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Single-Cell Multi-Omics in Human Clonal Hematopoiesis Reveals That DNMT3A R882 Mutations Perturb Early Progenitor States through Selective Hypomethylation

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BLOOD 2020年第Sup1期136卷 1-2页

作者： Nam, Anna S. Dusaj, Neville Izzo, Franco Murali, Rekha Mouhieddine, Tarek H. Myers, Robert M. Sotelo, Jesus Benbarche, Salima Gaiti, Federico Tahri, Sabrin Abdel-Wahab, Omar Ghobrial, Irene M. Chaligne, Ronan Landau, Dan A. Department of Pathology and Laboratory Medicine Weill Cornell Medicine NEW YORK NY Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New York NY Meyer Cancer Center & Institute of Computational Biomedicine Weill Cornell Medicine New York NY Weill Cornell Medicine New York NY Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School New York NY Tri-Institutional MD-PhD Program Weill Cornell Medicine Rockefeller University Memorial Sloan Kettering Cancer Center New York Weill Cornell Medicine New York Memorial Sloan Kettering Cancer Center New York NY Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School Boston MA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Sandra and Edward Meyer Cancer Center Weill Cornell Medicine New York NY Division of Hematology and Medical Oncology Department of Medicine Weill Cornell Medicine New York NY

Leukemia driver mutations have been identified in clonal hematopoiesis (CH; Jaiswal et al, NEJM, 2014). This provides a window of opportunity to interrogate the downstream impact of driver mutations in the earliest stages of neoplasia, before the accumulation of additional drivers that lead to frank malignancy. However, CH mutated cells are morphologically and phenotypically similar to normal cells. Thus, previous characterization in primary human samples have largely focused on genetic identification of these clonal outgrowths. To overcome this limitation and define the downstream effects of CH mutations, we leveraged multi-omic single-cell sequencing to profile the mutation status and whole transcriptome for the same cells at high-throughput (GoT; Nam et al, Nature, 2019). To identify CH samples, we screened 136 stem cell grafts collected for autologous transplant from multiple myeloma patients in remission (Mouhieddine et al, Nat Comm, 2020). We identified four individuals with DNMT3A R882 mutations with VAF > 0.05, allowing the profiling of 27,324 CD34+ cells (Fig. 1a-b). DNMT3A R882 mutations in these human CH samples did not result in a significant differentiation block in the hematopoietic stem cells, as assessed by pseudotime analysis (Fig. 1c). However, specific differentiation skews were apparent in multi-lineage progenitors, corresponding to the highest expression of DNMT3A in mutated immature myeloid progenitors (IMP, i.e. common myeloid progenitors/granulocyte-monocyte progenitors, Fig. 1d). We observed the expansion of mutated IMPs (Fig. 1e) that showed priming toward the megakaryocytic-erythroid (ME) lineage (Fig. 1f). DNMT3A R882 lympho-myeloid primed progenitors showed a myeloid bias and disfavored lymphoid development (Fig. 1g). In these CD34+ progenitors, we identified dysregulated expressions of megakaryocytic lineage markers, such as CD9 and PLEK , consistent with the ME-bias (Fig. 1h). We also observed downregulation of lymphoid differentiation

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Deep learning mutation prediction enables early stage lung cancer detection in liquid biopsy 6

Deep learning mutation prediction enables early stage lung c...

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6th International Conference on Learning Representations, ICLR 2018

作者： Kothen-Hill, Steven T. Zviran, Asaf Schulman, Rafi Maloney, Dillon Huang, Kevin Y. Liao, Will Robine, Nicolas Deochand, Sunil Omans, Nathaniel D. Landau, Dan A. Weill Cornell Medicine Meyer Cancer Center New YorkNY10065 United States New York Genome Center New YorkNY10003 United States New York University New YorkNY10003 United States Tri-Institutional Program in Computational Biology and Medicine Weill Cornell Medicine New YorkNY10065 United States Weill Cornell Medicine Division of Hematology and Medical Oncology New YorkNY10065 United States

