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Single-cell and multi-omics integration reveals cholesterol biosynthesis as a synergistic target with HER2 in aggressive breast cancer

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computational and Structural Biotechnology Journal 2025年 27卷 1719-1731页

作者： Tseng, Tzu-Yang Hsieh, Chiao-Hui Liu, Jie-Yu Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan

Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell survival, proliferation, and potentially modulating therapeutic responsiveness. Therefore, decoding the intricate molecular mechanisms and identifying novel therapeutic targets through systematic computational approaches are essential steps toward advancing effective breast cancer treatments. In this study, we developed an integrative computational framework that combines single-cell RNA sequencing (scRNA-seq) and multi-omics analyses to delineate the functional characteristics of malignant cell subsets in breast cancer patients. Our analyses revealed a significant correlation between cholesterol biosynthesis and HER2 expression in malignant breast cancer cells, supported by proteomics data, gene expression profiles, drug treatment scores, and cell-surface HER2 intensity measurements. Given previous evidence linking cholesterol biosynthesis to HER2 membrane dynamics, we proposed a combinatorial strategy targeting both pathways. Experimental validation through clonogenic and viability assays demonstrated that simultaneous inhibition of cholesterol biosynthesis (via statins) and HER2 (via Neratinib) synergistically reduced malignant breast cancer cells, even in HER2-negative contexts. Through systematic analysis of scRNA-seq and multi-omics data, our study computationally identified and experimentally validated cholesterol biosynthesis and HER2 as novel combinatorial therapeutic targets in breast cancer. This data-driven approach highlights the potential of leveraging multiple molecular profiling techniques to uncover previously unexplored treatment strategies. © 2025 The Authors

关键词： Drug infusion

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E2Former: A Linear-time Efficient and Equivariant Transformer for Scalable Molecular Modeling

arXiv

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arXiv 2025年

作者： Li, Yunyang Huang, Lin Ding, Zhihao Wang, Chu Wei, Xinran Yang, Han Wang, Zun Liu, Chang Shi, Yu Jin, Peiran Zhang, Jia Gerstein, Mark B. Qin, Tao Department of Computer Science Yale University New Haven United States Microsoft Research AI4Science The Hong Kong Polytechnic University Hong Kong Huazhong University of Science and Technology Wuhan China Program in Computational Biology and Biomedical Informatics Yale University New Haven United States

Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science. However, EGNNs face substantial computational challenges due to the high cost of constructing edge features via spherical tensor products, making them impractical for large-scale systems. To address this limitation, we introduce E2Former, an equivariant and efficient transformer architecture that incorporates the Wigner 6j convolution (Wigner 6j Conv). By shifting the computational burden from edges to nodes, the Wigner 6j Conv reduces the complexity from O(|E|) to O(|V|) while preserving both the model’s expressive power and rotational equivariance. We show that this approach achieves a 7x–30x speedup compared to conventional SO(3) convolutions. Furthermore, our empirical results demonstrate that the derived E2Former mitigates the computational challenges of existing approaches without compromising the ability to capture detailed geometric information. This development could suggest a promising direction for scalable and efficient molecular modeling. © 2025, CC BY.

关键词： Large scale systems

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The effect of stereochemical constraints on the radius of gyration of folded proteins

arXiv

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arXiv 2025年

作者： Logan, Jack A. Sumner, Jacob Grigas, Alex T. Shattuck, Mark D. O'Hern, Corey S. Department of Mechanical Engineering Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Biomedical Informatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Applied Physics Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States

Proteins are composed of linear chains of amino acids that fold into complex three-dimensional structures. A general feature of folded proteins is that they are compact with a radius of gyration Rg(N) ∼ Nν that obeys power-law scaling with the number of amino acids N in each protein and ν ∼ 1/3. In this study, we investigate the internal scaling of the radius of gyration Rg(n) versus the chemical separation n between amino acids for all subchains of length n. We show that for globular proteins Rg(n) ∼ nν1,2 with a larger exponent ν1 > 1/3 for small n and a smaller exponent ν2 g(N) ∝ Nν. To describe this scaling behavior for Rg(n), we carry out folding simulations for a series of coarse-grained models for proteins beginning with the freely-jointed and freely-rotating chain models composed of spherical monomers and varying degrees of stereochemical constraints. We show that a minimal model, which coarse-grains amino acids into a single spherical backbone atom and one variable-sized spherical side-chain atom, and enforces bend- and dihedral-angle constraints, can recapitulate Rg(n) for x-ray crystal structures of globular proteins. In addition, this model predicts the correct average packing fraction and size of the hydrophobic core, which are two key physical features that can be used to distinguish between computational 'decoys' and correctly folded proteins in protein design applications. © 2025, CC BY.

