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STAT5 and STAT3 balance shapes dendritic cell function and tumour immunity

Nature 2025年 1-10页

作者： Zhou, Jiajia Tison, Kole Zhou, Haibin Bai, Longchuan Acharyya, Ranjan Kumar McEachern, Donna Metwally, Hoda Wang, Yu Pitter, Michael Choi, Jae Eun Vatan, Linda Liao, Peng Yu, Jiali Lin, Heng Jiang, Long Wei, Shuang Gao, Xue Grove, Sara Parolia, Abhijit Cieslik, Marcin Kryczek, Ilona Green, Michael D. Lin, Jian-Xin Chinnaiyan, Arul M. Leonard, Warren J. Wang, Shaomeng Zou, Weiping Department of Surgery University of Michigan Medical School Ann Arbor MI United States Center of Excellence for Cancer Immunology and Immunotherapy University of Michigan Rogel Cancer Center Ann Arbor MI United States Graduate Program in Immunology University of Michigan Ann Arbor MI United States Department of Computational Medicine and Bioinformatics University of Michigan Medical School Ann Arbor MI United States Division of Hematology and Oncology Department of Internal Medicine University of Michigan Medical School Ann Arbor MI United States Department of Pathology University of Michigan Medical School Ann Arbor MI United States Michigan Center for Translational Pathology University of Michigan Medical School Ann Arbor MI United States Department of Radiation Oncology University of Michigan Medical School Ann Arbor MI United States Laboratory of Molecular Immunology and the Immunology Center National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States Howard Hughes Medical Institute University of Michigan Medical School Ann Arbor MI United States University of Michigan Rogel Cancer Center University of Michigan Ann Arbor MI United States Graduate Program in Cancer Biology University of Michigan Ann Arbor MI United States Department of Pharmacology University of Michigan Medical School Ann Arbor MI United States Department of Medicinal Chemistry College of Pharmacy University of Michigan Ann Arbor MI United States

Immune checkpoint blockade (ICB) has transformed cancer therapy1,2. The efficacy of immunotherapy depends on dendritic cell-mediated tumour antigen presentation, T cell priming and activation3,4. However, the relation...

Immune checkpoint blockade (ICB) has transformed cancer therapy^1,2. The efficacy of immunotherapy depends on dendritic cell-mediated tumour antigen presentation, T cell priming and activation^3,4. However, the relationship between the key transcription factors in dendritic cells and ICB efficacy remains unknown. Here we found that ICB reprograms the interplay between the STAT3 and STAT5 transcriptional pathways in dendritic cells, thereby activating T cell immunity and enabling ICB efficacy. Mechanistically, STAT3 restrained the JAK2 and STAT5 transcriptional pathway, determining the fate of dendritic cell function. As STAT3 is often activated in the tumour microenvironment⁵, we developed two distinct PROTAC (proteolysis-targeting chimera) degraders of STAT3, SD-36 and SD-2301. STAT3 degraders effectively degraded STAT3 in dendritic cells and reprogrammed the dendritic cell–transcriptional network towards immunogenicity. Furthermore, STAT3 degrader monotherapy was efficacious in treatment of advanced tumours and ICB-resistant tumours without toxicity in mice. Thus, the crosstalk between STAT3 and STAT5 transcriptional pathways determines the dendritic cell phenotype in the tumour microenvironment and STAT3 degraders hold promise for cancer immunotherapy. © The Author(s) 2025.

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13th Pacific Symposium on Biocomputing, PSB 2008

作者： Lee, Jae-Hyung Hamilton, Michael Gleeson, Colin Caragea, Cornelia Zaback, Peter Sander, Jeffry D. Li, Xue Wu, Feihong Terribilini, Michael Honavar, Vasant Dobbs, Drena Bioinformatics and Computational Biology Program L.H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50010 United States Dept. of Genetics Development and Cell Biology Discovery Iowa State University Ames IA 50010 United States Dept. of Computer Science Iowa State University Ames IA 50010 United States Artificial Intelligence Research Lab Center for Computational Intelligence Learning and Discovery Iowa State University Ames IA 50010 United States Dept. of Computer Science Colorado State University Fort Collins CO 80523 United States Dept. of Biological Sciences Univ. of Illinois Chicago IL 60607 United States

