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Custom CRISPR-Cas9 PAM variants via scalable engineering and machine learning

Nature 2025年 2025 Apr 22页

作者： Rachel A Silverstein Nahye Kim Ann-Sophie Kroell Russell T Walton Justin Delano Rossano M Butcher Martin Pacesa Blaire K Smith Kathleen A Christie Leillani L Ha Ronald J Meis Aaron B Clark Aviv D Spinner Cicera R Lazzarotto Yichao Li Azusa Matsubara Elizabeth O Urbina Gary A Dahl Bruno E Correia Debora S Marks Shengdar Q Tsai Luca Pinello Suk See De Ravin Qin Liu Benjamin P Kleinstiver PhD Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Department of Pathology Massachusetts General Hospital Boston MA USA. Department of Pathology Harvard Medical School Boston MA USA. Institute of Pharmacy and Molecular Biotechnology (IPMB) Faculty of Engineering Sciences Heidelberg University Heidelberg Germany. Department of Biomedical Informatics Harvard Medical School Boston MA USA. Molecular Pathology Unit Massachusetts General Hospital Charlestown MA USA. Krantz Family Center for Cancer Research Massachusetts General Hospital Charlestown Charlestown MA USA. Broad Institute of Harvard and MIT Cambridge MA USA. Ocular Genomics Institute Massachusetts Eye and Ear Boston MA USA. Department of Ophthalmology Harvard Medical School Boston MA USA. Laboratory of Protein Design and Immunoengineering École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics Lausanne Switzerland. CELLSCRIPTTM Madison WI USA. Department of Systems Biology Harvard Medical School Boston MA USA. Department of Hematology St. Jude Children's Research Hospital Memphis TN USA. Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. bkleinstiver@mgh.harvard.edu. Department of Pathology Massachusetts General Hospital Boston MA USA. bkleinstiver@mgh.harvard.edu. Department of Pathology Harvard Medical School Boston MA USA. bkleinstiver@mgh.harvard.edu.

Engineering and characterizing proteins can be time-consuming and cumbersome, motivating the development of generalist CRISPR-Cas enzymes to enable diverse genome editing applications. However, such enzymes have caveats such as an increased risk of off-target editing. To enable scalable reprogramming of Cas9 enzymes, here we combined high-throughput protein engineering with machine learning (ML) to derive bespoke editors more uniquely suited to specific targets. Via structure/function-informed saturation mutagenesis and bacterial selections, we obtained nearly 1,000 engineered SpCas9 enzymes and characterized their protospacer-adjacent motif (PAM) requirements to train a neural network that relates amino acid sequence to PAM specificity. By utilizing the resulting PAM ML algorithm (PAMmla) to predict the PAMs of 64 million SpCas9 enzymes, we identified efficacious and specific enzymes that outperform evolution-based and engineered SpCas9 enzymes as nucleases and base editors in human cells while reducing off-targets. An in silico directed evolution method enables user-directed Cas9 enzyme design, including for allele-selective targeting of the RHO P23H allele in human cells and mice. Together, PAMmla integrates ML and protein engineering to curate a catalog of SpCas9 enzymes with distinct PAM requirements, and motivates the use of efficient and safe bespoke Cas9 enzymes instead of generalist enzymes for various applications.

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SpliceAPP: an interactive web server to predict splicing errors arising from human mutations

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BMC genomics 2024年第1期25卷 600页

作者： Ang-Chu Huang Jia-Ying Su Yu-Jen Hung Hung-Lun Chiang Yi-Ting Chen Yen-Tsung Huang Chen-Hsin Albert Yu Hsin-Nan Lin Chien-Ling Lin Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan. Institute of Statistical Science Academia Sinica Taipei Taiwan. Bioinformatics Program International Graduate Program Academia Sinica Taipei Taiwan. Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan. Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. arith@gate.sinica.edu.tw. Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. mbcllin@gate.sinica.edu.tw. Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan. mbcllin@gate.sinica.edu.tw. Bioinformatics Program International Graduate Program Academia Sinica Taipei Taiwan. mbcllin@gate.sinica.edu.tw.

BACKGROUND:Splicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights. RESULTS:Our statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest. CONCLUSIONS:In summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://***/SpliceAPP . Source code can be downloaded at https://***/hsinnan75/SpliceAPP .