关键词： Spheres

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Transcriptionally distinct malignant neuroblastoma populations show selective response to adavosertib treatment

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Neurotherapeutics 2025年第3期22卷 e00575-e00575页

作者： Hsieh, Chiao-Hui Chen, Yi-Xuan Tseng, Tzu-Yang Li, Albert Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan

Neuroblastoma is an aggressive childhood cancer that arises from the sympathetic nervous system. Despite advances in treatment, high-risk neuroblastoma remains difficult to manage due to its heterogeneous nature and frequent development of drug resistance. Drug repurposing guided by single-cell analysis presents a promising strategy for identifying new therapeutic options. Here, we aim to characterize high-risk neuroblastoma subpopulations and identify effective repurposed drugs for targeted treatment. We performed single-cell transcriptomic analysis of neuroblastoma samples, integrating bulk RNA-seq data deconvolution with clinical outcomes to define distinct malignant cell states. Using a systematic drug repurposing pipeline, we identified and validated potential therapeutic agents targeting specific high-risk neuroblastoma subpopulations. Single-cell analysis revealed 17 transcriptionally distinct neuroblastoma subpopulations. Survival analysis identified a highly aggressive subpopulation characterized by elevated UBE2C/PTTG1 expression and poor patient outcomes, distinct from a less aggressive subpopulation with favorable prognosis. Drug repurposing screening identified Adavosertib as particularly effective against the aggressive subpopulation, validated using SK-N-DZ cells as a representative model. Mechanistically, Adavosertib suppressed cell proliferation through AKT/mTOR pathway disruption, induced G2/M phase cell cycle arrest, and promoted apoptosis. Further analysis revealed UBE2C and PTTG1 as key molecular drivers of drug resistance, where their overexpression enhanced proliferation, Adavosertib resistance, and cell migration. This study establishes a single-cell-based drug repurposing strategy for high-risk neuroblastoma treatment. Our approach successfully identified Adavosertib as a promising repurposed therapeutic agent for targeting specific high-risk neuroblastoma subpopulations, providing a framework for developing more effective personalized treat

关键词： Adavosertib AKT/mTOR pathway Cancer therapy Neuroblastoma Single cell RNA sequencing

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Quantum variational autoencoder utilizing regularized mixed-state latent representations

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Physical Review A 2025年第4期111卷 042416-042416页

作者： Gaoyuan Wang Jonathan Warrell Prashant S. Emani Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA NEC Laboratories America Princeton New Jersey 08540 USA Department of Computer Science Yale University New Haven Connecticut 06520 USA Department of Statistics & Data Science Yale University New Haven Connecticut 06520 USA Department of Biomedical Informatics & Data Science Yale University New Haven Connecticut 06520 USA

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared with

关键词： Quantum computers

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Unbiased Multiplex Antigen Screening of Cerebrospinal Fluid Detects Microbial and Autoantigenic Epitopes Associated with Multiple Sclerosis

SSRN

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SSRN 2025年

作者： Barton, Nathaniel J. Tran, Khanh Olson, Meagan N. Deshpande, Mugdha Radu, Irina Francis, Nimmy Kurban, Mariana Martin-Puig, Aurelie Wilner, Molly Orszulak, Adrian R. Chigas, Samantha M. Sundstrom, Jonathan Dawes, Pepper Murray, Liam F. Baiby, Marcus Ionete, Carolina Lim, Elaine T. Hemond, Christopher C. Chan, Yingleong Department of Neurology University of Massachusetts Chan Medical School WorcesterMA01605 United States Department of Medicine Division of Innate Immunity University of Massachusetts Chan Medical School WorcesterMA01605 United States NeuroNexus Institute University of Massachusetts Chan Medical School WorcesterMA01605 United States Department of Genomics and Computational Biology University of Massachusetts Chan Medical School WorcesterMA01605 United States Department of Molecular Cell and Cancer Biology University of Massachusetts Chan Medical School WorcesterMA01605 United States Graduate Program in Biochemistry & Molecular Biotechnology Morningside Graduate School of Biomedical Sciences University of Massachusetts Chan Medical School WorcesterMA01605 United States Department of Medicine Division of Cardiovascular Medicine University of Massachusetts Chan Medical School WorcesterMA01605 United States Graduate Program in Immunology and Microbiology Morningside Graduate School of Biomedical Sciences University of Massachusetts Chan Medical School WorcesterMA01605 United States Graduate Program in Neuroscience Morningside Graduate School of Biomedical Sciences University of Massachusetts Chan Medical School WorcesterMA01605 United States

