Biology subjects

Chan, T. A.

Publications and source records attributed to Chan, T. A..

4 recordsLinked to original sources

bioRxiv2026

Neural cell state modulation by PARK2 and dopaminergic neuroprotection by small molecule Parkin agonism

Parkin, an E3 ubiquitin ligase encoded by PARK2, plays a key role in both hereditary and sporadic Parkinsons disease (PD), yet there are no therapies currently available that can target this important pathway. Here, we show that Parkin is critical for successful neuronal differentiation and survival, and we develop small-molecule Parkin agonists that can protect dopaminergic neurons. Upon differentiation of neural progenitor cells, loss of Parkin results in a reduced capacity to maintain neuronal cell state, dopaminergic neuronal phenotypes, and stress resistance. Moreover, Parkin loss disrupted cell morphology and the stability of neurites. Transcriptional and single-cell analyses reveal that Parkin controls critical pathways regulating stem-like cell transitions and is needed for stable neuronal maturation. We also examined the effects of FB231, a small molecule enhancer of Parkin E3 ligase activity, in models of PD. FB231 reduced pathological -synuclein and enhanced cell survival in human iPSC-derived dopaminergic neurons treated with -synuclein preformed fibrils. Furthermore, FB231 attenuated -synuclein pathology and dopaminergic neurodegeneration in a gut -synuclein murine model of PD. Our findings support that Parkin plays a crucial role in maintaining neuronal homeostasis and that pharmacologic activation of Parkin may be a promising strategy to attenuate neurodegeneration in PD.

neuroscience↗

bioRxiv2025

Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP): Next-Generation Neoantigen Prediction with Quantum Neural Networks

The immune system is an intricately evolved series of cellular and protein-protein interactions, which defend the body against pathogens and abnormal cells such as cancer. A key player in the immunologic recognition of non-self is the immune synapse, where T cell receptors (TCRs) scan peptides presented on major histocompatibility complex (MHC) molecules to detect and eliminate cells displaying non-self antigens. While this interaction is vital for vaccine and immunotherapy success, the underlying rules of TCR recognition remain poorly understood. This is only further challenged as the application of predictive models is very limited due to small training datasets. While traditional machine learning models excel at predicting neoantigen binding to MHC, they often struggle to accurately predict immunogenicity. To address these challenges, we developed a quantum computing approach using Quantum Convolutional Neural Networks (QCNNs). Here we present Quantum Convolutional Human Leukocyte Antigen (HLA) Immunogenic Peptide Prediction (Q-CHIPP), the first application of quantum hardware based on training/predicting both MHC binding and immunogenicity in a combinatorial approach. Additionally, we present a large scale use of quantum hardware at scale with 46 qubits. This study underscores how quantum technology can be used for biological modeling and presents a scalable QCNN design with the potential to overcome current computational bottlenecks as quantum hardware advances.

immunology↗

bioRxiv2021

Resource-efficient pooled sequencing expands translational impact in solid tumors

Intratumoral genetic heterogeneity (ITH) poses a significant challenge to utilizing sequencing for decision making in the management of cancer. Although sequencing of multiple tumor regions can address the pitfalls of ITH, it does so at a significant increase in cost and resource utilization. We propose a pooled multiregional sequencing strategy, whereby DNA aliquots from multiple tumor regions are mixed prior to sequencing, as a cost-effective strategy to boost translational value by addressing ITH while preserving valuable residual tissue for secondary analysis. Focusing on kidney cancer, we demonstrate that DNA pooling from as few as two regions significantly increases mutation detection while reducing clonality misattribution. This leads to an increased fraction of patients identified with therapeutically actionable mutations, improved patient risk stratification, and improved inference of evolutionary trajectories with an accuracy comparable to bona fide multiregional sequencing. The same approach applied to non-small-cell lung cancer data substantially improves tumor mutational burden (TMB) detection. Our findings demonstrate that pooled DNA sequencing strategies are a cost-effective alternative to address intrinsic genetic heterogeneity in clinical settings.

genomics↗

bioRxiv2018

G-quadruplex DNA drives genomic instability and represents a targetable molecular abnormality in ATRX-deficient malignant glioma

Mutational inactivation of ATRX (-thalassemia mental retardation X-linked) represents a defining molecular alteration in large subsets of malignant glioma. Yet the pathogenic consequences of ATRX deficiency remain unclear, as do tractable mechanisms for its therapeutic targeting. Here we report that ATRX loss in isogenic glioma model systems induces replication stress and DNA damage by way of G-quadruplex (G4) DNA secondary structure. Moreover, these effects are associated with the acquisition of disease-relevant copy number alterations over time. We then demonstrate, both in vitro and in vivo, that ATRX deficiency selectively enhances DNA damage and cell death following chemical G4 stabilization. Finally, we show that G4 stabilization synergizes with other DNA-damaging therapies, including ionizing radiation, in the ATRX-deficient context. Our findings reveal novel pathogenic mechanisms driven by ATRX deficiency in glioma, while also pointing to tangible strategies for drug development.

cancer biology↗