Somatic cancer mutation detection at ultra-low variant allele frequencies (VAFs) is an unmet challenge that is intractable with current state-of-the-art mutation calling methods. Specifically, the limit of VAF detection is closely related to the depth of coverage due to the requirement of multiple supporting reads in extant methods, precluding the detection of mutations at VAFs that are orders of magnitude lower than the depth of coverage. Nevertheless, the ability to detect cancer-associated mutations in ultra low VAFs is a fundamental requirement for low-tumor burden cancer diagnostics applications such as early detection, monitoring, and therapy nomination using liquid biopsy methods (cell-free DNA). Here we defined a spatial representation of sequencing information adapted for convolutional architecture that enables variant detection in a manner independent of the depth of sequencing. This method enables the detection of cancer mutations even in VAFs as low as 104, more than two orders of magnitude below the current state-of-the-art. We validated our method on both simulated plasma and on clinical cfDNA plasma samples from cancer patients and non-cancer controls. This method introduces a new domain within bioinformatics and personalized medicine - somatic whole genome mutation calling for liquid biopsy. © 6th International Conference on Learning Representations, ICLR 2018 - Workshop Track Proceedings. All rights reserved.

关键词： Liquids

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Enhanced photostability of cyanine fluorophores across the visible spectrum (vol 9, pg 428, 2012)

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NATURE METHODS 2012年第6期9卷 626-626页

作者： Altman, Roger B. Zheng, Qinsi Zhou, Zhou Terry, Daniel S. Warren, J. David Blanchard, Scott C. Department of Physiology and Biophysics Weill Medical College of Cornell University New York New York USA. Tri-Institutional Training Program in Computational Biology and Medicine Weill Medical College of Cornell University New York New York USA. Department of Biochemistry Weill Medical College of Cornell University New York New York USA. Department of Biochemistry Weill Medical College of Cornell University New York New York USA.

Corrected online 27 April 2012In the version of this article initially published, the y-axis label on a graph in Figure 1b was incorrect. The error has been corrected in the HTML and PDF versions of the *** informationChange historyAuthor informationSupplementary informationPrimary authorsThese authors contributed equally to this *** B Altman & Qinsi ZhengAffiliationsDepartment of Physiology and Biophysics, Weill Medical College of Cornell University, New York, New York, USA. Roger B Altman,Qinsi Zheng,Zhou Zhou,Daniel S Terry &Scott C Blanchardtri-institutional Training program in Chemical biology, Weill Medical College of Cornell University, New York, New York, USA. Qinsi Zheng &Scott C Blanchardtri-institutional Training program in computational biology and medicine, Weill Medical College of Cornell University, New York, New York, USA. Daniel S Terry &Scott C BlanchardDepartment of Biochemistry, Weill Medical College of Cornell University, New York, New York, USA. J David Warren &Scott C Blanchard Competing financial interestsThe authors declare no competing financial interests.

关键词： Fluorescent dyes Imaging

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Wasserstein wormhole: scalable optimal transport distance with transformer 24

Wasserstein wormhole: scalable optimal transport distance wi...

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Proceedings of the 41st International Conference on Machine Learning

作者： Doron Haviv Russell Zhang Kunes Thomas Dougherty Cassandra Burdziak Tal Nawy Anna Gilbert Dana Pe'er Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center and Tri- Institutional Training Program in Computational Biology and Medicine Weill Cornell Medicine Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center and Department of Statistics Columbia University Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center Department of Mathematics and Statistics Yale University Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center and Howard Hughes Medical Institute

Optimal transport (OT) and the related Wasserstein metric (W) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS). We present Wasserstein Wormhole, a transformer-based autoencoder that embeds empirical distributions into a latent space wherein Euclidean distances approximate OT distances. Extending MDS theory, we show that our objective function implies a bound on the error incurred when embedding non-Euclidean distances. Empirically, distances between Wormhole embeddings closely match Wasserstein distances, enabling linear time computation of OT distances. Along with an encoder that maps distributions to embeddings, Wasserstein Wormhole includes a decoder that maps embeddings back to distributions, allowing for operations in the embedding space to generalize to OT spaces, such as Wasserstein barycenter estimation and OT interpolation. By lending scalability and interpretability to OT approaches, Wasserstein Wormhole unlocks new avenues for data analysis in the fields of computational geometry and single-cell biology. Software is available at http://***/en/latest/.