ISBN: (纸本)9812776087

Telomerase is a ribonucleoprotein enzyme that adds telomeric DNA repeat sequences to the ends of linear chromosomes. The enzyme plays pivotal roles in cellular senescence and aging, and because it provides a telomere maintenance mechanism for ∼90% of human cancers, it is a promising target for cancer therapy. Despite its importance, a high-resolution structure of the telomerase enzyme has been elusive, although a crystal structure of an N-terminal domain (TEN) of the telomerase reverse transcriptase subunit (TERT) from Tetrahymena has been reported. In this study, we used a comparative strategy, in which sequence-based machine learning approaches were integrated with computational structural modeling, to explore the potential conservation of structural and functional features of TERT in phylogenetically diverse species. We generated structural models of the N-terminal domains from human and yeast TERT using a combination of threading and homology modeling with the Tetrahymena TEN structure as a template. Comparative analysis of predicted and experimentally verified DNA and RNA binding residues, in the context of these structures, revealed significant similarities in nucleic acid binding surfaces of Tetrahymena and human TEN domains. In addition, the combined evidence from machine learning and structural modeling identified several specific amino acids that are likely to play a role in binding DNA or RNA, but for which no experimental evidence is currently available. © 2008 World Scientific Publishing Co. Pte. Ltd.

关键词： Enzymes

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Comparison of experimental and computed protein anisotropic temperature factors

Comparison of experimental and computed protein anisotropic ...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Lei Yang Guang Song Robert L. Jernigan Program of Bioinformatics and Computational Biology Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA Department of Computer Science Iowa State University Ames IA USA Department of Biochemistry Biophysics and Molecular Biology L. H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA USA

Because of its appealing simplicity, the anisotropic network model (ANM) has been widely accepted and applied to study many molecular motion problems: such as the molecular mechanisms of GroEL-GroES function, allosteric changes in hemoglobin, ribosome motion, motor-protein motions, and conformational changes in general, etc. However, the validity of the ANM has not been closely examined. In this work, we use ANM to predict the anisotropic temperature factors of proteins. On the flip side, the rich, directional anisotropic temperature factor data available for hundreds of proteins in the protein data bank (PDB) are used as validation data to closely test the ANM model. The significance of this work is that it presents a timely, important evaluation of the model, shows the extent of its accuracy in reproducing experimental anisotropic temperature factors, and suggests ways to improve the model. An improved model will help us better understand the internal dynamics of proteins, which in turn can greatly expand the usefulness of the models, which has already been demonstrated in many applications.

关键词： Proteins Anisotropic magnetoresistance Temperature Fluctuations computational biology biology computing Computer networks bioinformatics Biochemistry Biophysics

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Bayesian multinomial logistic normal models through marginally latent matrix-T processes

arXiv

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arXiv 2019年

作者： Silverman, Justin D. Roche, Kimberly Holmes, Zachary C. David, Lawrence A. Mukherjee, Sayan Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Medical Scientist Training Program Duke University DurhamNC27708 United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University DurhamNC27708 United States Center for Genomic and Computational Biology Duke University DurhamNC27708 United States Department of Molecular Genetics and Microbiology Duke University DurhamNC27708 United States

Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to handling small data sets due to the non-conjugacy of the multinomial and logistic-normal distributions. We introduce MLN models which can be written as marginally latent matrix-t process (LTP) models. Marginally LTP models describe a flexible class of generalized linear regression, non-linear regression, and time series models. We develop inference schemes for Marginally LTP models and, through application to MLN models, demonstrate that our inference schemes are both highly accurate and often 4-5 orders of magnitude faster than MCMC. Copyright © 2019, The Authors. All rights reserved.

关键词： Multivariant analysis

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Multiomics Reveals Induction of Neuroblastoma SK-N-BE(2)C Cell Death by Mitochondrial Division Inhibitor 1 through Multiple Effects

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Journal of Proteome Research 2024年第1期23卷 301-315页

作者： Wang, Wei-Hsuan Kao, Yi-Chun Hsieh, Chiao-Hui Tsai, Shin-Yu Cheung, Chantal Hoi Yin Huang, Hsuan-Cheng Juan, Hsueh-Fen Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei 106 Taiwan Department of Life Science National Taiwan University Taipei 106 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei 106 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 112 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 106 Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei 106 Taiwan

