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368 Uncovering links between innate immunity, DNA repair, and cognitive health in aging populations

Journal of Clinical and Translational Science 2025年第s1期9卷 113-114页

作者： Jamie Leiriao Hannah Lords Paola Sebastiani Tufts University Candidate Bioinformatics and Computational Biology Program Boston University Clinical and Translational Science Institute Tufts University

Objectives/Goals: Neurodegenerative diseases involve progressive neuronal loss or dysfunction, often due to accumulated damage and impaired repair mechanisms. Our research evaluates the role of innate immune recognition proteins to provide insights into age-related neurodegeneration and cognitive decline. Methods/Study Population: We will utilize transcriptomic data from the Long-Life Family Study (LLFS), a cohort rich in genetic and phenotypic data related to aging and longevity. Our approach includes assessing a set of innate immune recognition proteins, also known as pattern recognition receptors (PRRs) expression across various age groups, focusing on potential correlations with cognitive performance. By analyzing serum transcriptomic profiles, we aim to map changes in expression and DNA repair genes over time, evaluating their connection to cognitive health and neurodegeneration in aging populations. Results/Anticipated Results: We anticipate that the expression of some PRRs will increase with age and correlate with cognitive decline, suggesting a role in age-related neurodegeneration. We also expect a decrease in DNA repair pathway gene expression in older age groups, contrasting with an increase in genes involved in endogenous DNA detection. These results will reveal how PRRs may function as neuroprotective factors and how their expression changes may relate to the decline in DNA repair processes with age, providing a better understanding of innate immune recognition in cognitive health. Discussion/Significance of Impact: This study will reveal the role of PRRs in aging and neurodegeneration, potentially establishing them as a key player in neuronal protection. Findings may guide future research into therapeutic strategies targeting them for Alzheimer’s and other age-related neurodegenerative diseases.

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Catalysis-Induced Highly-Stable Interface on Porous Silicon for High-Rate Lithium-Ion Batteries

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Nano-Micro Letters 2025年第8期17卷 548-563页

作者： Zhuobin Han Phornphimon Maitarad Nuttapon Yodsin Baogang Zhao Haoyu Ma Kexin Liu Yongfeng Hu Siriporn Jungsuttiwong Yumei Wang Li Lu Liyi Shi Shuai Yuan Yongyao Xia Yingying Lv Research Centre of Nanoscience and Nanotechnology Shanghai UniversityShanghai 200444People’s Republic of China Department of Chemistry Faculty of ScienceSilpakorn UniversityNakhon Pathom 73000Thailand Sinopec Shanghai Research Institute of Petrochemical Technology Co. Ltd.Shanghai 201208People’s Republic of China Department of Chemistry and Center of Excellence for Innovation in Chemistry Faculty of Science Ubon Ratchathani UniversityUbon Ratchathani 34190Thailand National University of Singapore(Chongqing)Research Institute Chongqing 401123People’s Republic of China Department of Chemistry Fudan UniversityShanghai 200433People’s Republic of China Emerging Industries Institute Shanghai University Jiaxing 314006ZhejiangPeople’s Republic of China Program in Bioinformatics and Computational Biology Graduate SchoolChulalongkorn UniversityBangkok 10330Thailand

Silicon stands as a key anode material in lithium-ion battery ascribing to its high energy ***,the poor rate performance and limited cycling life remain unresolved through conventional approaches that involve carbon composites or nanostructures,primarily due to the un-controllable effects arising from the substantial formation of a solid electrolyte interphase(SEI)during the ***,an ultra-thin and homogeneous Ti doping alumina oxide catalytic interface is meticulously applied on the porous Si through a synergistic etching and hydrolysis *** defect-rich oxide interface promotes a selective adsorption of fluoroethylene carbonate,leading to a catalytic reaction that can be aptly described as“molecular concentration-in situ conversion”.The resultant inorganic-rich SEI layer is electrochemical stable and favors ion-transport,particularly at high-rate cycling and high *** robustly shielded porous Si,with a large surface area,achieves a high initial Coulombic efficiency of 84.7%and delivers exceptional high-rate performance at 25 A g^(−1)(692 mAh g^(−1))and a high Coulombic efficiency of 99.7%over 1000 *** robust SEI constructed through a precious catalytic layer promises significant advantages for the fast development of silicon-based anode in fast-charging batteries.