关键词： Human mutations LASSO regression RNA splicing Splicing variant prediction

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Random-effects substitution models for phylogenetics via scalable gradient approximations

arXiv

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arXiv 2023年

作者： Magee, Andrew F. Holbrook, Andrew J. Pekar, Jonathan E. Caviedes-Solis, Itzue W. Matsen, Fredrick A. Baele, Guy Wertheim, Joel O. Ji, Xiang Lemey, Philippe Suchard, Marc A. Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Biomedical Informatics University of California San Diega La Jolla CA United States Department of Biology Swarthmore College SwarthmorePA United States Howard Hughes Medical Institute SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium Department of Medicine University of California San Diego La Jolla CA United States Department of Mathematics Tulane University New OrleansLA United States Department of Biomathematics David Geffen School of Medicine at UCLA University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Universtiy of California Los Angeles Los AngelesCA United States

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

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Bayesian group factor analysis with structured sparsity

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Shiwen Zhao Chuan Gao Sayan Mukherjee Barbara E. Engelhardt Google MPI for Intelligent Systems Computational Biology and Bioinformatics Program Department of Statistical Science Duke University Durham NC Department of Statistical Science Duke University Durham NC Departments of Statistical Science Computer Science Mathematics Duke University Durham NC Department of Computer Science Center for Statistics and Machine Learning Princeton University Princeton NJ

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for a matrix of p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a Bayesian model of canonical correlation analysis. Here, we carefully define a structured Bayesian prior that encourages both element-wise and column-wise shrinkage and leads to desirable behavior on high-dimensional data. In particular, our model puts a structured prior on the joint factor loading matrix, regularizing at three levels, which enables element-wise sparsity and unsupervised recovery of latent factors corresponding to structured variance across arbitrary subsets of the observations. In addition, our structured prior allows for both dense and sparse latent factors so that covariation among either all features or only a subset of features can be recovered. We use fast parameter-expanded expectation-maximization for parameter estimation in this model. We validate our method on simulated data with substantial structure. We show results of our method applied to three high-dimensional data sets, comparing results against a number of state-of-the-art approaches. These results illustrate useful properties of our model, including i) recovering sparse signal in the presence of dense effects; ii) the ability to scale naturally to large numbers of observations; iii) flexible observation- and factor-specific regularization to recover factors with a wide variety of sparsity levels and percentage of variance explained; and iv) tractable inference that scales to modern genomic and text data sizes.

关键词： bayesian structured sparsity canonical correlation analysis mixture models parameter expansion sparse and low-rank matrix decomposition sparse priors

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The metabolome of induced pluripotent stem cells reveals metabolic changes occurring in somatic cell reprogramming

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细胞研究（英文版） 2012年第1期22卷 168-177页

作者： Athanasia D Panopoulos Oscar Yanes SergioRuiz Yasuyuki S Kida Dinh Diep Ralf Tautenhahn Aida Herrerias Erika M Batchelder Nongluk Plongthongkum Margaret Lutz W Travis Berggren Kun Zhang Ronald M Evans Gary Siuzdak Juan Carlos Izpisua Belmonte Gene Expression Laboratory The Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA Stem Cell Core The Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA Department of Bioengineering Institute for Genomic Medicine and Institute of Engineering in Medicine University of California at San Diego La Jolla CA 92093 USA Gene Expression Laboratory The Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA Howard Hughes Medical Institute The Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA Departments of Chemistry and Molecular Biology and Scripps Center for Metabolomics and Mass Spectrometry The Scripps Research Institute La Jolla CA 92037 USA Gene Expression Laboratory The Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA Center of Regenerative Medicine in Barcelona Dr Aiguader 88 08003 Barcelona Spain Departments of Chemistry and Molecular Biology and Scripps Center for Metabolomics and Mass Spectrometry The Scripps Research Institute La Jolla CA 92037 USA Metabolomics Plaiform of the Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders(CIBERDEM) University Rovira i Virgili 43007 Tarragona Spain Bioinformatics and Systems Biology Graduate Program University of California at San Diego La Jolla CA 92093 USA Center of Regenerative Medicine in Barcelona Dr Aiguader 88 08003 Barcelona Spain

Metabolism is vital to every aspect of cell function, yet the metabolome of induced pluripotent stem cells (iPSCs) remains largely unexplored. Here we report, using an untargeted metabolomics approach, that human iPSCs share a pluripotent metabolomic signature with embryonic stem cells (ESCs) that is distinct from their parental cells, and that is characterized by changes in metabolites involved in cellular respiration. Examination of cellular bioenergetics corroborated with our metabolomic analysis, and demonstrated that somatic cells convert from an oxidative state to a glycolytic state in pluripotency. Interestingly, the bioenergetics of various somatic cells correlated with their reprogramming efficiencies. We further identified metabolites that differ between iPSCs and ESCs, which revealed novel metabolic pathways that play a critical role in regulating somatic cell reprogramming. Our findings are the first to globally analyze the metabolome of iPSCs, and provide mechanistic insight into a new layer of regulation involved in inducing pluripotency, and in evaluating iPSC and ESC equivalence.

