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SSRN 2023年

作者： Kudo, Genki Hirao, Takumi Harada, Ryuhei Hirokawa, Takatsugu Shigeta, Yasuteru Yoshino, Ryunosuke Physics Department Graduate School of Pure and Applied Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan Master’s Program in Medical Sciences Graduate School of Comprehensive Human Sciences University of Tsukuba Ibaraki Tsukuba305-8575 Japan Division of Biomedical Science Faculty of Medicine University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8575 Japan Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Transborder Medical Research Center University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8575 Japan Japan

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor that mimics S-adenosyl-l-methionine and deoxycytidine has been developed;however, its selectivity mechanism is unclear because the structure of the inhibitor–protein complex has not yet been determined. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Through our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning revealed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as seen by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our study findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures. © 2023, The Authors. All rights reserved.

关键词： Molecular dynamics

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Phenotype-guided subpopulation identification from single-cell sequencing data

Research Square

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Research Square 2021年

作者： Sun, Duanchen Guan, Xiangnan Moran, Amy E. Qian, David Z. Schedin, Pepper Dai, Mu-Shui Danilov, Alexey V. Alumkal, Joshi Adey, Andrew Spellman, Paul T. Xia, Zheng Computational Biology Program Oregon Health & Science University PortlandOR United States Department of Biomedical Engineering Oregon Health & Science University PortlandOR United States Department of Cell Developmental and Cancer Biology Oregon Health & Science University PortlandOR United States Knight Cancer Institute Oregon Health & Science University PortlandOR United States Department of Molecular and Medical Genetics Oregon Health & Science University PortlandOR United States City of Hope National Medical Center DuarteCA United States Department of Internal Medicine University of Michigan Ann ArborMI United States Department of Molecular Microbiology and Immunology Oregon Health & Science University PortlandOR United States

Single-cell sequencing yields novel discoveries by distinguishing cell types, states, and lineages within the context of heterogeneous tissues. However, interpreting complex single-cell data from highly heterogeneous cell populations remains challenging. Currently, most existing single-cell data analyses focus on cell type clusters defined by unsupervised clustering methods that cannot directly link cell clusters with specific biological and clinical phenotypes. Here we present Scissor, a novel approach that utilizes disease phenotypes to identify cell subpopulations from single-cell data that are most highly associated with the given phenotype. This "phenotype-to-cell" within a single-step strategy utilizes clinical information collected from bulk assays to identify the most highly phenotype-associated cell subpopulations. When applied to a lung cancer single-cell RNA-seq (scRNA-seq) dataset, Scissor identified a subset of cells associated with worse survival as well as another cell subpopulation associated with TP53 mutation. Furthermore, in a melanoma scRNA-seq dataset, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expressions, which is associated with a favorable immunotherapy response. Scissor also demonstrated its broad applicability and effectiveness in interpreting Facioscapulohumeral muscular dystrophy (FSHD) and Alzheimer’s disease scRNA-seq datasets. Thus, Scissor provides a novel framework to identify biologically and clinically relevant cell subpopulations from single-cell assays by leveraging the wealth of phenotype and bulk-omics datasets. © 2021, CC BY.

关键词： Data integration

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Early Hyperlipidemia Triggers Metabolomic Reprogramming with Increased Sah, Increased Acetyl-Coa-Cholesterol Synthesis, and Decreased Glycolysis

SSRN

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SSRN 2023年

作者： Xu, Keman Saaoud, Fatma Shao, Ying Lu, Yifan Wu, Sheng Zhao, Huaqing Chen, Kaifu Vazquez-Padron, Roberto Jiang, Xiaohua Wang, Hong Yang, Xiaofeng Centers of Cardiovascular Research Temple Unoiversity Lewis Katz School of Medicine PhiladelphiaPA19140 United States Metabolic Disease Research Thrombosis Research Departments of Cardiovascular Sciences Temple University Lewis Katz School of Medicine PhiladelphiaPA19140 United States Medical Education and Data Science Temple University Lewis Katz School of Medicine PhiladelphiaPA19140 United States Computational Biology Program Department of Cardiology Boston Children’s Hospital Harvard Medical School BostonMA02115 United States DeWitt Daughtry Family Department of Surgery Leonard M. Miller School of Medicine University of Miami MiamiFL33125 United States

