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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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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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Functional annotation of chemical libraries across diverse biological processes (vol 13, pg 982, 2017)

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NATURE CHEMICAL BIOLOGY 2017年第12期13卷 1286-1286页

作者： Piotrowski, Jeff S. Li, Sheena C. Deshpande, Raamesh Simpkins, Scott W. Nelson, Justin Yashiroda, Yoko Barber, Jacqueline M. Safizadeh, Hamid Wilson, Erin Okada, Hiroki Gebre, Abraham A. Kubo, Karen Torres, Nikko P. LeBlanc, Marissa A. Andrusiak, Kerry Okamoto, Reika Yoshimura, Mami DeRango-Adem, Eva van Leeuwen, Jolanda Shirahige, Katsuhiko Baryshnikova, Anastasia Brown, Grant W. Hirano, Hiroyuki Costanzo, Michael Andrews, Brenda Ohya, Yoshikazu Osada, Hiroyuki Yoshida, Minoru Myers, Chad L. Boone, Charles RIKEN Center for Sustainable Resource Science Wako Saitama Japan Department of Computer Science and Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Bioinformatics and Computational Biology Program University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Electrical and Computer Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Integrated Biosciences Graduate School of Frontier Sciences University of Tokyo Kashiwa Chiba Japan Donnelly Centre University of Toronto Toronto Ontario Canada Institute of Molecular and Cellular Biosciences Center for Epigenetic Disease University of Tokyo Bunkyo-ku Tokyo Japan Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton New Jersey USA Department of Biochemistry University of Toronto Toronto Ontario Canada.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

关键词： Chemical genetics High-throughput screening Networks and systems biology Target identification

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ZNF280BY and ZNF280AY: Autosome derived Y-chromosome gene families in Bovidae

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BMC genomics 2011年第1期12卷 1-15页

作者： Yang, Yang Chang, Ti-Cheng Yasue, Hiroshi Bharti, Arvind K. Retzel, Ernest F. Liu, Wan-Sheng Department of Dairy and Animal Science The Center for Reproductive Biology and Health (CRBH) College of Agricultural Sciences The Pennsylvania State University University Park PA 16802 United States The Integrative Biosciences Program Bioinformatics and Genomics Option The Huck Institute of Life Sciences The Pennsylvania State University University Park PA 16802 United States National Institute of Agrobiological Sciences Tsukuba Ibaraki 305-0901 Japan National Center for Genome Resources Santa Fe NM 87505 United States Department of Pathology and Laboratory Medicine School of Medicine University of North Carolina-Chapel Hill Chapel Hill NC 27599 United States

Background: Recent progress in exploring the Y-chromosome gene content in humans, mice and cats have suggested that "autosome-to-Y" transposition of the male fertility genes is a recurrent theme during the mammalian Y-chromosome evolution. These transpositions are lineage-dependent. The purpose of this study is to investigate the lineage-specific Y-chromosome genes in ***: We took a direct testis cDNA selection strategy and discovered two novel gene families, ZNF280BY and ZNF280AY, on the bovine (Bos taurus) Y-chromosome (BTAY), which originated from the transposition of a gene block on the bovine chromosome 17 (BTA17) and subsequently amplified. Approximately 130 active ZNF280BY loci (and ~240 pseudogenes) and ~130 pseudogenized ZNF280AY copies are present over the majority of the male-specific region (MSY). Phylogenetic analysis indicated that both gene families fit with the "birth-and-death" model of evolution. The active ZNF280BY loci share high sequence similarity and comprise three major genomic structures, resulted from insertions/deletions (indels). Assembly of a 1.2 Mb BTAY sequence in the MSY ampliconic region demonstrated that ZNF280BY and ZNF280AY, together with HSFY and TSPY families, constitute the major elements within the repeat units. The ZNF280BY gene family was found to express in different developmental stages of testis with sense RNA detected in all cell types of the seminiferous tubules while the antisense RNA detected only in the spermatids. Deep sequencing of the selected cDNAs revealed that different loci of ZNF280BY were differentially expressed up to 60-fold. Interestingly, different copies of the ZNF280AY pseudogenes were also found to differentially express up to 10-fold. However, expression level of the ZNF280AY pseudogenes was almost 6-fold lower than that of the ZNF280BY genes. ZNF280BY and ZNF280AY gene families are present in bovid, but absent in other mammalian ***: ZNF280BY and ZNF280AY are lineage-speci

关键词： Autosomal Gene Bovine Testis Comparative Mapping Approach Male Fertility Gene Bovine Bacterial Artificial Chromosome

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Regulatory Mechanisms Controlling Mosquito Reproduction

Regulatory Mechanisms Controlling Mosquito Reproduction

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The 5th International Symposium on Insect Physiology, Biochemistry and Molecular Biology

作者： Alexander S.Raikhel Tusar Saha Sourav Roy Lisa Johnson Keira Lucas Shiping Liu Bo Zhao Vlastimil Smykal Xiufeng Zhang Sang Woon Shin Jisu Ha Wei Dou Vladimir Kokoza Zhen Zou Department of Entomology and Institute for Integrative Genome Biology University of California Riverside Graduate Program in Cell Molecular and Developmental BiologyUniversity of California Riverside Graduate Program in Genomics Genetics and BioinformaticsUniversity of California Riverside The State Key Laboratory of Silkworm Genome Biology Southwest University College of Plant Protection Southwest University State Key Laboratory of Integrated Management of Pest Insects and Rodents Institute of ZoologyChinese Academy of Sciences

