Biomedical subjects

Yongmei Zhao

Publications and source records attributed to Yongmei Zhao.

6 recordsLinked to original sources

PubMed2026

Identification of elements determining KIR gene demethylation at the CD56-bright stage of NK cell development.

The variegated expression of the KIR family of class I MHC receptors generates specialized natural killer (NK) cells capable of allele-specific HLA recognition. Understanding the mechanism of KIR gene activation will lead to improved methods for the generation of fully functional NK cells. A central RUNX-binding site in the KIR proximal promoter is required for gene activation. RUNX proteins recruit ten-eleven translocation (TET) proteins that generate 5-hydroxymethylcytosine (5hmC) and drive DNA demethylation. Assessment of 5-methylcytosine (5mC) and 5hmC residues at four stages of NK cell development reveals deposition of 5hmC primarily in a CREB site next to the RUNX site at the CD56Bright stage but not the subsequent CD56Dim stage representing fully mature NK cells. KIR promoter demethylation is delayed relative to other lineage-associated genes, indicating a high threshold for KIR gene demethylation in developing NK cells, and a window of opportunity for RUNX/TET-dependent KIR gene activation in CD56Bright NK cells.

6-base sequencing↗

PubMed2025

Accurate somatic small variant discovery for multiple sequencing technologies with DeepSomatic.

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages in repeat mapping and variant phasing. We present DeepSomatic, a deep-learning method for detecting somatic small nucleotide variations and insertions and deletions from both short-read and long-read data. The method has modes for whole-genome and whole-exome sequencing and can run on tumor-normal, tumor-only and formalin-fixed paraffin-embedded samples. To train DeepSomatic and help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available the Cancer Standards Long-read Evaluation (CASTLE) dataset of six matched tumor-normal cell line pairs whole-genome sequenced with Illumina, PacBio HiFi and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples, both cell line and patient-derived, and across short-read and long-read sequencing technologies, DeepSomatic consistently outperforms existing callers.

Humans↗

PubMed2025

Long-read sequencing of single cell-derived melanoma subclones reveals divergent and parallel genomic and epigenomic evolutionary trajectories.

Tumor evolution is driven by various mutational processes, ranging from single-nucleotide variants (SNVs) to large structural variants (SVs) to dynamic shifts in DNA methylation. Current short-read sequencing methods struggle to accurately capture the full spectrum of these genomic and epigenomic alterations due to inherent technical limitations. To overcome that, here we introduce an approach for long-read sequencing of single-cell derived subclones, and use it to profile 23 subclones of a mouse melanoma cell line, characterized with distinct growth phenotypes and treatment responses. We develop a computational framework for harmonization and joint analysis of different variant types in the evolutionary context. Uniquely, our framework enables detection of recurrent amplifications of putative driver genes, generated by independent SVs across different lineages, suggesting parallel evolution. In addition, our approach revealed gradual and lineage-specific methylation changes associated with aggressive clonal phenotypes. We also show our set of phylogeny-constrained variant calls along with openly released sequencing data can be a valuable resource for the development of new computational methods.

Journal Article↗

PubMed2025

Intrathecally expanded GZMK+/GZMH+ CD8 T cells targeting EBV antigens may reduce severity of Multiple Sclerosis.

Combining cerebrospinal fluid B cell receptor and T cell receptor repertoire analysis with transcriptional/ flow cytometry cellular profiles in hundreds of deeply-phenotyped people with Multiple Sclerosis (pwMS) and controls, we identified intrathecal expansion of anti-viral, cytotoxic, granzymes H/K (GZMH+/GZMK+) double positive (DP) CD8+ T cells that recognize EBV epitopes in pwMS. DP CD8+ T cells are activated and expanded by, and kill autologous, EBV-infected CSF B cell lines in-vitro. Correlations of surrogate transcriptional profiles with clinical and imaging outcomes infer a beneficial role for EBV-targeting DP CD8+ T cells, as untreated pwMS with proportionally higher DP CD8+ T cells to intrathecal B cells accumulate neurological disability slower. MS therapies also increase ratios of beneficial CD8+ T cell responses to intrathecal B cells, consistent with their ability to inhibit disability progression. This study provides indirect evidence that intrathecal EBV infection participates in disability accumulation in pwMS.

Journal Article↗

PubMed2025

Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing.

For the detection of somatic structural variation (SV) in cancer genomes, long-read sequencing is advantageous over short-read sequencing with respect to mappability and variant phasing. However, most current long-read SV detection methods are not developed for the analysis of tumor genomes characterized by complex rearrangements and heterogeneity. Here, we present Severus, a breakpoint graph-based algorithm for somatic SV calling from long-read cancer sequencing. Severus works with matching normal samples, supports unbalanced cancer karyotypes, can characterize complex multibreak SV patterns and produces haplotype-specific calls. On a comprehensive multitechnology cell line panel, Severus consistently outperforms other long-read and short-read methods in terms of SV detection F1 score (harmonic mean of the precision and recall). We also illustrate that compared to long-read methods, short-read sequencing systematically misses certain classes of somatic SVs, such as insertions or clustered rearrangements. We apply Severus to several clinical cases of pediatric leukemia/lymphoma, revealing clinically relevant cryptic rearrangements missed by standard genomic panels.

Humans↗

PubMed2024

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies.

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies now offer potential advantages in terms of repeat mapping and variant phasing. We present DeepSomatic, a deep learning method for detecting somatic SNVs and insertions and deletions (indels) from both short-read and long-read data, with modes for whole-genome and exome sequencing, and able to run on tumor-normal, tumor-only, and with FFPE-prepared samples. To help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available a dataset of five matched tumor-normal cell line pairs sequenced with Illumina, PacBio HiFi, and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples and technologies (short-read and long-read), DeepSomatic consistently outperforms existing callers, particularly for indels.

Journal Article↗