To comprehensively investigate the intrathecal antibody profile of multiple sclerosis (MS), we examined the cerebrospinal fluid of 195 patients (92 MS and 103 non-MS) for antibodies using a multiplex unbiased bacteria peptide library. We first tested against Epstein-Barr nuclear antigen 1 (EBNA1) for epitope sites enriched in MS and found a significant enrichment at position 407-419. We then used the data to perform a high-throughput screen against a library of 129 viruses known to infect humans. We discovered several additional epitopes from viruses such as Hantaan virus, Human Herpesvirus 6A and Human respiratory syncytial virus B associated with MS. Besides viral epitopes, we also screened for potential autoantigens of the central nervous system (CNS). We discovered several autoantigenic epitopes in proteins such as ADRB3, HTR3A and MPO that were significantly enriched for MS. Because of previous associations of Toxoplasma gondii infection with MS, we also performed a Toxoplasma gondii specific analysis and discovered additional epitopes enriched for MS. We further assessed epitope-epitope correlations within the patient samples and found distinct patterns of association between these microbial and autoantigenic epitopes. Finally, we performed machine-learning to determine if these epitopes are predictive for MS and found that the model incorporating all the epitopes could most effectively discriminate between MS and non-MS (ROC-AUC score = 0.91). Our results demonstrate the effectiveness of multiplex unbiased screens to detect the identity of potentially cross-reactive antibodies targeting MS CNS epitopes and they can also be used as effective biomarkers for MS. © 2025, The Authors. All rights reserved.

关键词： Epitopes

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Insights into the binding interactions of Macitentan to bovine serum Albumin: A Spectroscopic, electrochemical and computational study

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Journal of Molecular Liquids 2025年 429卷

作者： Sadak, Selenay Dindar, Çiğdem Kanbeş Kabir, Md.Zahirul Rehman, Fazal Mohamad, Saharuddin B. Yildirim, Sercan Uslu, Bengi Ankara University Faculty of Pharmacy Department of Analytical Chemistry Ankara06560 Turkey Ankara University The Graduate School of Health Sciences Ankara06110 Turkey Biochemistry Program Institute of Biological Sciences Faculty of Science University Malaya Kuala Lumpur Malaysia Faculty of Science Bioinformatics Programme Institute of Biological Sciences Malaysia Centre of Research for Computational Sciences and Informatics for Biology Bioindustry Environment Agriculture and Healthcare University of Malaya Kuala Lumpur Malaysia Karadeniz Technical University Faculty of Pharmacy Department of Analytical Chemistry Trabzon Turkey

Macitentan (MACI) is an endothelial receptor antagonist used to treat pulmonary arterial hypertension (PAH). The pharmacokinetics and therapeutic efficacy of MACI are significantly influenced by its interaction with plasma proteins, especially bovine serum albumin (BSA). In this study, spectroscopic (fluorescence and UV–visible absorption spectroscopy), electrochemical, and computational techniques were applied to explore the binding properties and interaction mechanisms between MACI and BSA. The results indicated that the MACI quenched BSA fluorescence through a static mechanism. The interaction possessed a moderate binding affinity with Ka values ranging from 1.27 × 104 to 7.25 × 104 M−1 at different temperatures. Hydrophobic forces and hydrogen bonds were identified as critical factors in stabilizing the complex based on thermodynamic parameters. The local environment of tryptophan residues in BSA was found to be altered following the addition of MACI, as revealed by synchronous and three-dimensional fluorescence spectra. The spectroscopic studies were supported by electrochemical analyses, which indicated the formation of an electro-inactive complex between MACI and BSA. From the molecular docking experiments, the most likely binding site for MACI was found to be site I of BSA. Several hydrogen bonds and hydrophobic contacts were observed during the interaction of MACI with site I of BSA. These findings provided detailed insight to understand the binding properties of MACI to BSA, which may be beneficial for medical and pharmaceutical applications in the future. © 2025 Elsevier B.V.

