Biology subjects

Hoang, C.

Publications and source records attributed to Hoang, C..

4 recordsLinked to original sources

bioRxiv2026

Programmable DNA Assemblies Reconstitute Supramolecular Protein Function

Biological processes are often regulated through reactions involving supramolecular protein complexes organized with nanoscale precision, such as the apoptosome that activates procaspase-9 through defined stoichiometry to induce apoptosis. Here we report designer CASCAD (CARD Assembly on SCAffold DNA), a self-assembled DNA nanoplatform that spatially organizes caspase recruitment domains (CARDs), exhibiting catalytic intracellular caspase-9 activation. By functioning like a natural apoptosome, CASCAD enhances caspase-9 oligomerization and promotes valency-dependent enzymatic activation. Further, after incorporating targeting aptamers and membrane-interacting peptides for cellular uptake and cytosolic delivery, CASCAD has successfully reconstituted apoptosome function and downstream apoptotic signaling in in-vitro 2D and 3D cultures. Our molecular design paradigm establishes CASCAD as versatile and programmable DNA nanomaterial for reconstructing signaling pathways driven by diverse supramolecular complexes, including inflammasomes and myddosomes, providing a general chemical framework for engineering cellular signaling and directing cellular function and fate.

synthetic biology↗

bioRxiv2025

Phase Separation Oppositely Modulates G-quadruplex and i-Motif DNA Folding in the Nuclei of Living Cells

G-quadruplexes and i-motifs are non-canonical DNA structures that are important not only as targets of gene regulation in cancer therapy, but also as nanotools with biomedical applications. However, their apparent in vitro stabilities and dynamics cast doubt upon their physiological relevance and utility in vivo. Here we investigate folding of the thrombin-binding aptamer G-quadruplex and the TAA(C5TAA)4 i-motif. We use fast relaxation imaging, which couples fluorescence microscopy with laser-induced temperature jumps, to quantify the stability and dynamics of FRET-labelled constructs in vitro and in the nuclei of living cells. Our work suggests that the stability and kinetics of i-motif and G-quadruplex DNA are carefully tuned by phase separation to fit the seconds-to-minutes timescales of key regulatory processes inside cells. Taken together, our findings underscore the importance of studying the folding of DNA structures under near-physiological conditions to their mechanistic understanding as well as for the development of DNA-targeted drugs. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/661982v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@e76eedorg.highwire.dtl.DTLVardef@23aefforg.highwire.dtl.DTLVardef@b6117aorg.highwire.dtl.DTLVardef@135736a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Fast and scalable querying of eukaryotic linear motifs with gget elm

MotivationEukaryotic linear motifs (ELMs), or Short Linear Motifs (SLiMs), are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for cataloging motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt IDs on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited. ResultsTo allow swift, large-scale motif analyses on protein sequences using ELMs curated on the ELM database, we have developed a Python and command line tool, gget elm, which relies on local computations for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt identifiers. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences. Availability and implementationThe manual and source code are available at https://github.com/pachterlab/gget.

bioinformatics↗

bioRxiv2019

Clinically-relevant cell type cross-talk identified from a human lung tumor microenvironment interactome

Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway. To develop a deeper understanding of the interactions between cells within human lung tumors we performed RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We mapped the cell-specific differential expression of prognostically-associated secreted factors and cell surface genes, and computationally reconstructed cross-talk between these cell types to generate a novel resource we call the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identified and validated a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also found a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior. These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.\n\nSummaryRNA-seq profiling of sorted populations from primary lung cancer samples identifies prognostically relevant cross-talk between cell types in the tumor microenvironment.

cancer biology↗