关键词： reprogramming iPS cells metabolome stem cells metabolism

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Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex

SSRN

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SSRN 2024年

作者： Johansen, Nelson J. Kempynck, Niklas Zemke, Nathan R. Somasundaram, Saroja Winter, Seppe De Hooper, Marcus Dwivedi, Deepanjali Lohia, Ruchi Wehbe, Fabien Li, Bocheng Abaffyová, Darina Armand, Ethan J. Man, Julie De Eksi, Eren Can Hecker, Nikolai Hulselmans, Gert Konstantakos, Vasilis Mauduit, David Mich, John K. Partel, Gabriele Daigle, Tanya L. Levi, Boaz P. Zhang, Kai Tanaka, Yoshiaki Gillis, Jesse Ting, Jonathan T. Ben-Simon, Yoav Miller, Jeremy Ecker, Joseph R. Ren, Bing Aerts, Stein Lein, Ed S. Tasic, Bosiljka Bakken, Trygve E. Allen Institute for Brain Science SeattleWA98109 United States VIB Center for AI & Computational Biology VIB-KU Leuven Center for Brain and Disease Research KU Leuven Department of Human Genetics Leuven Belgium Center for Epigenomics Department of Cellular and Molecular Medicine University of California La Jolla San DiegoCA92093 United States Physiology Department Donnelly Centre for Cellular and Biomolecular Research University of Toronto TorontoON Canada Maisonneuve-Rosemont Hospital Research Centre University of Montreal MontrealQC Canada School of Life Sciences Westlake University Zhejiang Hangzhou China Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA92093 United States Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Physiology and Biophysics University of Washington SeattleWA98195 United States Salk Institute for Biological Studies La Jolla CA92037 United States

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the "Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi- Omics" to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for the identification of functional enhancers for mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian brain and help us design more efficient genetic tools and potential gene therapies for human neurological diseases. © 2024, The Authors. All rights reserved.

关键词： Mammals

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QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data

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Current Protocols in bioinformatics 2020年第1期70卷 e100页

作者： Estaki, Mehrbod Jiang, Lingjing Bokulich, Nicholas A. McDonald, Daniel González, Antonio Kosciolek, Tomasz Martino, Cameron Zhu, Qiyun Birmingham, Amanda Vázquez-Baeza, Yoshiki Dillon, Matthew R. Bolyen, Evan Caporaso, J. Gregory Knight, Rob Department of Pediatrics University of California San Diego La Jolla CA United States Division of Biostatistics University of California San Diego La Jolla CA United States Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University Flagstaff AZ United States Department of Biological Sciences Northern Arizona University Flagstaff AZ United States Małopolska Centre of Biotechnology Jagiellonian University Kraków Poland Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA United States Center for Microbiome Innovation University of California San Diego La Jolla CA United States Center for Computational Biology and Bioinformatics University of California San Diego La Jolla CA United States Jacobs School of Engineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States

QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open-source web-based platform, to re-use available data for meta-analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta-analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://***. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://***. © 2020 The Authors. Basic Protocol: Using QIIME 2 with microbiome data. Support Protocol: Further microbiome analyses. © 2020 The Authors.

关键词： bioinformatics metagenomics microbiome QIIME 2 Qiita

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Nitrogen assimilation in cassava: implications for carbon metabolism and biomass synthesis

引用

Journal of Physics: Conference Series 2019年第1期1232卷

作者： W Siriwat V Muhardina C Thammarongtham S Kalapanulak T Saithong Systems Biology and Bioinformatics Research Laboratory Pilot Plant Development and Training Institute King Mongkut's University of Technology Thonburi Bang Khun Thian Bangkok 10150 Thailand Department of Agricultural Industrial Technology Faculty of Agricultural Technology University of SerambiMekkah Jl. TeungkuImumLueng Bata Batoh Banda Aceh Aceh 23245 Indonesia Biochemical Engineering and Pilot Plant Research and Development Unit National Center for Genetic Engineering and Biotechnology at King Mongkut's University of Technology Thonburi Bang KhunThian Bangkok 10150 Thailand Bioinformatics and Systems Biology Program School of Bioresources and Technology King Mongkut's University of Technology Thonburi Bang KhunThian Bangkok 10150 Thailand

