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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Aaron McKenna Gil Alterovitz Bioinformatics Program Boston University Boston MA USA Harvard Partners Center for Genetics and Genomics Harvard Medical School Boston MA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA Children's Hospital Informatics Program Boston MA

The breadth of biological data collected in the last decade has far outstripped the methods available to process it. To effectively investigate and explore this abundance of data, novel automated collection and analysis approaches must be devised. We have developed a new open software framework, the Open Genomic Analysis Platform (OGAP), to aid in the analysis of genomic data. It is capable of analyzing a variety of data source, and focuses on using information theory to characterize data. The frameworks has is capable of import a variety of genome tied data, and provides custom analysis and visualization of results. We then demonstrate the use of this framework analyzing the Prochlorococcus Marinus organism. We show a strong correlation between the information content of sequence data and up regulation of gene expression during lytic infection.

关键词： genomics bioinformatics Data analysis Biological information theory Open source software Information analysis Information theory Data visualization Organisms Gene expression

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Identify Non-mutational p53 Functional Deficiency in Human Cancers

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genomics, Proteomics & bioinformatics 2024年第5期22卷 85-98页

作者： Qianpeng Li Yang Zhang Sicheng Luo Zhang Zhang Ann L.Oberg David E.Kozono Hua Lu Jann N.Sarkaria Lina Ma Liguo Wang National Genomics Data Center China National Center for BioinformationBeijing 100101China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Division of Computational Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Radiation Oncology Dana-Farber Cancer Institute and Brigham and Women's HospitalBostonMA 02215USA Department of Biochemistry&Molecular Biology and Tulane Cancer Center Tulane University School of MedicineNew OrleansLA 70112USA Department of Radiation Oncology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Bioinformatics and Computational Biology Graduate Program University of Minnesota RochesterRochesterMN 55904USA

An accurate assessment of p53's functional statuses is critical for cancer genomic ***,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA *** undetected cases are often misclassified as p53-normal,leading to inaccurate prognosis and downstream association *** address this issue,we built the support vector machine(SVM)models to systematically reassess p53's functional statuses in TP53 wild-type(TP53^(WT))tumors from multiple The Cancer Genome Atlas(TCGA)***-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve(AUROC)of 0.9822,precision of 0.9747,and recall of *** study revealed that a significant proportion(87%-99%)of TP53^(WT) tumors actually had compromised p53 *** analyses uncovered that these genetically intact but functionally impaired(termed as predictively reduced function of p53 or TP53^(WT)-pRF)tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors:heightened genomic instability and elevated levels of ***,patients with TP53^(WT)-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53(TP53^(WT)-pN)tumors,and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

关键词： Cancer Composite expression DNA mutation Machine learning p53 deficiency

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Distinct adipose progenitor cells emerging with age drive active adipogenesis

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Science (New York, N.Y.) 2025年第6745期388卷 eadj0430页

作者： Wang, Guan Li, Gaoyan Song, Anying Zhao, Yutian Yu, Jiayu Wang, Yifan Dai, Wenting Salas, Martha Qin, Hanjun Medrano, Leonard Dow, Joan Li, Aimin Armstrong, Brian Fueger, Patrick T. Yu, Hua Zhu, Yi Shao, Mengle Wu, Xiwei Jiang, Lei Campisi, Judith Yang, Xia Wang, Qiong A. Department of Molecular and Cellular Endocrinology Arthur Riggs Diabetes and Metabolism Research Institute City of Hope Medical Center Duarte CA USA Department of Integrative Biology and Physiology University of California Los Angeles CA United States Light Microscopy Core City of Hope Medical Center Duarte CA USA The Integrative Genomics Core City of Hope Medical Center Duarte CA USA Division of Developmental and Translational Diabetes and Endocrinology Research City of Hope Medical Center Duarte CA USA Comprehensive Metabolic Phenotyping Core City of Hope Medical Center Duarte CA USA Pathology Core of Shared Resources City of Hope Medical Center Duarte CA USA Comprehensive Cancer Center Beckman Research Institute City of Hope Medical Center Duarte CA USA Children's Nutrition Research Center Department of Pediatrics Baylor College of Medicine Houston TX United States Key Laboratory of Immune Response and Immunotherapy Shanghai Institute of Immunity and Infection Chinese Academy of Sciences Shanghai China Buck Institute for Research on Aging Novato CA United States Bioinformatics Interdepartmental Program University of California Los Angeles CA United States Institute for Quantitative and Computational Biosciences University of California Los Angeles CA United States Molecular and Medical Pharmacology University of California Los Angeles CA United States