关键词： Hydrogen bonds

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Tuning the Size and Stiffness of Inflatable Particles

arXiv

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arXiv 2025年

作者： Pashine, Nidhi Wang, Dong Baines, Robert Goyal, Medha Shattuck, Mark D. O’Hern, Corey S. Kramer-Bottiglio, Rebecca Department of Mechanical Engineering Yale University New HavenCT06520 United States Department of Physics BioInspired Institute Syracuse University SyracuseNY13244 United States Department of Physics Benjamin Levich Institute City College of New York New York CityNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Biomedical Informatics Yale University New HavenCT06520 United States

We describe size-varying cylindrical particles made from silicone elastomers that can serve as building blocks for robotic granular materials. The particle size variation, which is achieved by inflation, gives rise to changes in stiffness under compression. We design and fabricate inflatable particles that can become stiffer or softer during inflation, depending on key parameters of the particle geometry, such as the ratio of the fillet radius to the wall thickness, r/t. We also conduct numerical simulations of the inflatable particles and show that they only soften during inflation when localization of large strains occurs in the regime r/t → 0. This work introduces novel particle systems with tunable size and stiffness that can be implemented in numerous soft robotic applications. © 2025, CC BY.

关键词： Stiffness

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Generalization is not a universal guarantee: Estimating similarity to training data with an ensemble out-of-distribution metric

arXiv

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arXiv 2025年

作者： Schreyer, W. Max Anderson, Christopher Thompson, Reid F. Computational Biology Program Oregon Health & Science University PortlandOR United States Department of Biomedical Engineering Oregon Health & Science University PortlandOR United States Portland VA Research Foundation PortlandOR United States Department of Radiation Medicine Oregon Health & Science University PortlandOR United States Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University PortlandOR United States Division of Hospital and Specialty Medicine VA Portland Healthcare System PortlandOR United States

Failure of machine learning models to generalize to new data is a core problem limiting the reliability of AI systems, partly due to the lack of simple and robust methods for comparing new data to the original training dataset. We propose a standardized approach for assessing data similarity in a model-agnostic manner by constructing a supervised autoencoder for generalizability estimation (SAGE). We compare points in a low-dimensional embedded latent space, defining empirical probability measures for k-Nearest Neighbors (kNN) distance, reconstruction of inputs and task-based performance. As proof of concept for classification tasks, we use MNIST and CIFAR-10 to demonstrate how an ensemble output probability score can separate deformed images from a mixture of typical test examples, and how this SAGE score is robust to transformations of increasing severity. As further proof of concept, we extend this approach to a regression task using non-imaging data (UCI Abalone). In all cases, we show that out-of-the-box model performance increases after SAGE score filtering, even when applied to data from the model’s own training and test datasets. Our out-of-distribution scoring method can be introduced during several steps of model construction and assessment, leading to future improvements in responsible deep learning implementation. Copyright © 2025, The Authors. All rights reserved.

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Characterizing Emergency Department Care for Patients With Histories of Incarceration

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JACEP Open 2025年第1期6卷 100022-100022页

作者： Huang, Thomas Socrates, Vimig Ovchinnikova, Polina Faustino, Isaac Kumar, Anusha Safranek, Conrad Chi, Ling Wang, Emily A. Puglisi, Lisa Wong, Ambrose H. Wang, Karen H. Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Department for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States SEICHE Center for Health and Justice Yale School of Medicine New Haven CT United States Equity Research and Innovation Center Yale School of Medicine Yale University New Haven CT United States

Objectives: Patients with a history of incarceration experience bias from health care team members, barriers to privacy, and a multitude of health care disparities. We aimed to assess care processes delivered in emergency departments (EDs) for people with histories of incarceration. Methods: We utilized a fine-tuned large language model to identify patient incarceration status from 480,374 notes from the ED setting. We compared socio-demographic characteristics, comorbidities, and care processes, including disposition, restraint use, and sedation, between individuals with and without a history of incarceration. We then conducted multivariable logistic regression to assess the independent correlation of incarceration history and management in the ED while adjusting for demographic characteristics, health behaviors, presentation, and past medical history. Results: We found 1734 unique patient encounters with a history of incarceration from a total of 177,987 encounters. Patients with history of incarceration were more likely to be male, Black, Hispanic, or other race/ethnicity, currently unemployed or disabled, and had smoking and substance use histories, compared with those without. This cohort demonstrated higher odds of elopement (OR: 3.59 [95% CI: 2.41–5.12]), leaving against medical advice (OR: 2.39 [95% CI: 1.46–3.67]), and being subjected to sedation (OR: 3.89 [95% CI: 3.19–4.70]) and restraint use (OR: 3.76 [95% CI: 3.06–4.57]). After adjusting for covariates, the association between incarceration and elopement remained significant (adjusted odds ratio: 1.65 [95% CI: 1.08–2.43]), while associations with other dispositions, restraint use, and sedation did not persist. Conclusion: This study identified differences in patient characteristics and care processes in the ED for patients with histories of incarceration and demonstrated the potential of using natural language processing in measuring care processes in populations that are largely hidden, but highly prev

关键词： artificial intelligence emergency medicine incarceration large language models quality of health care

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