Biology subjects

Chen, L.-F.

Publications and source records attributed to Chen, L.-F..

7 recordsLinked to original sources

bioRxiv2026

FXR and BET signaling orchestrate to protect β cells

In both type 1 and type 2 diabetes (T1D and T2D), insulin-producing {beta} cells undergo progressive dysfunction due to inflammation, leading to impaired glucose responsiveness, dedifferentiation, and cell loss. While bile acid (BA) dysregulation under diabetic conditions is known to influence metabolic and inflammatory pathways, its mechanistic role in {beta} cell regulation remains incompletely defined1-3. Here we show that bile acid sensor Farnesoid X receptor (FXR) and Bromodomain and Extra-Terminal motif (BET) signaling cooperatively regulates {beta} cell inflammatory response and {beta} cell identity. We identified the physiological protein-protein interaction between FXR and the bromodomain-containing protein 4 (BRD4) as a regulatory axis that protects against {beta} cell dysfunction. We show that FXR activation by Fexaramine (Fex) together with BRD4 inhibition by JQ1 synergistically suppressed IL-1{beta}-induced inflammation while also improving {beta} cell identity and insulin secretion in both db/db model and high-fat diet (HFD) plus multi low-dose streptozotocin (MLD-STZ) model of diabetes. Importantly, this cooperative effect is abolished in {beta} cell-specific FXR knockout ({beta}FXRKO) mice, establishing that FXR is required for the functional synergy between these pathways in vivo. Mechanistically, structure-guided modeling and mutational analyses identified a direct interaction between FXR and the BD2 domain of BRD4, depending on specific lysine acetylation sites. Additionally, inhibition of the BD2 domain of BET combined with FXR activation markedly improved {beta} cell survival in human T1D and T2D models established from human pluripotent stem cell (hPSC)-derived islet-like organoids (HILOs). Collectively, these findings establish a BA-bromodomain axis as a transcriptional interface linking metabolic signaling and chromatin regulation, and highlight FXR-BET targeting as a promising strategy to counter progressive {beta} cell failure in diabetes.

molecular biology↗

bioRxiv2025

Kinetic organization of the genome revealed by ultra-resolution, multiscale live imaging

In the last decade, sequencing methods like Hi-C have made it clear the genome is intricately folded, and that this organization contributes significantly to the control of gene expression and thence cell fate and behavior. Single-cell DNA tracing microscopy and polymer physics-based simulations of genome folding have proposed these population-scale patterns arise from motor- driven, heterogeneous movement, rather than stable 3D genomic architecture, implying that motion, rather than structure, is key to understanding genome function. However, tools to directly observe this motion in vivo have been limited in coverage and resolution. Here we describe TRansposon Assisted Chromatin Kinetic Imaging Technology (TRACK-IT), which combines a suite of imaging and labeling improvements to achieve ultra-resolution in space and time, with self-mapping transposons to distribute labels across the chromosome, uncovering dynamic behaviors across four orders of magnitude of genomic separation. We find that sequences separated by sub-megabase distances, typically 200-500 nm of nanometers apart, can transition to close proximity in tens of seconds - faster than previously hypothesized. This rapid motion is dependent upon cohesin and is exhibited only within certain genomic domains. Domain borders act as kinetic impediments to this search process, substantially slowing the rate and frequency of the transition to proximity. The genomic separation-dependent scaling of the search time for cis-interactions within a domain violates predictions of diffusion, suggesting motor driven folding. This distinctive scaling is lost following cohesin depletion, replaced with a behavior consistent with diffusion. Finally, we found cohesin containing cells exhibited rare, processive movements, not seen in cohesin depleted cells. These processive trajectories exhibit extrusion rates of [~]2.7 kb/s across three distinct genomic intervals, faster than recent in vitro measurements and prior estimates from in vivo data. Taken together, these results reveal a genome in motion across multiple genomic and temporal scales, where motor-dependent extrusion divides the sequence, not into spatially separate domains, but into kinetically separated domains that experience accelerated local search.

biophysics↗

bioRxiv2025

Forecast Padding Enhances Accuracy and Robustness of EEG-Phase-Synchronized TMS

Closed-loop neuromodulation is a promising personalized treatment for various neuropsychiatric disor-ders, delivering precise stimuli based on real-time brain signals. However, its clinical potential is currently limited by technical challenges inherent in its real-time nature. This article addresses the primary technical challenges in EEG-Phase-Synchronized Transcranial Magnetic Stimulation (TMS), including poor stimulation accuracy and inefficient biomarker detection (correlated with deadlock). These challenges arise from the vulnerability of existing algorithms to filter edge effects. Inspired by predictive coding theory in neuroscience, we propose a novel signal padding method (forecast padding) to mitigate the filter edge effect. To properly quantify the improvements that forecast padding brings about in real-world systems, we introduce a novel delay-relevant validation framework and demonstrate its reliability using experimental data from a real system. Through this framework, we demonstrate that forecast padding significantly improves both stimulation accuracy and deadlock rate. Given the pervasive impact of filter edge effects in closed-loop neuromodulation and other signal processing domains, forecast padding shows broad application potential across various fields.

bioengineering↗

bioRxiv2024

Long range regulation of transcription scales with genomic distance in a gene specific manner