Starting at middle age, adults often suffer from visceral adiposity and associated adverse metabolic disorders. Lineage tracing in mice revealed that adipose progenitor cells (APCs) in visceral fat undergo extensive adipogenesis during middle age. Thus, despite the low turnover rate of adipocytes in young adults, adipogenesis is unlocked during middle age. Transplantations quantitatively showed that APCs in middle-aged mice exhibited high adipogenic capacity cell-autonomously. Single-cell RNA sequencing identified a distinct APC population, the committed preadipocyte, age-enriched (CP-A), emerging at this age. CP-As demonstrated elevated proliferation and adipogenesis activity. Pharmacological and genetic manipulations indicated that leukemia inhibitory factor receptor signaling was indispensable for CP-A adipogenesis and visceral fat expansion. These findings uncover a fundamental mechanism of age-dependent adipose remodeling, offering critical insights into age-related metabolic diseases.

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Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex (vol 21, pg 120, 2018)

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NATURE NEUROSCIENCE 2018年第7期21卷 1017-1017页

作者： Hrvatin, Sinisa Hochbaum, Daniel R. Nagy, M. Aurel Cicconet, Marcelo Robertson, Keiramarie Cheadle, Lucas Zilionis, Rapolas Ratner, Alex Borges-Monroy, Rebeca Klein, Allon M. Sabatini, Bernardo L. Greenberg, Michael E. Department of Neurobiology Harvard Medical School Boston MA USA Society of Fellows Harvard University Cambridge MA USA Department of Neurobiology Howard Hughes Medical Institute Harvard Medical School Boston MA USA Image and Data Analysis Core Harvard Medical School Boston MA USA Department of Systems Biology Harvard Medical School Boston MA USA Vilnius University Institute of Biotechnology Vilnius Lithuania ICCB-L Single Cell Core Harvard Medical School Boston MA USA Program for Bioinformatics and Integrative Genomics Graduate School of Arts and Science Division of Medical Sciences Harvard University Cambridge MA USA

Activity-dependent transcriptional responses shape cortical function. However, a comprehensive understanding of the diversity of these responses across the full range of cortical cell types, and how these changes contribute to neuronal plasticity and disease, is lacking. To investigate the breadth of transcriptional changes that occur across cell types in the mouse visual cortex after exposure to light, we applied high-throughput single-cell RNA sequencing. We identified significant and divergent transcriptional responses to stimulation in each of the 30 cell types characterized, thus revealing 611 stimulus-responsive genes. Excitatory pyramidal neurons exhibited inter- and intralaminar heterogeneity in the induction of stimulus-responsive genes. Non-neuronal cells showed clear transcriptional responses that may regulate experience-dependent changes in neurovascular coupling and myelination. Together, these results reveal the dynamic landscape of the stimulus-dependent transcriptional changes occurring across cell types in the visual cortex; these changes are probably critical for cortical function and may be sites of deregulation in developmental brain disorders.