Mitochondrial division inhibitor 1 (Mdivi-1) is a well-known synthetic compound aimed at inhibiting dynamin-related protein 1 (Drp1) to suppress mitochondrial fission, making it a valuable tool for studying mitochondrial dynamics. However, its specific effects beyond Drp1 inhibition remain to be confirmed. In this study, we employed integrative proteomics and phosphoproteomics to delve into the molecular responses induced by Mdivi-1 in SK-N-BE(2)C cells. A total of 3070 proteins and 1945 phosphorylation sites were identified, with 880 of them represented as phosphoproteins. Among these, 266 proteins and 97 phosphorylation sites were found to be sensitive to the Mdivi-1 treatment. Functional enrichment analysis unveiled their involvement in serine biosynthesis and extrinsic apoptotic signaling pathways. Through targeted metabolomics, we observed that Mdivi-1 enhanced intracellular serine biosynthesis while reducing the production of C24:1-ceramide. Within these regulated phosphoproteins, dynamic dephosphorylation of proteasome subunit alpha type 3 serine 250 (PSMA3-S250) occurred after Mdivi-1 treatment. Further site-directed mutagenesis experiments revealed that the dephosphorylation-deficient mutant PSMA3-S250A exhibited a decreased cell survival. This research confirms that Mdivi-1’s inhibition of mitochondrial division leads to various side effects, ultimately influencing cell survival, rather than solely targeting Drp1 inhibition. © 2023 American Chemical Society.

关键词： Mdivi-1 multiomics neuroblastoma phosphoproteome proteome targeted metabolome

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Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM

Donor Selection for Hematopoietic Stem Cell Transplant Using...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Adarsh Sivasankaran Vladimir Cherkassky Mark Albrecht Eric Williams Martin Maiers Biomedical Informatics and Computational Biology Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota U.S.A. Bioinformatics Research National Marrow Donor Program Minneapolis Minnesota U.S.A.

ISBN: (纸本)9781509002887

Donor selection for Hematopoietic Stem Cell Transplant often requires physicians to manually select 3 to 5 donors from a list of 100s of genetically compatible donors as identified by HLA-based matching algorithms. The decision process is complicated by a lack of strict guidelines governing a "secondary" selection process, which is based upon non-HLA donor attributes. Our research is aimed at modeling this "secondary" decision process which can help physicians choose the right donors, based on donor attributes and historical choice behavior. Proposed black box models will help in improving selection consistency.

关键词： Medical services Support vector machines Data models Stem cells Mathematical model Numerical models Training

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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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Autofix for backward-fit sidechains: Using MolProbity and real-space refinement to put misfits in their place

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Journal of Structural and Functional Genomics 2009年第1期10卷 83-93页

作者： Headd, Jeffrey J. Immormino, Robert M. Keedy, Daniel A. Emsley, Paul Richardson, David C. Richardson, Jane S. Department of Biochemistry Duke University Medical Center 211 Nanaline Duke Building Durham NC 27710 United States Computational Biology and Bioinformatics Program Duke University Durham NC 27708 United States Department of Immunology University of Washington School of Medicine Seattle WA 98195 United States Laboratory of Molecular Biophysics Department of Biochemistry University of Oxford Oxford Oxfordshire 0X1 3QU United Kingdom

Misfit sidechains in protein crystal structures are a stumbling block in using those structures to direct further scientific inference. Problems due to surface disorder and poor electron density are very difficult to address, but a large class of systematic errors are quite common even in well-ordered regions, resulting in sidechains fit backwards into local density in predictable ways. The MolProbity web site is effective at diagnosing such errors, and can perform reliable automated correction of a few special cases such as 180° flips of Asn or Gln sidechain amides, using all-atom contacts and H-bond networks. However, most at-risk residues involve tetrahedral geometry, and their valid correction requires rigorous evaluation of sidechain movement and sometimes backbone shift. The current work extends the benefits of robust automated correction to more sidechain types. The Autofix method identifies candidate systematic, flipped-over errors in Leu, Thr, Val, and Arg using MolProbity quality statistics, proposes a corrected position using real-space refinement with rotamer selection in Coot, and accepts or rejects the correction based on improvement in MolProbity criteria and on χ angle change. Criteria are chosen conservatively, after examining many individual results, to ensure valid correction. To test this method, Autofix was run and analyzed for 945 representative PDB files and on the 50S ribosomal subunit of file 1YHQ. Over 40% of Leu, Val, and Thr outliers and 15% of Arg outliers were successfully corrected, resulting in a total of 3,679 corrected sidechains, or 4 per structure on average. Summary Sentences: A common class of misfit sidechains in protein crystal structures is due to systematic errors that place the sidechain backwards into the local electron density. A fully automated method called "Autofix" identifies such errors for Leu, Val, Thr, and Arg and corrects over one third of them, using MolProbity validation criteria and Coot real-space refinement

