Biology subjects

Chen, X. D.

Publications and source records attributed to Chen, X. D..

6 recordsLinked to original sources

bioRxiv2026

Deep-learning-based design of an orthogonal self-labeling protein from K-Ras(G12C)

Self-labeling protein (SLP) tags enable versatile labeling of proteins in live cells, yet only two SLPs are commonly used, limiting multiplexing. Here, we repurposed the oncoprotein K-Ras(G12C) and its covalent inhibitors to create a third, orthogonal SLP system. We used a deep-learning-based approach to radically redesign the sequence of K-Ras(G12C) while preserving its covalent-inhibitor binding pocket. Our top design, LUCI-tag, is a 19 kDa, monomeric, thermostable SLP that rapidly and covalently reacts with commercially available K-Ras(G12C) inhibitors bearing diverse payloads. Unlike existing SLPs, LUCI-tag exhibited payload-agnostic labeling kinetics, outperforming HaloTag7 and SNAP-tag for a negatively charged payload. X-ray crystal structures of drug-bound and drug-free LUCI-tag showed the design was structurally accurate and contained a preorganized inhibitor-binding pocket that could explain its rapid labeling kinetics. Proteomics and cell-signaling experiments confirmed LUCI-tag is biologically inert. LUCI-tag enabled rapid, wash-free, live-cell imaging and simultaneous three-color multiplexed experiments with HaloTag7 and SNAP-tag. This work establishes LUCI-tag as an immediately useful orthogonal SLP and demonstrates that covalent drug-target pairs can be repurposed into a broadly applicable platform for protein labeling.

bioengineering↗

bioRxiv2026

BioVault: A privacy-first data visitation platform for equitable global collaboration in biomedicine

Biomedical datasets representing diverse populations are essential for advancing precision medicine, yet remain siloed due to regulatory, sovereignty, and privacy constraints. Existing data-sharing solutions remain limited. Centralized repositories and Trusted Research Environments (TREs) require data migration into external infrastructure, introducing governance complexities, cost barriers, and constraints on accountable reuse. Secure computation frameworks from modern cryptography, and federated learning models demand deep domain expertise and substantial engineering effort, limiting their accessibility in resource-constrained environments. These barriers disproportionately impact under-resourced institutions, limiting equitable participation in global collaborations. Here, we introduce BioVault (https://www.biovault.net), an open-source platform for privacy-first biomedical collaboration through a peer-to-peer data visitation network, where analysis code travels to data rather than data being transferred to centralized systems. BioVault can be deployed as a desktop application or command-line interface, enabling out-of-the-box use for participants in diverse resource settings. BioVault supports both clinical and research workflows, and with built-in permissioning, audit trails, and local governance controls, it enables data holders and collaborators to retain oversight and control while participating in collaborative research. We demonstrate BioVaults utility in enabling two cross-border collaborations with under-resourced communities: a genome-wide association study of Type 2 Diabetes in Circassian and Chechen populations in Jordan, and allele frequency estimation across Caribbean cohorts. In both cases, analyses were executed locally without data export. We further demonstrate compatibility for user-defined secure, federated computation protocols built on state-of-the-art cryptographic tools (multiparty computation and homomorphic encryption), by lowering barriers to their deployment. Together, these results establish BioVault as a general-purpose framework for decentralized analytics that reduces the technical and financial constraints to biomedical collaboration - enabling diverse institutions to participate as equal partners in global discovery, while preserving privacy and data sovereignty.

genomics↗

bioRxiv2026

Understanding Coral Health from Reactor Engineering Perspective: Multiphysics Modeling of Coral - Environment Interactions

Coral, as a bioreactor, has to continuously interact with surrounding environment to maintain a healthy state. A multi-physics reaction engineering model has been developed to capture this interaction. The coral interior is modeled as interconnected reaction units respectively for photosynthesis, respiration, and calcification, whose reaction kinetics are influenced by environmental fluctuations. Coupling between coral and environment is realized by bi-directional mass transfer at the coral-seawater interface, with consideration of the unique flow fields induced by ciliary beating. By resorting to this comprehensive model, we discover that ciliary beating demonstrates distinctively different diurnal and nocturnal functions. During daytime, beating can help reduce photosynthetic oxygen accumulation to prevent hyperoxia-induced mortality, while enhancing carbon dioxide uptake efficiency to promote nutrient production. At night, however, beating promotes oxygen acquisition for adequate respiration, while expelling carbon dioxide to inhibit symbiotic destruction under acidic stress. The model further enables mechanistic analysis of the detrimental impact of climate change on coral health, where the influences from two key factors (i.e., temperature and CO2 level) can be decoupled. Its interesting to find out that the elevated temperature plays a dominant role during daytime, while at night the coral is dominantly influenced by rising CO2 level.