While critical for tuning the timing and level of transcription, enhancer communication with distal promoters is not well understood. Here we bypass the need for sequence-specific transcription factors and recruit activators directly using CARGO-VPR, an approach for targeting dCas9-VPR using a multiplexed array of RNA guides. We show that this approach achieves effective activator recruitment to arbitrary genomic sites, even those inaccessible by single dCas9. We utilize CARGO-VPR across the Prdm8-Fgf5 locus in mESCs, where neither gene is expressed. We demonstrate that while activator recruitment to any tested region results in transcriptional induction of at least one gene, the expression level strongly depends on the genomic distance between the promoter and activator recruitment site. However, the expression-distance relationship for each gene scales distinctly in a manner not attributable to differences in 3D contact frequency, promoter DNA sequence or presence of the repressive chromatin marks at the locus.

molecular biology↗

bioRxiv2022

Structural elements facilitate extreme long-range gene regulation at a human disease locus

Enhancer clusters overlapping disease-associated mutations in Pierre Robin sequence (PRS) patients regulate SOX9 expression at genomic distances over 1.25 megabases. We applied optical reconstruction of chromatin architecture (ORCA) imaging to trace 3D locus topology during PRS-enhancer activation. While we observed pronounced changes in locus topology between cell-types, analysis of single chromatin fiber traces revealed that these ensemble-average differences arise not from the presence of cell-type unique conformations, but through changes in frequency of commonly sampled topologies. We further identified two CTCF-bound elements, internal to the SOX9 topologically associating domain, which are positioned near its 3D geometric center and bridge enhancer-promoter contacts in a series of chromatin loops. Ablation of these elements results in diminished SOX9 expression and altered domain-wide contacts. Polymer models with uniform loading across the domain and frequent cohesin collisions recapitulate this multiloop, centrally clustered geometry, suggesting a mechanism for gene regulation over ultralong ranges. Four short bullet points that convey the key message of the paperSOX9 domain topology dynamically changes during a developmental transition Structural elements promote TAD-wide interactions, stripe formation and transcription Structural elements are CTCF-dependent and situated centrally in the 3D TAD structure Polymer simulations of multi-loop model best recapitulate topological features

molecular biology↗

bioRxiv2020

Discovery of rhodomyrtone as a broad-spectrum antiviral inhibitor with anti-SARS-CoV-2 activity

The outbreak of new viruses, such as serve acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as well as the emerging of drug-resistance viruses highlight the urgent need for the development of broad-spectrum antiviral drugs. Herein, we report the discovery of a plant-derived small molecule, 6,8-dihydroxy-9-isobutyl-2,2,4,4-tetramethyl-7-(3-methylbutanoyl)-4,9-dihydro-1H-xanthene-1,3(2H)-dione (rhodomyrtone, RDT), which exhibited potent broad-spectrum antiviral activities against several RNA and DNA viruses, including SARS-CoV-2, respiratory syncytial virus (RSV), herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), varicella-zoster virus (VZV), human cytomegalovirus (HCMV), and Kaposis sarcoma-associated herpesvirus (KSHV). RDT can significantly suppress viral gene expression and show the low possibility to elicit drug-resistant variants. Mechanistic study implied that RDT inhibited viral infection by disturbing the cellular factors that essential for viral gene expression. Our results suggested that RDT might be a promising lead compound for the development of broad-spectrum antiviral drugs.

pharmacology and toxicology↗

bioRxiv2017

Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

Single-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution on the abundance and localization of nascent and mature transcripts in single cells. Gene expression dynamics are typically inferred by measuring mRNA abundance in small numbers of fixed cells sampled from a population at multiple time-points after induction. The sparse data that arise from the small number of cells obtained using smFISH present a challenge for inferring transcription dynamics. Here, we developed a computational pipeline (BayFish) to infer kinetic parameters of gene expression from smFISH data at multiple time points after induction. Given an underlying model of gene expression, BayFish uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data. We tested BayFish on smFISH measurements of the neuronal activity inducible gene Npas4 in primary neurons. We showed that a 2-state promoter model can recapitulate Npas4 dynamics after induction and we inferred that the transition rate from the promoter OFF state to the ON state is increased by the stimulus.\n\nAuthor SummaryGene expression can exhibit cell-to-cell variability due to the stochastic nature of biochemical reactions. Single cell assays (e.g. smFISH) directly quantify stochastic gene expression by measuring the number of active promoters and transcripts per cell in a population of cells. The data are distributions and their shape and time-evolution contain critical information on the underlying process of gene expression. Recent work has combined models of stochastic gene expression with maximum likelihood methods to infer kinetic parameters from smFISH distributions. However, these approaches do not provide a probability distribution or likelihood of model parameters inferred from the smFISH data. This information is useful because it indicates which parameters are loosely constrained by the data and suggests follow up experiments. We developed a suite of MATLAB programs (BayFish) that estimate the Bayesian posterior probability of model parameters from smFISH data. The user specifies an underlying model of stochastic gene expression with unknown parameters ({theta}) and provides smFISH data (Y). BayFish uses a Monte Carlo algorithm to estimate the Bayesian posterior probability P({theta}|Y) of model parameters. BayFish is easily modified and can be applied to other models of stochastic gene expression and smFISH data sets.

systems biology↗