关键词： Gene expression analysis Molecular neuroscience Oligodendrocyte Transcriptomics Visual system

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Based on deep feature extraction non-invasive bladder cancer prediction system 24

Based on deep feature extraction non-invasive bladder cancer...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Lin, Shih-Huan Xu, Ya-Wen Chuang, Min-Ling Chu, Yen-Wei Ph.D. Program in Medical Biotechnology National Chung Hsing University Taichung Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Department of Pathology Feng Yuan Hospital Ministry of Health and Welfare Taichung Taiwan Biotechnology Center National Chung Hsing University Taichung Taiwan Agricultural Biotechnology Center National Chung Hsing University Taichung Taiwan Institute of Molecular Biology National Chung Hsing University Taichung Taiwan Taichung Taiwan

ISBN: (纸本)9798400716874

Bladder cancer (BCa) ranks among the top ten most common cancers worldwide, and its five-year survival rate largely depends on early diagnosis. Given that 90% of BCa patients are over 55 years old, with an average age of 73 years. Therefore, a non-invasive early detection method is crucial for treatment and improving patient prognosis. To meet this need, our study leveraged non-invasive urine routine analysis as analytical data, combined with deep feature extraction, to achieve early detection and precise prediction, thereby alleviating the burden on middle-aged and elderly patients. We conducted urine routine analysis on 783 patients at Fengyuan Hospital, yielding promising preliminary results with accuracy (Acc), area under the curve (AUC), specificity (Sp), and sensitivity (Sn) values of 0.725, 0.800, 0.730, and 0.721, respectively. Furthermore, independent testing on 196 patients at MacKay Memorial Hospital provided stable validation, with AUC, Sp, and Sn values of 0.766, 0.606, and 0.831, respectively. These findings offer valuable insights for cancer detection, and the provided model can assist doctors in promoting early treatment plans, thereby improving patient outcomes and potentially saving lives. © 2024 Copyright owner author(s)

关键词： Diseases

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Nfia-dependent mechanoreceptors are required for dynamic mechanical hypersensitivity after nerve injury

Nfia-dependent mechanoreceptors are required for dynamic mec...

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中国神经科学学会第十二届全国学术会议

作者： Lu QI Qiuyi GU Xiaohua WU Guangjuan YIN Lu HAN Mengsheng QIU Richard M.GRONOSTAJSKI Qiufu MA Yang LIU Zhejiang Key Laboratory of Organ Development and Regeneration Institute of Life Sciences Hangzhou Normal University College of Life Sciences Zhejiang University Department of Biochemistry Program in Genetics Genomics and BioinformaticsCenter of Excellence in Bioinformatics and Life Sciences State University of New York at Buffalo Dana-Farber Cancer Institute Harvard Medical School Department of Neurobiology Harvard Medical School

Mechanical allodynia represents an excruciating chronic pain resulted from lesions or inflammation to somatosensory system. Different forms of mechanical hypersensitivities, including brush-evoked dynamic and filament-evoked punctate(static) allodynia, recruit distinct circuits for signal transmission. A group of v Glut3-expressing excitatory spinal neurons is identified to preferentially transmit dynamic mechanical pain information after nerve injury and inflammation. However, what types of peripheral sensory and spinal inhibitory neurons specifically mediate dynamic allodynia in neuropathic or inflammatory conditions is still unknown. Here, we report that a group of Nuclear Factor I/A(Nfia)-dependent myelinated mechanoreceptors is particularly required for dynamic allodynia manifestation after nerve injury. Nfia conditional knockout(c KO) mice generated by using Nav1.8-Cre(Nfia;Nav1.8-Cre), in which only subsets of myelinated Aβ and Aδ neurons in the dorsal root ganglia(DRG) are affected, exhibit specific loss of dynamic, but not static allodynia in neuropathic pain model, while both forms of allodynia remain intact in CFA-induced inflammatory pain model. Reduced brush-evoked c-Fos signals in the dorsal spinal cord indicate the impairment of signal transduction in these mice. Furthermore, removal of GABA inhibition by intrathecal injection of Bicuculline induces severe mechanical hypersensitivities in wildtype mice, but fails in NfiaF/F;Nav1.8-Cre mice, implying that GABA spinal neurons constitute a "gate" controlling the transmission of mechanical allodynia information from these Nfia-dependent sensory neurons. In contrast, disinhibition of glycinergic spinal neurons by Strychnine evokes intact dynamic and static allodynia in the Nfia cKO mice, suggesting a more important role of glycinergic spinal neurons in gating inflammatory pain signals. These results distinguish different sensory and spinal neuronal components mediating dynamic allodynia in neuropathic and