To identify metabolomic reprogramming in early hyperlipidemia, unbiased metabolome was screened in four tissues from ApoE-/- mice fed with high fat diet (HFD) for 3 weeks. 30, 122, 67, and 97 metabolites in the aorta, heart, liver, and plasma, respectively, were upregulated. 9 upregulated metabolites were uremic toxins, and 13 metabolites, including palmitate, promoted a trained immunity with increased syntheses of acetyl-CoA and cholesterol, increased S-adenosylhomocysteine (SAH) and hypomethylation and decreased glycolysis. The cross-omics analysis found upregulation of 11 metabolite synthetases in ApoE ¯ / ¯ aorta, which promote ROS, cholesterol biosynthesis, and inflammation. Statistical correlation of 12 upregulated metabolites with 37 gene upregulations in ApoE ¯ / ¯ aorta indicated 9 upregulated new metabolites to be proatherogenic. Antioxidant transcription factor NRF2-/- transcriptome analysis indicated that NRF2 suppresses trained immunity-metabolomic reprogramming. Our results have provided novel insights on metabolomic reprogramming in multiple tissues in early hyperlipidemia oriented toward three co-existed new types of trained immunity. © 2023, The Authors. All rights reserved.

关键词： Metabolites

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Forecasting Vegetation Health in the MENA Region by Predicting Vegetation Indicators with Machine Learning Models

Forecasting Vegetation Health in the MENA Region by Predicti...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sachi Perera Wenzhao Li Erik Linstead Hesham El-Askary Computational and Data Sciences Graduate Program Schmid College of Science and Technology Chapman University Orange CA USA Schmid College of Science and Technology Chapman University Orange CA USA Fowler School of Engineering Chapman University Orange CA USA Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R 2 > 0.95) in the testing and (R 2 > 0.93) in the evaluation process.

关键词： Vegetation mapping MODIS Atmospheric modeling Remote sensing Predictive models Training Support vector machines

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Weakly Semi-supervised phenotyping using Electronic Health records

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Journal of Biomedical Informatics 2022年 134卷 104175页

作者： Nogues, Isabelle-Emmanuella Wen, Jun Lin, Yucong Liu, Molei Tedeschi, Sara K. Geva, Alon Cai, Tianxi Hong, Chuan Department of Biostatistics Harvard T. H. Chan School of Public Health BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Center for Statistical Science Tsinghua University Beijing China Department of Medicine Division of Rheumatology Inflammation and Immunity Brigham and Women's Hospital BostonMA United States Department of Anesthesiology Critical Care and Pain Medicine Computational Health Informatics Program Boston Children's Hospital BostonMA United States Department of Anesthesia Harvard Medical School BostonMA United States

Objective: Electronic Health Record (EHR) based phenotyping is a crucial yet challenging problem in the biomedical field. Though clinicians typically determine patient-level diagnoses via manual chart review, the sheer volume and heterogeneity of EHR data renders such tasks challenging, time-consuming, and prohibitively expensive, thus leading to a scarcity of clinical annotations in EHRs. Weakly supervised learning algorithms have been successfully applied to various EHR phenotyping problems, due to their ability to leverage information from large quantities of unlabeled samples to better inform predictions based on a far smaller number of patients. However, most weakly supervised methods are subject to the challenge to choose the right cutoff value to generate an optimal classifier. Furthermore, since they only utilize the most informative features (i.e., main ICD and NLP counts) they may fail for episodic phenotypes that cannot be consistently detected via ICD and NLP data. In this paper, we propose a label-efficient, weakly semi-supervised deep learning algorithm for EHR phenotyping (WSS-DL), which overcomes the limitations above. Materials and Methods: WSS-DL classifies patient-level disease status through a series of learning stages: 1) generating silver standard labels, 2) deriving enhanced-silver-standard labels by fitting a weakly supervised deep learning model to data with silver standard labels as outcomes and high dimensional EHR features as input, and 3) obtaining the final prediction score and classifier by fitting a supervised learning model to data with a minimal number of gold standard labels as the outcome, and the enhanced-silver-standard labels and a minimal set of most informative EHR features as input. To assess the generalizability of WSS-DL across different phenotypes and medical institutions, we apply WSS-DL to classify a total of 17 diseases, including both acute and chronic conditions, using EHR data from three healthcare systems. Addition

关键词： Learning algorithms

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Pre-clinical safety and efficacy of human induced pluripotent stem cell-derived products for autologous cell therapy in Parkinson's disease

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Cell Stem Cell 2025年第3期32卷 343-360.e7页