systems biology↗

bioRxiv2025

Generative Design of Cell Type-Specific RNA Splicing Elements for Programmable Gene Regulation

Programmable control of gene expression in specific cell types is essential for both basic discovery and therapeutic intervention, yet current strategies lack scalability across diverse cellular contexts. Here, we introduce SPICE (Splicing Proportions In Cell types), an integrated experimental and computational framework that harnesses alternative RNA splicing as a programmable modality for cell type-specific gene regulation. To power SPICE, we constructed a massively parallel reporter assay (MPRA) comprising 46,372 human-derived sequences and profiled exon skipping across 43 cell lines spanning 10 lineages, uncovering widespread cell type-specific exon skipping. Using this data, we trained deep learning models that both predict splicing in unseen contexts and generate synthetic sequences with programmed, cell type-specific splicing patterns. Leveraging these models, we further engineered sequences that selectively splice in cells harboring oncogenic splicing factor mutations, demonstrating translational potential. SPICE provides a generalizable strategy for dissecting splicing regulation and engineering alternative splicing as a gene expression regulatory layer for research and therapeutic applications. One Sentence SummaryWe introduce SPICE, an integrated framework that couples large-scale splicing assays with generative design to uncover regulatory principles and design programmable, cell-specific gene expression for research and therapeutic applications.

molecular biology↗

bioRxiv2024

Helicase-assisted continuous editing for programmable mutagenesis of endogenous genomes

A major challenge in human genomics is to decipher the context specific relationship of sequence to function. However, existing tools for locus specific hypermutation and evolution in the native genome context are limited. Here we present a novel programmable platform for long-range, locus-specific hypermutation called helicase-assisted continuous editing (HACE). HACE leverages CRISPR-Cas9 to target a processive helicase-deaminase fusion that incurs mutations across large (>1000 bp) genomic intervals. We applied HACE to identify mutations in MEK1 that confer kinase inhibitor resistance, to dissect the impact of individual variants in SF3B1-dependent mis-splicing, and to evaluate noncoding variants in a stimulation-dependent immune enhancer of CD69. HACE provides a powerful tool for investigating coding and noncoding variants, uncovering combinatorial sequence-to-function relationships, and evolving new biological functions. One Sentence SummaryWe developed a tool for continuous, long-range, targeted diversification of endogenous mammalian genomes and used it to explore the function of genetic variants in both coding and non-coding regions.

genetics↗

bioRxiv2022

Programmable eukaryotic protein expression with RNA sensors

The diversity of cell types and states can be scalably measured and defined by expressed RNA transcripts. However, approaches to programmably sense and respond to the presence of specific RNAs within living biological systems with high sensitivity are lacking. RNA sensors that gate expression of reporter or cargo genes would have diverse applications for basic biology, diagnostics and therapeutics by enabling cell-state specific control of transgene expression. Here, we engineer a novel programmable RNA-sensing technology, Reprogrammable ADAR Sensors (RADARS), which leverages RNA editing by adenosine deaminases acting on RNA (ADAR) to gate translation of a protein payload on the presence of endogenous RNA transcripts. In mammalian cells, we engineer RADARS with diverse payloads, including luciferase and fluorescent proteins, with up to 164-fold activation and quantitative detection in the presence of target RNAs. We show RADARS are functional either expressed from DNA or as synthetic mRNA. Importantly, RADARS can function with endogenous cellular ADAR. We apply RADARS to multiple contexts, including RNA-sensing induced cell death via caspases, cell type identification, and in vivo control of synthetic mRNA translation, demonstrating RADARS as a tool with significant potential for gene and cell therapy, synthetic biology, and biomedical research. One Sentence SummaryA new technology utilizing ADAR mediated RNA-editing enables robust reprogrammable protein expression based on target RNA transcripts in mammalian cells, leading to broad applications in basic science research, cell engineering, and gene therapy.

genomics↗