Biology subjects

Huang, H.-T.

Publications and source records attributed to Huang, H.-T..

5 recordsLinked to original sources

bioRxiv2026

Proteasomal surveillance of exposed cysteines

Cysteine residues are critical for both the catalytic activity and structural stability of many proteins, yet their high chemical reactivity threatens proteome integrity. Whether cells have specialized mechanisms to recognize and eliminate proteins with aberrant or exposed cysteines is unknown. By systematically analyzing the stability of thousands of cysteine-containing peptides, here we identify two complementary surveillance mechanisms targeting proteins with exposed cysteine residues. First, a set of E3 ubiquitin ligases, including AMBRA1, TRPC4AP, FBXO3, and FBXO21, detects double cysteines and targets proteins for ubiquitin-dependent proteasomal degradation. Second, the PSMD14/Rpn11 subunit of the 26S proteasome recognizes exposed cysteines in a ubiquitin-independent manner. Both pathways function in quality control to clear misfolded proteins exposing cysteine residues, including zinc finger-containing proteins. Mutational escape from the ubiquitin-independent pathway through dysregulation of ornithine decarboxylase or FOS levels is responsible for two human diseases, Bachmann-Bupp Syndrome and osteoblastoma, respectively. Our findings reveal conserved mechanisms for degrading proteins with exposed cysteines and uncover an alternative mode of substrate recognition by the 26S proteasome.

cell biology↗

bioRxiv2025

Co-targeting menin and LSD1 dismantles oncogenic programs and restores differentiation in MLL-rearranged AML

Acute myeloid leukemia (AML) harboring MLL (MLL1, KMT2A) rearrangement (MLL-r) remains a lethal subtype with limited durable responses to single-agent menin inhibition. To define rational combination strategies, we performed a high-throughput screen of >900 epigenetic modulators in combination with menin inhibition in MLL-r AML models. This uncovered consistent synergy between menin and lysine-specific demethylase 1 (LSD1) inhibition, including with the clinical agent iadademstat. Mechanistically, LSD1 was found to interact with LEDGF/p75 (PSIP1), a chromatin-anchoring cofactor of the menin-MLL complex at H3K36me3 marked euchromatin. Chromatin profiling revealed extensive co-occupancy of LSD1 and menin-MLL components at leukemogenic loci in MLL-r AML cells. Dual inhibition of menin and LSD1 dismantled this chromatin complex, evicted H3K36me3 from LEDGF-bound sites, and reprogrammed transcription toward myeloid differentiation. Combined menin and LSD1 blockade repressed canonical MLL targets, including HOXA9, MYC, FLT3, PBX3, and CDK6, while restoring H3K36me3 and H3K4me3 and activating differentiation-associated genes. In vivo, the combination produced potent antileukemic effects in both MOLM-13 and MLL-r patient-derived xenografts, markedly reducing leukemic burden and extending survival without overt toxicity. These findings identify LSD1 as a critical cofactor of the menin-MLL-LEDGF axis and establish concurrent menin and LSD1 inhibition as a mechanistically informed combinatorial therapeutic approach in MLL-r AML.

cancer biology↗

bioRxiv2024

Multi-stage-mixing to create a core-then-shell structure improves DNA-loaded lipid nanoparticles transfection by orders of magnitude

As they became the dominant gene therapy platform, lipid nanoparticles (LNPs) experienced nearly all their innovation in varying the structure of individual molecules in LNPs. This ignored control of the spatial arrangement of molecules, which is suboptimal because supramolecular structure determines function in biology. To control LNPs supramolecular structure, we introduce multi-stage-mixing (MSM) to successively add different molecules to LNPs. We first utilize MSM to create a core-then-shell (CTS) synthesis. CTS-LNPs display a clear core-shell structure, vastly lower frequency of LNPs containing no detectable mRNA, and improved mRNA-LNP expression. With DNA-loaded LNPs, which for decades lagged behind mRNA-LNPs due to low expression, CTS improved DNA-LNPs protein expression by 2-3 orders of magnitude, bringing it within range of mRNA-LNPs. These results show that supramolecular arrangement is critical to LNP performance and can be controlled by mixing methodology. Further, MSM/CTS have finally made DNA-LNPs into a practical platform for long-term gene expression.

bioengineering↗

bioRxiv2024

A Kinetic Scout Approach Accelerates Targeted Protein Degrader Development

Bifunctional molecules such as targeted protein degraders induce proximity to promote gain-of-function pharmacology. These powerful approaches have gained broad traction across academia and the pharmaceutical industry, leading to an intensive focus on strategies that can accelerate their identification and optimization. We and others have previously used chemical proteomics to map degradable target space, and these datasets have been used to develop and train multiparameter models to extend degradability predictions across the proteome. In this study, we now turn our attention to develop generalizable chemistry strategies to accelerate the development of new bifunctional degraders. We implement lysine-targeted reversible-covalent chemistry to rationally tune the binding kinetics at the protein-of-interest across a set of 25 targets. We define an unbiased workflow consisting of global proteomics analysis, IP/MS of ternary complexes and the E-STUB assay, to mechanistically characterize the effects of ligand residence time on targeted protein degradation and formulate hypotheses about the rate-limiting step of degradation for each target. Our key finding is that target residence time is a major determinant of degrader activity, and this can be rapidly and rationally tuned through the synthesis of a minimal number of analogues to accelerate early degrader discovery and optimization efforts.

pharmacology and toxicology↗

bioRxiv2016

lncRNA-screen: an interactive platform for computationally screening long non-coding RNAs in large genomics datasets

Long non-coding RNAs (lncRNAs) have emerged as a class of factors that are important for regulating development and cancer. Computational prediction of lncRNAs from ultra-deep RNA sequencing has been successful in identifying candidate lncRNAs. However, the complexity of handling and integrating different types of genomics data poses significant challenges to experimental laboratories that lack extensive genomics expertise. To address this issue, we have developed lncRNA-screen, a comprehensive pipeline for computationally screening putative lncRNA transcripts over large multimodal datasets. The main objective of this work is to facilitate the computational discovery of lncRNA candidates to be further examined by functional experiments. lncRNA-screen provides a fully automated easy-to-run pipeline which performs data download, RNA-seq alignment, assembly, quality assessment, transcript filtration, novel lncRNA identification, coding potential estimation, expression level quantification, histone mark enrichment profile integration, differential expression analysis, annotation with other type of segmented data (CNVs, SNPs, Hi-C, etc.) and visualization. Importantly, lncRNA-screen generates an interactive report summarizing all interesting lncRNA features including genome browser snapshots and lncRNA-mRNA interactions based on Hi-C data. In summary, our pipeline provides a comprehensive solution for lncRNA discovery and an intuitive interactive report for identifying promising lncRNA candidates. lncRNA-screen is available as free open-source software on GitHub.

bioinformatics↗