关键词： Catalytic interface Mesoporous Inorganic-rich SEI Silicon anode Lithium-ion batteries

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Rationally designed antimicrobial peptides with high selectivity and efficiency against phytopathogenic Ralstonia solanecearum

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Science of the Total Environment 2025年 976卷 179354页

作者： Manik, Melvalia Cristin Darai, Nitchakan Rungrotmongkol, Thanyada Duan, Lian Harada, Ryuhei Shigeta, Yasuteru Hengphasatporn, Kowit Vangnai, Alisa S. Biotechnology Program Faculty of Science Chulalongkorn University Bangkok10330 Thailand Futuristic Science Research Center School of Science Walailak University Nakhon Si Thammarat80160 Thailand Drug and Cosmetics Excellence Center Walailak University Thasala Nakhon Si Thammarat80160 Thailand Program in Bioinformatics and Computational Biology Chulalongkorn University Bangkok10330 Thailand Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Center of Excellence in Biocatalyst and Sustainable Biotechnology Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand

Ralstonia solanacearum, the causative agent of bacterial wilt, poses a global threat to agriculture, necessitating urgent and sustainable solutions as traditional methods lose efficacy. This study developed WRF-13, a synthetic antimicrobial peptide (AMP) designed to mimic natural AMPs, exhibiting potent antibacterial and anti-biofilm activity with high specificity against R. solanacearum. Mechanistic studies, including microscopy and computational analyses, demonstrated that WRF-13 disrupts the bacterial membrane. WRF-13 remained stable across a wide pH (5–8) and temperature (25–50 °C) range, essential for field applications, and showed no detectable toxicity to mammalian or plant cells at elevated concentrations. Greenhouse trials confirmed its efficacy in reducing bacterial wilt severity up to 65 %, highlighting its potential to protect crops from R. solanacearum infection. Overall, this study highlights WRF-13 as a targeted solution for managing bacterial wilt and advancing sustainable agriculture. © 2025 Elsevier B.V.

关键词： Antimicrobial peptides Bacterial wilt Crop protection In silico-peptide design Ralstonia solanacearum

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BPS2025 - Characterizing oligomeric amyloid-β42 and POPC interactions through molecular dynamics: A computational approach for understanding Alzheimer’s disease

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Biophysical Journal 2025年第3期124卷 70a-70a页

作者： Emma M. Cleveland Kelsie M. King Anne M. Brown Systems Biology Virginia Tech Blacksburg VA USA Genetics Bioinformatics and Computational Biology program Virginia Tech Christiansburg VA USA Department of Biochemistry Virginia Tech University Blacksburg VA USA

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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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Engineering Mice to Study Human Immunity

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Annual Review of Immunology 2025年第1期43卷 451-487页

作者： Sefik, Esen Xiao, Tianli Chiorazzi, Michael Odell, Ian Zhang, Fengrui Agrawal, Kriti Micevic, Goran Flavell, Richard A. Department of Immunobiology Yale School of Medicine New Haven CT United States Howard Hughes Medical Institute Yale School of Medicine New Haven CT United States Department of Internal Medicine Section of Medical Oncology Yale School of Medicine New Haven CT United States Department of Dermatology Yale School of Medicine New Haven CT United States Computational Biology and Bioinformatics Program Yale University New Haven CT United States Department of Pathology Yale School of Medicine New Haven CT United States

Humanized mice, which carry a human hematopoietic and immune system, have greatly advanced our understanding of human immune responses and immunological diseases. These mice are created via the transplantation of human hematopoietic stem and progenitor cells into immunocompromised murine hosts further engineered to support human hematopoiesis and immune cell growth. This article explores genetic modifications in mice that enhance xeno-tolerance, promote human hematopoiesis and immunity, and enable xenotransplantation of human tissues with resident immune cells. We also discuss genetic editing of the human immune system, provide examples of how humanized mice with humanized organs model diseases for mechanistic studies, and highlight the roles of these models in advancing knowledge of organ biology, immune responses to pathogens, and preclinical drugs tested for cancer treatment. The integration of multi-omics and state-of-the art approaches with humanized mouse models is crucial for bridging existing human data with causality and promises to significantly advance mechanistic studies. Copyright © 2025 by the author(s).

