere, we use deep learning to infer the underlying process resulting in anomalous diffusion. We implement a neural network to classify single particle trajectories according to diffusion type - Brownian motion, fractional Brownian motion (FBM) and Continuous Time Random Walk (CTRW). We further use the net to estimate the Hurst exponent for FBM, and the diffusion coefficient for Brownian motion, demonstrating its applicability on simulated and experimental data. The networks outperform time averaged MSD analysis on simulated trajectories while requiring as few as 25 time-steps. Furthermore, when tested on experimental data, both network and ensemble MSD analysis converge to similar values, with the network requiring half the trajectories required for ensemble MSD. Finally, we use the nets to extract diffusion parameters from multiple extremely short trajectories (10 steps).

biophysics↗

bioRxiv2017

Demonstration of de novo chemotaxis in E. coli using a real-time, quantitative, and digital-like approach

Chemotaxis is the movement of an organism in response to an external chemical stimulus. This system enables bacteria to sense their immediate environment and adapt to changes in its chemical composition. Bacterial chemotaxis is mediated by chemoreceptors, membrane proteins that bind an effector and transduce the signal to the downstream proteins. From a synthetic biology perspective, the natural chemotactic repertoire is of little use since bacterial chemoreceptors have evolved to sense specific ligands that either benefit or harm the cell. Here we demonstrate that using a combined computational design approach together with a quantitative, real-time, and digital detection approach, we can rapidly design, manufacture, and characterize a synthetic chemoreceptor in E. coli for histamine (a ligand for which there are no known chemoreceptors). First, we employed a computational protocol that uses the Rosetta bioinformatics software together with high threshold filters to design mutational variants to the native Tar ligand binding domain that target histamine. Second, we tested different ligand-chemoreceptors pairs with a novel chemotaxis assay, based on optical reflectance interferometry of porous silicon (PSi) optical transducers, enabling label-free quantification of chemotaxis by monitoring real-time changes in the optical readout (expressed as the effective optical thickness, EOT). We found that different ligands can be characterized by an individual set of fingerprints in our assay. Namely, a binary, digital-like response in EOT change (i.e. positive or negative) that differentiates between attractants and repellants, the amplitude of change of EOT response, and the rate by which steady state in EOT change is reached. Using this assay, we were able to positively identify and characterize a single mutational chemoreceptor variant for histamine that mediated chemotaxis comparably to the natural Tar-aspartate system. Our results demonstrate the possibility of not only expanding the natural chemotaxis repertoire, but also provide a new quantitative assay by which to characterize the efficacy of the chemotactic response.

synthetic biology↗