Biomedical subjects

Eunjung Alice Lee

Publications and source records attributed to Eunjung Alice Lee.

4 recordsLinked to original sources

PubMed2026

Recurrent patterns of TOP1-mediated neuronal genomic damage shared by major neurodegenerative disorders.

Amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Alzheimer's disease (AD) represent two major categories of neurodegenerative disorders-TAR DNA-binding protein 43 (TDP-43) and tau proteinopathies-for which the mechanisms driving neuronal death remain unclear. Single-cell whole-genome sequencing of 469 neurons from C9ORF72 ALS, C9ORF72 FTD, AD, and control brains revealed increased somatic single-nucleotide variants (sSNVs) and insertions/deletions (sIndels) in all three diseases. Mutational signature analysis identified a disease-associated sSNV signature consistent with oxidative damage and an sIndel process affecting 22% of ALS, 76% of FTD, and 61% of AD neurons-but only 2% of control neurons-resembling signature ID4, previously linked to topoisomerase 1 (TOP1)-mediated mutagenesis. Rapid approach to DNA adduct recovery (RADAR) assays confirmed increased TOP1-DNA covalent complexes, and duplex sequencing confirmed the increased sIndels and identified single-strand events as likely precursor lesions. TOP1-associated sIndel mutagenesis and genome instability thus represent a mechanism shared by both TDP-43 and tau neurodegeneration.

Humans↗

PubMed2026

Duplex-Indel: a Snakemake pipeline for somatic Indel calling in Tn5 transposase-based duplex sequencing data.

SUMMARY: Duplex-Indel is a novel Snakemake workflow for detecting somatic small insertions and deletions (Indels) from Tn5 transposase-based duplex sequencing data. Duplex-Indel enhances the accuracy of mutation calling at the single-molecule level by requiring consensus support from both DNA strands for each somatic Indel, minimizing confounding from technical artifacts. Duplex-Indel extends somatic mutation calling in Tn5 transposase-based duplex sequencing data to include Indels. We have demonstrated the accuracy and robustness of Duplex-Indel using cancer cell lines. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are available under the MIT license on GitHub at https://github.com/ealee-lab/duplex-indel and archived on Zenodo at https://doi.org/10.5281/zenodo.19228799.

Transposases↗

PubMed2025

STRUMP-I: Structure-based machine learning approach to pMHC-I binding prediction using force field energy features.

The adaptive immune system monitors cellular integrity by recognizing short peptides from intracellular proteins presented on Major Histocompatibility Complex class I (MHC-I) molecules, collectively termed peptide-MHC complexes (pMHC), enabling detection of foreign or mutated proteins. With the rising importance of immunotherapies targeting neoantigens in cancers, the ability to accurately predict which peptides will bind to the diverse population of MHC alleles is critically important. Current computational methods for pMHC-I prediction fall broadly into sequence-based methods, which rely heavily on large training datasets, and structure-based methods that leverage structural modeling and energetics of pMHC binding. While sequence-based methods have been popularly used, their performance is dependent on the size and quality of training data. On the other hands, while structure-based approaches can generalize better across diverse MHC alleles, they traditionally depend on identifying a single global minimum energy conformation, an assumption that often fails due to the inherent binding promiscuity of MHC-I molecules. To address these limitations, we developed a STRUMP-I (STRUcture-based pMHC Prediction (for class I)), a novel pMHC binding prediction tool that directly leverages a broad set of force-field-derived energy terms as machine-learning features. STRUMP-I achieves performance comparable to state-of-the-art sequence-based models while significantly outperforming them on MHC alleles with limited representation in training data. Furthermore, STRUMP-I demonstrates strong synergy when integrated with sequence-based methods, notably enhancing prediction precision. The robustness and generalizability of STRUMP-I were confirmed by evaluating its predictive performance on independent, previously unseen datasets, including an experimentally validated cancer neoantigen dataset. This combined approach advances our capability to reliably identify clinically relevant neoantigen targets. The source code and trained models are available at https://github.com/yoonjoolab/STRUMP-I.

energy optimization↗

PubMed2025

Recurrent patterns of widespread neuronal genomic damage shared by major neurodegenerative disorders.

Amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Alzheimer's disease (AD) are common neurodegenerative disorders for which the mechanisms driving neuronal death remain unclear. Single-cell whole-genome sequencing of 429 neurons from three C9ORF72 ALS, six C9ORF72 FTD, seven AD, and twenty-three neurotypical control brains revealed significantly increased burdens in somatic single nucleotide variant (sSNV) and insertion/deletion (sIndel) in all three disease conditions. Mutational signature analysis identified a disease-associated sSNV signature suggestive of oxidative damage and an sIndel process, affecting 28% of ALS, 79% of FTD, and 65% of AD neurons but only 5% of control neurons (diseased vs. control: OR=31.20, p = 2.35×10-10). Disease-associated sIndels were primarily two-basepair deletions resembling signature ID4, which was previously linked to topoisomerase 1 (TOP1)-mediated mutagenesis. Duplex sequencing confirmed the presence of sIndels and identified similar single-strand events as potential precursor lesions. TOP1-associated sIndel mutagenesis and resulting genome instability may thus represent a common mechanism of neurodegeneration.

Journal Article↗