关键词： dynamic allodynia Nuclear Factor I/A primary sensory neurons GABAA spinal inhibitory neurons

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation (vol 21, pg 432, 2018)

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NATURE NEUROSCIENCE 2018年第7期21卷 1015-1015页

作者： Preissl, Sebastian Fang, Rongxin Huang, Hui Zhao, Yuan Raviram, Ramya Gorkin, David U. Zhang, Yanxiao Sos, Brandon C. Afzal, Veena Dickel, Diane E. Kuan, Samantha Visel, Axel Pennacchio, Len A. Zhang, Kun Ren, Bing Ludwig Institute for Cancer Research La Jolla CA USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego School of Medicine La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA US Department of Energy Joint Genome Institute Walnut Creek CA USA School of Natural Sciences University of California Merced Merced CA USA Comparative Biochemistry Program University of California Berkeley Berkeley CA USA Institute of Genomic Medicine Moores Cancer Center University of California San Diego School of Medicine La Jolla CA USA

Analysis of chromatin accessibility can reveal transcriptional regulatory sequences, but heterogeneity of primary tissues poses a significant challenge in mapping the precise chromatin landscape in specific cell types. Here we report single-nucleus ATAC-seq, a combinatorial barcoding-assisted single-cell assay for transposase-accessible chromatin that is optimized for use on flash-frozen primary tissue samples. We apply this technique to the mouse forebrain through eight developmental stages. Through analysis of more than 15,000 nuclei, we identify 20 distinct cell populations corresponding to major neuronal and non-neuronal cell types. We further define cell-type-specific transcriptional regulatory sequences, infer potential master transcriptional regulators and delineate developmental changes in forebrain cellular composition. Our results provide insight into the molecular and cellular dynamics that underlie forebrain development in the mouse and establish technical and analytical frameworks that are broadly applicable to other heterogeneous tissues.

关键词： Cell type diversity Chromatin Chromatin analysis Epigenomics

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High-Resolution Imaging of Human Cancer Proteins Using Microprocessor Materials

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ChemBioChem 2022年第17期23卷 e202200310-e202200310页

作者： Solares, Maria J. Jonaid, G.M. Luqiu, William Y. Berry, Samantha Khadela, Janki Liang, Yanping Evans, Madison C. Pridham, Kevin J. Dearnaley, William J. Sheng, Zhi Kelly, Deborah F. Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Department of Biomedical Engineering Pennsylvania State University University Park 16802 PA United States Center for Structural Oncology Pennsylvania State University University Park 16802 PA United States Department of Electrical and Computer Engineering Duke University Durham 27708 NC United States Fralin Biomedical Research Institute at VTC Virginia Tech Roanoke 24016 VA United States

Mutations in tumor suppressor genes, such as Tumor Protein 53 (TP53), are heavily implicated in aggressive cancers giving rise to gain- and loss-of-function phenotypes. While individual domains of the p53 protein have been studied extensively, structural information for full-length p53 remains incomplete. Functionalized microprocessor chips (microchips) with properties amenable to electron microscopy permitted us to visualize complete p53 assemblies for the first time. The new structures revealed p53 in an inactive dimeric state independent of DNA binding. Residues located at the protein-protein interface corresponded with modification sites in cancer-related hot spots. Changes in these regions may amplify the toxic effects of clinical mutations. Taken together, these results contribute advances in technology and imaging approaches to decode native protein models in different states of activation. © 2022 Wiley-VCH GmbH.