The nitrogen assimilation pathway in cassava was reconstructed by comparative genomics approach to understand the underlying metabolism as well as the interaction between carbon and nitrogen assimilation towards the synthesis of metabolic phenotype. First, theproteins of cassava were annotated via sequence similarity search against genes of 11 template plants obtained from KEGG and PMN databases, employing reciprocal BLASTp(E-value ≤ 1x10−10, identity percentage ≥ 60, and coverage percentage ≥ 80). The template plants comprised well-known plant, starchy crops, nitrogen-fixing crops and crops that are evolutionarily related to cassava and includedArabidopsis thaliana, Oryzasativa, Zea mays, Ricinuscommunis, Solanumtuberosum, Brassica rapa, Cicerarietinum, Jatrophacurcas, Medicagotruncatula, Phaseolus vulgaris and Glycine *** pathway was then curatedwith reactions obtained from the CassavaCyc database to ensure full pathway *** was subsequently validated with cloned sequence of cassava from the GenBank and cassava transcriptome data from literature. The resulting N-assimilation pathway, covering the conversion of nitrate to amino acids (glutamine and glutamate),consists of 14 biochemical reactions corresponding to 59 genes, 73 proteins and 2 transport reactions. At least 92 percent of the identified proteins in the pathway were supported by the transcriptome data. In addition, the proposed N-assimilation pathway contains four additional enzymes, including glutamate synthase, nitrilase, formamidase and carbamoyl phosphate synthasecompared to the existing N-assimilation pathway in CassavaCyc database. Taken together, the N-assimilation pathway herein proposed identified reactions involved in N-assimilation and represents a forward step towards understanding metabolic basis for cassava yield as well as its phenotypic plasticity and adaptation to stress.

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An atlas of dynamic chromatin landscapes in mouse fetal development (vol 583, pg 744, 2020)

引用

NATURE 2021年第7842期589卷 E4-E4页

作者： Gorkin, David U. Barozzi, Iros Zhao, Yuan Zhang, Yanxiao Huang, Hui Lee, Ah Young Li, Bin Chiou, Joshua Wildberg, Andre Ding, Bo Zhang, Bo Wang, Mengchi Strattan, J. Seth Davidson, Jean M. Qiu, Yunjiang Afzal, Veena Akiyama, Jennifer A. Plajzer-Frick, Ingrid Novak, Catherine S. Kato, Momoe Garvin, Tyler H. Pham, Quan T. Harrington, Anne N. Mannion, Brandon J. Lee, Elizabeth A. Fukuda-Yuzawa, Yoko He, Yupeng Preissl, Sebastian Chee, Sora Han, Jee Yun Williams, Brian A. Trout, Diane Amrhein, Henry Yang, Hongbo Cherry, J. Michael Wang, Wei Gaulton, Kyle Ecker, Joseph R. Shen, Yin Dickel, Diane E. Visel, Axel Pennacchio, Len A. Ren, Bing Ludwig Institute for Cancer Research La Jolla CA USA Center for Epigenomics 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 School of Medicine La Jolla CA USA Department of Cellular and Molecular Medicine University of California San Diego School of Medicine La Jolla CA USA Institute of Genomic Medicine University of California San Diego School of Medicine La Jolla CA USA Moores Cancer Center University of California San Diego School of Medicine La Jolla CA USA Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA Department of Surgery and Cancer Imperial College London London UK US Department of Energy Joint Genome Institute Berkeley CA USA School of Natural Sciences University of California Merced Merced CA USA Comparative Biochemistry Program University of California Berkeley Berkeley CA USA Genomic Analysis Laboratory Salk Institute for Biological Studies La Jolla CA USA Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA Department of Biochemistry and Molecular Biology Penn State School of Medicine Hershey PA USA Stanford University School of Medicine Department of Genetics Stanford CA USA Howard Hughes Medical Institute Salk Institute for Biological Studies La Jolla CA USA Division of Biology and Biological Engineering California Institute of Technology Pasadena CA USA Institute for Human Genetics and University of California San Francisco San Francisco CA USA Department of Neurology University of California San Francisco San Francisco CA USA

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Mixed effects of HDACi on host gene expression and their implications for HIV reactivation from latency

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Journal of Virus Eradication 2015年 1卷 4-4页

作者： N. Beliakova-Bethell A. Mukim S. Deshmukh C.H. White C.H. Woelk C.A. Spina Department of Medicine University of California San Diego La Jolla CA USA VA San Diego Healthcare System San Diego CA USA Graduate Program in Bioinformatics and Systems Biology University of California San Diego La Jolla CA USA Faculty of Medicine University of Southampton UK Department of Pathology University of California San Diego La Jolla CA USA

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