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Fenofibrate prevents skeletal muscle loss in mice with lung cancer (vol 115, pg E743, 2018)

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2018年第9期115卷 E2146-E2146页

作者： Goncalves, Marcus D. Hwang, Seo-Kyoung Pauli, Chantal Murphy, Charles J. Cheng, Zhe Hopkins, Benjamin D. Wu, David Loughran, Ryan M. Emerling, Brooke M. Zhang, Guoan Fearon, Douglas T. Cantley, Lewis C. Meyer Cancer Center Weill Cornell Medicine New York NY 10021. Department of Medicine Weill Cornell Medicine New York NY 10021. Division of Endocrinology Department of Medicine Weill Cornell Medicine New York NY 10021. Institute for Pathology and Molecular Pathology University Hospital Zurich 8091 Zurich Switzerland. Tri-Institutional Training Program in Computational Biology and Medicine Weill Cornell Medicine New York NY 10021. Proteomics and Metabolomics Core Facility Weill Cornell Medicine New York NY 10021. CSHL Cancer Center Cold Spring Harbor Laboratory Cold Spring Harbor NY 11724. Meyer Cancer Center Weill Cornell Medicine New York NY 10021 lcantley@med.cornell.edu.

The cancer anorexia cachexia syndrome is a systemic metabolic disorder characterized by the catabolism of stored nutrients in skeletal muscle and adipose tissue that is particularly prevalent in nonsmall cell lung cancer (NSCLC). Loss of skeletal muscle results in functional impairments and increased mortality. The aim of the present study was to characterize the changes in systemic metabolism in a genetically engineered mouse model of NSCLC. We show that a portion of these animals develop loss of skeletal muscle, loss of adipose tissue, and increased inflammatory markers mirroring the human cachexia syndrome. Using noncachexic and fasted animals as controls, we report a unique cachexia metabolite phenotype that includes the loss of peroxisome proliferator-activated receptor-α (PPARα) -dependent ketone production by the liver. In this setting, glucocorticoid levels rise and correlate with skeletal muscle degradation and hepatic markers of gluconeogenesis. Restoring ketone production using the PPARα agonist, fenofibrate, prevents the loss of skeletal muscle mass and body weight. These results demonstrate how targeting hepatic metabolism can prevent muscle wasting in lung cancer, and provide evidence for a therapeutic strategy.

关键词： cachexia fenofibrate glucocorticoids ketones skeletal muscle

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TET proteins safeguard bivalent promoters from de novo methylation in human embryonic stem cells (vol 50, pg 83, 2017)

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NATURE GENETICS 2018年第5期50卷 764-764页

作者： Verma, Nipun Pan, Heng Dore, Louis C. Shukla, Abhijit Li, Qing V. Pelham-Webb, Bobbie Teijeiro, Virginia Gonzalez, Federico Krivtsov, Andrei Chang, Chan-Jung Papapetrou, Eirini P. He, Chuan Elemento, Olivier Huangfu, Danwei Developmental Biology Program Sloan Kettering Institute New York NY USA Weill Graduate School of Medical Sciences at Cornell University/The Rockefeller University/Sloan Kettering Institute Tri-Institutional MD-PhD Program New York NY USA Department of Physiology and Biophysics Englander Institute for Precision Medicine Institute for Computational Biomedicine Weill Cornell Medical College New York NY USA Weill Cornell Graduate School of Medical Sciences Weill Cornell Medical College New York NY USA Department of Chemistry Department of Biochemistry and Molecular Biology Institute for Biophysical Dynamics Howard Hughes Medical Institute University of Chicago Chicago IL USA Louis V. Gerstner Jr. Graduate School of Biomedical Sciences Memorial Sloan Kettering Cancer Center New York NY USA Cancer Biology and Genetics Program Sloan Kettering Institute New York NY USA Department of Oncological Sciences and Black Family Stem Cell Institute Icahn School of Medicine at Mount Sinai New York NY USA

TET enzymes oxidize 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), which can lead to DNA demethylation. However, direct connections between TET-mediated DNA demethylation and transcriptional output are difficult to establish owing to challenges in distinguishing global versus locus-specific effects. Here we show that TET1, TET2 and TET3 triple-knockout (TKO) human embryonic stem cells (hESCs) exhibit prominent bivalent promoter hypermethylation without an overall corresponding decrease in gene expression in the undifferentiated state. Focusing on the bivalent PAX6 locus, we find that increased DNMT3B binding is associated with promoter hypermethylation, which precipitates a neural differentiation defect and failure of PAX6 induction during differentiation. dCas9-mediated locus-specific demethylation and global inactivation of DNMT3B in TKO hESCs partially reverses the hypermethylation at the PAX6 promoter and improves differentiation to neuroectoderm. Taking these findings together with further genome-wide methylation and TET1 and DNMT3B ChIP-seq analyses, we conclude that TET proteins safeguard bivalent promoters from de novo methylation to ensure robust lineage-specific transcription upon differentiation.