关键词： cancer electron microscopy microchips p53, molecular modeling

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Clinical and Immunophenotypic Analysis of Donor-Derived T Cells Targeting Twelve Viral Antigens Administered to Patients after Hematopoietic Stem Cell Transplant

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Transplantation and Cellular Therapy 2025年第2期31卷 S513-S513页

作者： Kinoshita, Hannah, MD Chansky, Pamela Adhikari, Nisha Motta, Cecilia, PhD Jensen-Wachspress, Mariah, BA Hanley, Patrick J., PhD Tanna, Jay, MS Lang, Haili, MS Hoq, Fahmida, MD Saldaña, Blachy J Dávila, MD Perez-Albuerne, Evelio, MD PhD Jacobsohn, David A, MD, MBA Abraham, Allistair, MD Bollard, Catherine M., MD, FRACP, FRCPA Keller, Michael D., MD Center for Cancer and Blood Disorders Children's National Hospital Washington DC Department of Pediatrics The George Washington School of Medicine and Health Sciences Washington DC Genomics and Bioinformatics Program The George Washington University Washington DC Center for Cancer and Immunology Research Children's National Hospital Washington DC Children's National Hospital Washington DC Program for Cell Enhancement and Technologies for Immunotherapy Center for Cancer & Immunology Research Children's National Research Institute Washington DC Center for Cancer and Immunology Research Children's National Medical Center Washington DC Children's National Medical Center Washington DC Division of Bone Marrow Transplantation and Immune Deficiency Cincinnati Children's Hospital Cincinnati OH Blood and Marrow Transplantation Center for Cancer and Blood Disorders Children's National Hospital； George Washington University School of Medicine and Health Sciences Washington DC Bone Marrow Transplant Children's National Medical Center Washington DC Department of Pediatrics The George Washington University Washington DC

Hematopoietic-stem-cell transplantation (HSCT) remains the only curative option for many patients with relapsed or high-risk malignancies, immunodeficiency disorders and hemoglobinopathies. In mis-matched transplant donors, immunosuppression is required post-HSCT to prevent graft-versus-host disease (GVHD), increasing the risk of virus infections before immune reconstitution is achieved. Use of multi-virus-specific T cells (mVST) therapy following HSCT may help to restore antiviral immunity, including targeting of latent and respiratory viruses. Aims: To evaluate the safety and antiviral activity of a “first-in-human” mVST targeting 12 viral antigens of 6 viruses. Methods: Recipients of allogeneic-HSCT were enrolled on a phase-I dose-escalation (1 × 10 7/m 2–5 × 10 7/m 2 /dose) trial evaluating donor-derived mVSTs targeting cytomegalovirus, Epstein-Barr virus, adenovirus, human herpes virus 6, BK virus and human-parainfluenza-3 for the prevention or treatment of infection (Fig. 1). Clinical and immunobiological responses were measured and gene expression profiles and T cell receptor (TCR) clonotypes from the products were tracked in the patient over time. Results: Fourteen patients (median age 14.7, range 0.9-62 years) with primary and/or treatment-related immunodeficiency received mVSTs post-HSCT. Six patients had inborn errors of immunity, 4 patients had sickle cell anemia, 4 with malignancy and 1 had myelofibrosis. Four patients received mVSTs as prophylaxis, of whom 75% (n=3/4) remained virus-free post-mVSTs. One patient (n=1/4) had reactivation of EBV, requiring rituximab treatment. Of the 10 patients receiving mVSTs for treatment, 90% achieved a partial or complete response to at least one target virus by 90 days post-mVSTs. Unique TCR clonotypes from the product were tracked over time in the patient post-mVSTs, persisting up to 180 days. (Fig. 2) Expression of high-frequency clones by individual cell phenotype demonstrated predominance in CD8+ T cells. (Fig.

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