作者： Jeon, Jeha Cha, Young Hong, Yean Ju Lee, In-Hee Jang, Heejin Ko, Sanghyeok Naumenko, Serhiy Kim, Minseon Ryu, Hannah L. Shrestha, Zenith Lee, Nayeon Park, Tae-Yoon Park, HoeWon Kim, Seo-Hyun Yoon, Ki-Jun Song, Bin Schweitzer, Jeffrey Herrington, Todd M. Kong, Sek Won Carter, Bob Leblanc, Pierre Kim, Kwang-Soo Molecular Neurobiology Laboratory McLean Hospital and Department of Psychiatry Harvard Medical School Belmont 02478 MA United States Program in Neuroscience Harvard Medical School Belmont 02478 MA United States Department of Pediatrics Harvard Medical School Boston 02115 MA United States Computational Health Informatics Program Boston Children's Hospital Harvard Medical School Boston 02115 MA United States Department of Biostatistics Harvard Chan School of Public Health Boston 02215 MA United States Department of Biological Sciences Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Department of Neurosurgery Massachusetts General Hospital Harvard Medical School Boston 02114 MA United States Department of Neurology Massachusetts General Hospital Harvard Medical School Boston 02114 MA United States Harvard Stem Cell Institute Harvard Medical School Cambridge 02138 MA United States

Human induced pluripotent stem cell (hiPSC)-derived midbrain dopaminergic cells (mDACs) represent a promising source for autologous cell therapy in Parkinson's disease (PD), but standardized regulatory criteria are essential for clinical translation. In this pre-clinical study, we generated multiple clinical-grade hiPSC lines from freshly biopsied fibroblasts of four sporadic PD patients using episomal reprogramming and differentiated them into mDACs using a refined 21-day protocol. Rigorous evaluations included whole-genome/exome sequencing, RNA sequencing, and in vivo studies, including a 39-week Good Laboratory Practice-compliant mouse safety study. While mDACs from all lines met safety criteria, mDACs from one patient failed to improve rodent behavioral outcomes, underscoring inter-individual variability. Importantly, in vitro assessments did not reliably predict in vivo efficacy, identifying dopaminergic fiber density as a key efficacy criterion. These findings support comprehensive quality control guidelines for autologous cell therapy and pave the way for a clinical trial with eight sporadic PD patients, scheduled to commence in 2025. © 2025 Elsevier Inc.

关键词： autologous cell therapy dopaminergic fiber density genome integrity human induced pluripotent stem cells inter-individual variability midbrain dopaminergic cells Parkinson's disease phase 1 clinical trial pre-clinical study safety and efficacy

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Cross-Species Functional Genomic Screens Identify Novel Therapeutic Targets in Malignant Peripheral Nerve Sheath Tumors

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The American Journal of Pathology 2025年

作者： Brittany Turner-Ivey Amanda M. Prechtl Stephen T. Guest Shannon W. Doutt Jody F. Longo Elizabeth Garrett-Mayer Steven L. Carroll Departments of Pathology and Laboratory Medicine Medical University of South Carolina Charleston SC 29425-9080 Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor Michigan 48409-2218 Medical Scientist Training Program Medical University of South Carolina Charleston SC 29425-9080 Public Health Science Medical University of South Carolina Charleston SC 29425-9080

As oncogenic pathways are highly conserved in vertebrates, genetically engineered mouse models can potentially be used to identify therapeutic targets relevant to rare human cancers such as malignant peripheral nerve sheath tumors (MPNSTs). To test this, genome-scale shRNA screens designed to identify genes driving proliferation and survival were performed in five MPNST cultures derived from P 0 -GGFβ3 mice and three human MPNST cell lines. Several hundred gene hits mediating proliferation and survival were identified in human and mouse MPNST cells, many of which have been implicated in proliferation and survival in other cancers and/or mediate the pathogenesis of other cancer types. These hits and their associated signaling pathways extensively overlapped in human and mouse MPNST cells. A drug discovery pathway based on the Drug-Gene Interaction Database was developed to identify hits encoding druggable targets. Five druggable targets were selected for validation, with four of the five agents tested proving effective against human MPNST cells (the POLA1 inhibitor clofarabine, DNTT inhibitor cordycepin, BCL6 inhibitor 79-6 and LPAR1/3 inhibitor Ki16425). Clofarabine was especially effective, potently reducing cell numbers at low nanomolar concentrations and inducing a senescent phenotype, possibly via the p53/p21 pathway. These results demonstrate the utility of cross-species functional oncogenomics for the discovery of novel therapeutic targets relevant to human MPNSTs and suggest that clofarabine warrants further evaluation for its therapeutic potential.