关键词： Automation Crystallography Protein/RNA interactions Sidechain rotamers Structure improvement

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Incorporation of protein binding effects into likelihood ratio test for exome sequencing data

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BMC proceedings 2016年第SUPPL 7期10卷 275-281页

作者： Dongni Zhang Hongzhu Cui Dmitry Korkin Zheyang Wu Mathematics Department Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609-2280 USA. Computer Science Department Bioinformatics and Computational Biology Program Worcester Polytechnic Institute 100 Institute Road Worcester MA 01609-2280 USA.

Statistical association studies are an important tool in detecting novel disease genes. However, for sequencing data, association studies confront the challenge of low power because of relatively small data sample size and rare variants. Incorporating biological information that reflects disease mechanism is likely to strengthen the association evidence of disease genes, and thus increase the power of association studies. In this paper, we annotate non-synonymous single-nucleotide variants according to protein binding sites (BSs) by using a more accurate BS prediction method. We then incorporate this information into association study through a statistical framework of likelihood ratio test (LRT) based on weighted burden score of single-nucleotide variants (SNVs). The strategy is applied to Genetic Analysis Workshop 19 exome-sequencing data for detecting novel genes associated to hypotension. The SNV-weighting LRT idea is empirically verified by the simulated phenotypes (336 cases and 1607 controls), and the weights based on BS annotation are applied to the real phenotypes (394 cases and 1457 controls). Such strategy of weighting the prior information on protein functional sites is shown to be superior to the unweighted LRT and serves as a good complement to the existing association tests. Several putative genes are reported; some of them are functionally related to hypertension according to the previous evidence in the literature.

关键词： Likelihood Ratio Test Real Data Analysis Burden Score Data Sample Size Genomic Inflation Factor

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SeqScreen: a biocuration platform for robust taxonomic and biological process characterization of nucleic acid sequences of interest

SeqScreen: a biocuration platform for robust taxonomic and b...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Dreycey Albin Dan Nasko R. A. Leo Elworth Jacob Lu Advait Balaji Christian Diaz Nidhi Shah Jeremy Selengut Chris Hulme-Lowe Pravin Muthu Gene Godbold Mikael Lindvall Madeline Diep Adam Porter Mihai Pop Krista Ternus Todd J. Treangen Program in Systems Synthetic and Physical Biology Rice University Center for Bioinformatics and Computational Biology University of Maryland Department of Computer Science Rice University Signature Science LLC Fraunhofer Center for Experimental Software Engineering Department of Computer Science University of Maryland

ISBN: (纸本)9781728118680

Rapid advancements in synthetic biology and nucleic acid synthesis, in particular concerns about its intentional or accidental misuse, call for more sophisticated screening tools to identify genes of interest within short sequence fragments. One major gap in predicting genes of concern is the inadequacy of current tools and ontologies to describe the specific biological processes of pathogenic proteins. The objective of this work is to design software that sensitively assigns taxonomic classifications, functional annotations, and biological processes of interest to short nucleotide sequences of unknown origin (50bp-1,000bp). The overarching goal is to perform sensitive characterization of short sequences and highlight specific pathogenic biological processes of interest (BPoIs). The SeqScreen software executes these tasks in analytical workflows with Nextflow and outputs results in a tab-delimited report. Local and global alignments differentiate hits to taxonomically-related sequences from similar but unrelated sequences, and an ensemble approach leverages multiple tools and databases to assign a variety of functional terms to each query sequence. Final biological process assessments are made from the predicted functional annotations, which leverage information in pre-existing databases, as well as new custom biocurations. Machine learning models predict each biological process of interest on large protein databases before incorporation into the SeqScreen framework to streamline computational efficiency, ensure reproducible results, allow for version control, and facilitate the review of the automated predictions by expert biocurators. The SeqScreen source code is available at .

关键词： Biological Process interest Base Sequence Nucleic acids Version control protein database Database synthetic biology ANNOTATIONS Leverage

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