关键词： Epigenetics Stem cells

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The MetaSUB microbiome core analysis pipeline enables large scale metagenomic analysis

arXiv

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arXiv 2020年

作者： Danko, David C. Mason, Christopher Tri-Institutional Computational Biology & Medicine Program Cornell University NY United States Institute for Computational Biomedicine Department of Physiology and Biophysics Weill Cornell Medicine of Cornell University NY United States Feil Family Brain and Mind Research Institute Weill Cornell Medicine NY United States

Motivation Accurate data analysis and quality control is critical for metagenomic studies. Though many tools exist to analyze metagenomic data there is no consistent framework to integrate and run these tools across projects. Currently, computational analysis of metagenomes is time consuming, often misses potentially interesting results, and is difficult to reproduce. Further, comparison between metagenomic studies is hampered by inconsistencies in tools and databases. Results We present the MetaSUB Core Analysis Pipeline (CAP) a comprehensive tool to analyze metagenomes and summarize the results of a project. The CAP is designed in a bottom up fashion to perform QC, preprocessing, analysis and even to build relevant databases and install necessary tools. Availability and Implementation The CAP is available under an MIT License on GitHub at https://***/MetaSUB/CAP2 and on the Python Package Index. Documentation and examples are available on GitHub. Copyright © 2020, The Authors. All rights reserved.

关键词： Pipelines

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Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers

arXiv

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arXiv 2024年

作者： Haviv, Doron Kunes, Russell Zhang Dougherty, Thomas Burdziak, Cassandra Nawy, Tal Gilbert, Anna Pe’er, Dana Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center United States Tri-Institutional Training Program in Computational Biology and Medicine Weill Cornell Medicine United States Department of Statistics Columbia University United States Department of Mathematics and Statistics Yale University United States Howard Hughes Medical Institute United States

关键词： computational geometry

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Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory

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Nature communications 2025年第1期16卷 4450页

作者： Suraj Rajendran Zhenxing Xu Weishen Pan Chengxi Zang Ilias Siempos Lisa Torres Jie Xu Jiang Bian Edward J Schenck Fei Wang Tri-Institutional Computational Biology & Medicine Program Cornell University Cornell NY USA. Division of Health Informatics Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Division of Pulmonary and Critical Care Medicine Department of Medicine New York-Presbyterian Hospital-Weill Cornell Medical Center Weill Cornell Medicine New York NY USA. First Department of Critical Care Medicine and Pulmonary Services Evangelismos Hospital National and Kapodistrian University of Athens Medical School Athens Greece. Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville FL USA. Department of Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN USA. Regenstrief Institute Indianapolis IN USA. Division of Pulmonary and Critical Care Medicine Department of Medicine New York-Presbyterian Hospital-Weill Cornell Medical Center Weill Cornell Medicine New York NY USA. ejs9005@med.cornell.edu. Division of Health Informatics Department of Population Health Sciences Weill Cornell Medicine New York NY USA. few2001@med.cornell.edu.

Corticosteroids decrease the duration of organ dysfunction in sepsis and a range of overlapping and complementary infectious critical illnesses, including septic shock, pneumonia and the acute respiratory distress syndrome (ARDS). The risk and benefit of corticosteroids are not fully defined using the construct of organ dysfunction duration. This retrospective multicenter, proof-of-concept study aimed to evaluate the association between usage of corticosteroids and mortality of patients with sepsis, pneumonia and ARDS by emulating a target trial framework stratified by predicted organ dysfunction trajectory. The study employed a two staged machine learning (ML) methodology to first subphenotype based on organ dysfunction trajectory then predict this defined trajectory. Once patients were classified by predicted trajectory we conducted a target trial emulation. Our analysis revealed that the association between corticosteroid use and 28-day mortality varied by predicted trajectory and between *** findings suggest that matching treatment strategies to empirically observed pathobiology may offer a more nuanced understanding of corticosteroid utility.

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End-to-End Differentiable Molecular Mechanics Force Field Construction

arXiv

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arXiv 2020年

作者： Wang, Yuanqing Fass, Josh Kaminow, Benjamin Herr, John E. Rufa, Dominic Zhang, Ivy Pulido, Iván Henry, Mike Chodera, John D. Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medical College Cornell University New YorkNY10065 United States Physiology Biophysics and System Biology Ph.D. Program Weill Cornell Medical College Cornell University New YorkNY10065 United States Program in Creative Writing Division of Humanities and Arts The City College of New York The City University of New York New YorkNY10031 United States

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with little inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset

关键词： Atoms

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