Biology subjects

Ung, M.

Publications and source records attributed to Ung, M..

5 recordsLinked to original sources

bioRxiv2025

A Human Genetics Framework for De-risking Gene Editing Targets for Hematopoietic Cell and Gene Therapy

Developing novel therapeutics requires robust early-stage target de-risking to ensure safety and efficacy. We developed a scalable proteogenomic framework integrating population-scale human genetics and plasma proteomics to identify genes tolerant of inactivation (i.e., dispensable) within hematopoietic compartments, thereby enabling safer targeted immunotherapies. Using CD33 as a validated benchmark, we observed that naturally occurring loss-of-function (LoF) variants lead to concordant RNA and protein depletion, supporting functional gene inactivation. Early clinical results from the Trem-Cel trial (NCT05945849) further provide proof of concept that deletion of dispensable antigens can enable safe and effective immunotherapy in humans. We extended this approach genome-wide in the UK Biobank and identified 237 candidate dispensable genes, filtered by plasma proteomic data and hematopoietic expression, highlighting LY75 (CD205) as a novel candidate with strong proteogenomic evidence of LoF tolerance. This work establishes a generalizable, quantitative proteogenomic framework for systematic prioritization of dispensable gene targets for editing, providing a foundation for next-generation cell and gene therapies that minimize on-target, off-tumor toxicities.

genetics↗

bioRxiv2025

scGenAI: A generative AI platform with biological context embedding of multimodal features enhances single cell state classification

SummarySingle-cell sequencing has advanced the understanding of cellular heterogeneity, yet traditional cell type annotation tools struggle with increasingly complex datasets and multimodal integration. Recent large language models (LLMs) offer improved accuracy but rely on pre-trained models with limited gene vocabularies and lack of biological contextualization. These constraints make it challenging to effectively fine-tune pre-trained models, especially for novel datasets, non-human species, or disease-specific studies where unique gene expression patterns are critical. To address these constraints, we developed scGenAI, an LLM-based tool that supports straightforward and flexible de novo training, allowing researchers to incorporate genomic and biofunctional contexts to improve accuracy and interpretability on the prediction of cell states. Here, we demonstrate that scGenAI outperforms conventional models in tasks such as cell type prediction and acute myeloid leukemia (AML) malignant cell states identification, making it a powerful tool for single-cell analysis workflows. Availability and implementationscGenAI (DOI: 10.5281/zenodo.14927611) is distributed as an open-source Python package, with source code and comprehensive documentation accessible on GitHub at https://github.com/VOR-Quantitative-Biology/scGenAI.

bioinformatics↗

bioRxiv2019

An integrative ENCODE resource for cancer genomics

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

genomics↗

bioRxiv2019

Acceptability of active case finding with a seed-and-recruit model to improve tuberculosis case detection and linkage to treatment in Cambodia: a qualitative study

BackgroundWith support of the national tuberculosis (TB) program, KHANA has implemented an innovative approach using a seed-and-recruit model to actively find TB cases in the community. The intervention engaged community members including TB survivors to recruit people with TB symptoms in a snowball approach for screening and linkage to treatment. This study aims to explore the acceptability of active case finding with the seed-and-recruit model in detecting new TB cases and determine the characteristics of successful seeds.\n\nMethodsThis qualitative study was conducted in four provinces (Banteay Meanchey, Kampong Chhnang, Siem Reap, and Takeo) in Cambodia in 2017. Fifty six in-depth interviews and ten focus group discussions were conducted to gain insights into the acceptability, strengths, and challenges in implementing the model. Transcripts were coded and content analyses were performed.\n\nResultsThe seed-and-recruit active case finding model was generally well-received by the study participants. They saw the benefits of engaging TB survivors and utilize their social network to find new TB cases in the community. The social embeddedness of the model within the local community was one of the major strengths. The success of the model also hinges on the integration with existing health facilities. Having extensive social network, being motivated, and having good knowledge about TB were important characteristics of successful seeds. Study participants reported challenges in motivating the recruits for screening, logistic capacities, and high workload during implementation. However, there was a general consensus that the model ought to be expanded.\n\nConclusionsThese findings indicate that the seed-and-recruit model should be fine-tuned and scaled up as part of the national TB Program to increase TB new case detection in Cambodia. Further studies are needed to more comprehensively evaluate the impacts and cost-effectiveness of the model in Cambodia as well as in other resource-limited settings.

epidemiology↗

bioRxiv2018

Pan-cancer systematic identification of lncRNAs associated with cancer prognosis

The \"dark matter\" of the genome harbors several non-coding RNA species including IncRNAs, which have been implicated in neoplasias but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes. In this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types. In total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.

cancer biology↗