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Local number fluctuations in hyperuniform and nonhyperuniform systems: Higher-order moments and distribution functions

arXiv

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arXiv 2020年

作者： Torquato, Salvatore Kim, Jaeuk Klatt, Michael A. Department of Physics Princeton University PrincetonNJ08544 United States Department of Chemistry Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

The local number variance σ2(R) associated with a spherical sampling window of radius R enables a classification of many-particle systems in d-dimensional Euclidean space Rd according to the degree to which large-scale density fluctuations are suppressed, resulting in a demarcation between hyperuniform and nonhyperuniform phyla. To more completely characterize density fluctuations, we carry out an extensive study of higher-order moments or cumulants, including the skewness γ1(R), excess kurtosis γ2(R) and the corresponding probability distribution function P[N(R)] of a large family of models across the first three space dimensions, including both hyperuniform and nonhyperuniform systems with varying degrees of short- and long-range order. To carry out this comprehensive program, we derive new theoretical results that apply to general point processes and conduct high-precision numerical studies. Specifically, we derive explicit closed-form integral expressions for γ1(R) and γ2(R) that encode structural information up to three-body and four-body correlation functions, respectively. We also derive rigorous bounds on γ1(R), γ2(R) and P[N(R)] for general point processes and corresponding exact results for general packings of identical spheres. High-quality simulation data for γ1(R), γ2(R) and P[N(R)] are generated for each model. We also ascertain the proximity of P[N(R)] to the normal distribution via a novel Gaussian "distance" metric l2(R). Among all models, the convergence to a central limit theorem (CLT) is generally fastest for the disordered hyperuniform processes such that γ1(R) ∼ l2(R) ∼ R-(d+1)/2 and γ2(R) ∼ R-(d+1) for large R. The convergence to a CLT is slower for standard nonhyperuniform models and slowest for the "antihyperuniform" model studied here. We prove that one-dimensional hyperuniform systems of class I or any d-dimensional lattice cannot obey a CLT. Remarkably, we discovered that the gamma distribution provides a good approximation to P[N(R)] for

关键词： Higher order statistics

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Augment Reality-Based Teaching Practice

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Biomedical engineering education 2021年第1期1卷 237-241页

作者： You Wu Mingzhen Zhang Xiaosi Li Yu Gan Chao Zhao Department of Chemical and Biological Engineering The University of Alabama P. O. Box870203 Tuscaloosa AL 35401 USA. Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research Frederick MD 21702 USA. Department of Electrical and Computer Engineering The University of Alabama P. O. Box870286 Tuscaloosa AL 35401 USA.

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METTL4 is an snRNA m6 Am methyltransferase that regulates RNA splicing

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细胞研究（英文版） 2020年第6期30卷 544-547页

作者： Hao Chen Lei Gu Esteban A.Orellana Yuanyuan Wang Jiaojiao Guo Qi Liu Longfei Wang Zhangfei Shen Hao Wu Richard I.Gregory Yi Xing Yang Shi Division of Newborn Medicine and Program in Epigenetics Boston Children's Hospital Harvard Medical School 300 Longwood AvenueBoston MA 02115 USA Stem Cell Program Division of Hematology/Oncology Boston Children's Hospital Department of Biological Chemistry and Molecular Pharmacology Harvard Medical SchoolBoston MA 02115 USA Bioinformatics Interdepartmental Graduate Program University of California Los Angeles Los Angeles CA 90095USA College of Science Northeastern University 360 Huntington Ave. Boston MA 02115 USA Program in Cellular and Molecular Medicine Boston Children's Hospital Boston MA 02115 USA Department of Biological Chemistry and Molecular Pharmacology Harvard Medical School Boston MA 02115 USA Division of Newborn Medicine and Program in Epigenetics Boston Children's Hospital Harvard Medical School 300 Longwood AvenueBoston MA 02115 USA Program in Cellular and Molecular Medicine Boston Children's Hospital Boston MA 02115 USA Department of Biological Chemistry and Molecular Pharmacology Harvard Medical School Boston MA 02115 USA Center for Computational and Genomic Medicine The Children's Hospital of Philadelphia Philadelphia PA 19104 USA Department of Pathology and Laboratory Medicine University of PennsylvaniaPhiladelphia PA 19104 USA

Dear Editor, Protein coding mRNAs and non-coding RNAs are extensively modified by chemical modifications,1 including methylation of the N6 position of adenosine (m6A).2 While mRNA internal m6A methylation is mediated by METTL3 and METTL14, which are members of the MT-A70 family,2 N6-methylation of cap-adjacent N6,2'-O-dimethyladenosine (m6Am) is catalyzed by PCIF1.3-6 Non-coding RNAs are also m6Am methylated internally,7 but the corresponding enzyme remains *** we provide evidence that METTL4 mediates internal m6Am methylation of U2 small nuclear RNA (snRNA).

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