关键词： cancer human hematopoietic stem and progenitor cells human immunity human organ modeling humanized mice immunological diseases infectious diseases xenotransplantation

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BPS2025 - Biophysical insights into allosteric modulation and inhibition of viral proteases using MD simulations

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Biophysical Journal 2025年第3期124卷 61a-61a页

作者： Marion Q. LoPresti Kelsie M. King Anne M. Brown Virginia Tech Blacksburg VA USA Genetics Bioinformatics and Computational Biology Program Virginia Tech Christiansburg VA USA Department of Biochemistry Virginia Tech Blacksburg VA USA

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BPS2025 - Probing the influence of sequence on cytotoxic and functional amyloid oligomer conformations

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Biophysical Journal 2025年第3期124卷 73a-73a页

作者： Kelsie M. King Hajar Zaheer Anne M. Brown Genetics Bioinformatics and Computational Biology Program Virginia Tech Christiansburg VA USA University of North Carolina Chapel Hill Chapel Hill NC USA Department of Biochemistry Virginia Tech University Blacksburg VA USA

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TACO： Taxonomic prediction of unknown OTUs through OTU co-abundance networks

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Frontiers of Electrical and Electronic Engineering in China 2016年第3期4卷 149-158页

作者： Zohreh Baharvand Irannia Ting Chen Program in Computational Biology and Bioinformatics Department of Biological Sciences University of Southern California Los Angeles CA 90089 USA Bioinformatics Division TNLIST Tsinghua University Beijing 100084 China

Background： A main goal of metagenomics is taxonomic characterization of microbial communities. Although sequence comparison has been the main method for the taxonomic classification, there is not a clear agreement on similarity calculation and similarity thresholds, especially at higher taxonomic levels such as phylum and class. Thus taxonomic classification of novel metagenomic sequences without close homologs in the biological databases poses a challenge. Methods： In this study, we propose to use the co-abundant associations between taxa/operational taxonomic units （OTU） across complex and diverse communities to assist taxonomic classification. We developed a Markov Random Field model to predict taxa of unknown microorganisms using co-abundant associations. Results： Although such associations are intrinsically functional associations, we demonstrate that they are strongly correlated with taxonomic associations and can be combined with sequence comparison methods to predict taxonomic origins of unknown microorganisms at phylum and class levels. Conclusions： With the ever-increasing accumulation of sequence data from microbial communities, we now take the first step to explore these associations for taxonomic identification beyond sequence similarity. Availability and Implementation： Source codes of TACO are freely available at the following URL： https：//***/ baharvand/OTU-Taxonomy-Identification implemented in C＋＋, supported on Linux and MS Windows.

关键词： metagenomics 16s rRNA gene taxonomic profiling taxonomic prediction Markov Random Field OTUco-abundance network

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HiPoNet: A Topology-Preserving Multi-View Neural Network For High Dimensional Point Cloud and Single-Cell Data

arXiv

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arXiv 2025年

作者： Viswanath, Siddharth Madhu, Hiren Bhaskar, Dhananjay Kovalic, Jake Johnson, Dave Ying, Rex Tape, Christopher Adelstein, Ian Perlmutter, Michael Krishnaswamy, Smita Department of Computer Science Yale University New Haven United States Department of Genetics Yale University New Haven United States Department of Applied Physics Yale University New Haven United States Program in Computing Boise State University Idaho United States Cell Communication Lab Department of Oncology University College London Cancer Institute London United Kingdom Department of Mathematics Yale University New Haven United States Department of Mathematics Boise State University Idaho United States Computational Biology and Bioinformatics Program Yale University New Haven United States Wu-Tsai Institute Yale University New Haven United States Program for Applied Math Yale University New Haven United States

In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionality exceeding the capabilities of existing methods for point clouds which are tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e. one on every patient), necessitating models that can process large, high-dimensional point clouds at scale. Most current approaches build a single nearest-neighbor graph, discarding important geometric information. In contrast, HiPoNet forms higher-order simplicial complexes through learnable feature reweighting, generating multiple data views that disentangle distinct biological processes. It then employs simplicial wavelet transforms to extract multiscale features—capturing both local and global topology. We empirically show that these components preserve topological information in the learned representations, and that HiPoNet significantly outperforms state-of-the-art point-cloud and graph-based models on single cell. We also show an application of HiPoNet on spatial transcriptomics datasets using spatial co-ordinates as one of the views. Overall, HiPoNet offers a robust and scalable solution for high-dimensional data analysis. © 2025, CC BY.

关键词： Network topology

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