Mosquitoes transmit numerous devastating diseases to humans and animals due to obligatory blood feeding that is required for egg development. Elucidating molecular mechanisms underlying mosquito reproduction is of paramount importance for devising novel control approaches. In addition, mosquitoes represent outstanding models for investigating these events in insects in general. Two major insect hormones-juvenile hormone(JH) and20-hydroxyecdysone(20E)-regulate a plethora of physiological processes. In female mosquitoes,these hormones are responsible for controlling sequential reproductive phases. Transcriptomic analyses combined with RNA interference depletions of JH receptor Methoprene-tolerant(Met)and Ecdysone receptor(EcR) have revealed functions of these hormone receptors in activation and repression of gene cohorts during previtellogenic and vitellogenic phases, respectively. We identified how JH receptor mediates JH action on gene expression in mosquitoes and devised an approach of studying the subunit composition of this receptor. Using this technique we screened numerous natural compounds and isolated several molecules that serve as strong disruptors of JH receptor and JH action, providing a basis for developing a novel class of insecticides. We extensively investigated the role of Ecdysone Receptor in regulation of gene expression,uncovering the complexity of the 20 E genetic hierarchy that controls vitellogenic phase of female mosquito reproduction. Understanding of the molecular mechanisms of Ecdysone receptor-dependent gene repression is of great importance for devising future control methodologies. Small non-coding RNAs belonging to the class of micro RNAs(miR NA) have been implicated in regulation of numerous cellular and physiological processes in animals and plants. However, relatively little is known about mi RNA roles in non-Drosophilid insects. We have developed systemic and transgenic approaches for analyzing functions of miR NAs in mosquito repro

关键词： juvenile hormone methoprene-tolerant 20-hydroxyecdysone ecdysone receptor vitellogenesis microRNA

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Graph Representation Learning in Biomedicine

arXiv

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arXiv 2021年

作者： Li, Michelle M. Huang, Kexin Zitnik, Marinka Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Health Data Science Program Harvard T.H. Chan School of Public Health BostonMA02115 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States

Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge. Advances in artificial intelligence, specifically deep learning, have enabled us to model, analyze, and learn with such networked data. In this review, we put forward an observation that long-standing principles of systems biology and medicine-while often unspoken in machine learning research-provide the conceptual grounding for representation learning on graphs, explain its current successes and limitations, and even inform future advancements. We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces. We also capture the breadth of ways in which representation learning has dramatically improved the state-of-the-art in biomedical machine learning. Exemplary domains covered include identifying variants underlying complex traits, disentangling behaviors of single cells and their effects on health, assisting in diagnosis and treatment of patients, and developing safe and effective medicines. © 2021, CC BY.

关键词： Vector spaces

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Protein-protein binding affinity and conformation changes: a structure/affinity benchmark

Protein-protein binding affinity and conformation changes: a...

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the 3rd Asia Pacific Protein Association Conference in ConJunction with The 3rd Symposiun of the Chinese Protein Society(第三届亚太地区蛋白质科学大会暨第三届全国跨学科蛋白质研究学术讨论会)

作者： Jo(e)l Janin Panagiotis L.Kastritis Iain H.Moal Howook Hwang Zhiping Weng Paul A.Bates Alexandre M.J.J.Bonvin Yeast Structural Genomics IBBMC Bat.430 Université Paris-Sud 91405-Orsay France Bijvoet Center for Biomolecular Research Utrecht University 3584CH Utrecht NL Biomolecular Modelling Laboratory Cancer Research UK London Research Institute London WC2A 3LY UK program in Bioinformatics and Integrative Biology University of Massachusetts Medical School Worcester MA 01605

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Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis (vol 56, pg 627, 2024)

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NATURE GENETICS 2024年第6期56卷 1319-1319页

作者： Mitra, Sneha Malik, Rohan Wong, Wilfred Rahman, Afsana Hartemink, Alexander J. Pritykin, Yuri Dey, Kushal K. Leslie, Christina S. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA Rye Country Day School Rye NY USA Tri-Institutional Training Program in Computational Biology and Medicine New York City NY USA Hunter College City University of New York New York City NY USA Department of Computer Science Duke University Durham NC USA Program in Computational Biology and Bioinformatics Duke University Durham NC USA Center for Genomic and Computational Biology Duke University Durham NC USA Department of Computer Science Princeton University Princeton NJ USA Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton NJ USA

We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC–seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene–peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.

关键词： Epigenetics Population genetics Software

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Multimodal learning with graphs

arXiv

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arXiv 2022年

作者： Ektefaie, Yasha Dasoulas, George Noori, Ayush Farhat, Maha Zitnik, Marinka Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Department of Biomedical Informatics Harvard University BostonMA02115 United States Harvard College CambridgeMA02138 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States Division of Pulmonary and Critical Care Department of Medicine Massachusetts General Hospital BostonMA United States

Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases—the set of assumptions that algorithms use to make predictions for inputs they have not encountered during training. Learning on multimodal datasets presents fundamental challenges because the inductive biases can vary by data modality and graphs might not be explicitly given in the input. To address these challenges, multimodal graph AI methods combine different modalities while leveraging cross-modal dependencies using graphs. Diverse datasets are combined using graphs and fed into sophisticated multimodal architectures, specified as image-intensive, knowledge-grounded and language-intensive models. Using this categorization, we introduce a blueprint for multimodal graph learning, use it to study existing methods and provide guidelines to design new models. © 2022, CC BY.

关键词： Blueprints

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Graph AI in Medicine

arXiv

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arXiv 2023年

作者： Johnson, Ruth Li, Michelle M. Noori, Ayush Queen, Owen Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Berkowitz Family Living Laboratory Harvard Medical School BostonMA02115 United States Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Harvard College CambridgeMA02138 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters or minimal to no re-training. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions. © 2023, CC BY.

关键词： Knowledge graph

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