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KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection

Science (New York, N.Y.) 2025年第6735期387卷 eadn2337页

作者： Fagerberg, Eric Attanasio, John Dien, Christine Singh, Jaiveer Kessler, Emily A. Abdullah, Leena Shen, Jian Hunt, Brian G. Connolly, Kelli A. De Brouwer, Edward He, Jiaming Iyer, Nivedita R. Buck, Jessica Borr, Emily R. Damo, Martina Foster, Gena G. Giles, Josephine R. Huang, Yina H. Tsang, John S. Krishnaswamy, Smita Cui, Weiguo Joshi, Nikhil S. Department of Immunobiology Yale University School of Medicine New Haven CT United States Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth NH United States Department of Pathology Feinberg School of Medicine at Northwestern University Chicago IL United States Department of Genetics and Computer Science Yale University School of Medicine New Haven CT United States Department of Medicine University of Chicago Chicago IL United States Section of Hematology Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia PA United States Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Yale Center for Systems and Engineering Immunology Yale University School of Medicine New Haven CT United States New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University CT USA New Haven United States Applied Math Program Yale University New Haven CT United States

Naïve CD8 T cells have the potential to differentiate into a spectrum of functional states during an immune response. How these developmental decisions are made and what mechanisms exist to suppress differentiation toward alternative fates remains unclear. We employed in vivo CRISPR-Cas9-based perturbation sequencing to assess the role of ~40 transcription factors (TFs) and epigenetic modulators in T cell fate decisions. Unexpectedly, we found that knockout of the TF Klf2 resulted in aberrant differentiation to exhausted-like CD8 T cells during acute infection. KLF2 was required to suppress the exhaustion-promoting TF TOX and to enable the TF TBET to drive effector differentiation. KLF2 was also necessary to maintain a polyfunctional tumor-specific progenitor state. Thus, KLF2 provides effector CD8 T cell lineage fidelity and suppresses the exhaustion program.

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Predicting Protein Interaction Network Perturbation by Alternative Splicing with Semi-Supervised Learning

SSRN

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SSRN 2020年

作者： Narykov, Oleksandr Johnson, Nathan Korkin, Dmitry Department of Computer Science Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States Harvard Program in Therapeutic Sciences Harvard Medical School Breast Tumor Immunology Laboratory Dana-Farber Cancer Institute BostonMA United States Department of Computer Science Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States

Alternative splicing introduces a new layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. In particular, recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions, yet the extent to which the macromolecular interactions are affected by alternative splicing has to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here we develop an accurate feature-based machine learning approach that predicts if a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method called ALTernatively Spliced INteractions prediction (ALT-IN) Tool was compared with the state-of-the-art PPI prediction tools and showed superior performance, achieving 0.92 in precision and recall values. © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Wire Before You Walk

Wire Before You Walk

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Asilomar Conference on Signals, Systems & Computers

作者： Tesfa Asmara Dhananjay Bhaskar Ian Adelstein Smita Krishnaswamy Michael Perlmutter Department of Computer Science Pomona College Claremont CA USA Mathematics and Statistics Pomona College Claremont CA USA Department of Genetics Yale School of Medicine New Haven CT USA Department of Mathematics Yale University New Haven CT USA Department of Computer Science Yale University New Haven CT USA Program for Applied Mathematics Yale University New Haven CT USA Computational Biology and Bioinformatics Program Yale University New Haven CT USA Wu Tsai Institute Yale University New Haven CT USA Department of Mathematics Boise State University Boise ID USA

Node embeddings aim to associate a vector to every vertex of a graph which can then be used for downstream tasks such as clustering, classification, or link prediction. Many popular node embeddings such as no $d$ e2vec and DeepWalk are based upon counting which nodes frequently co-occur in random walks of the graph. In this paper, we show that the performance of such algorithms can be improved by rewiring the edges of the graph through a variety of network indices before running DeepWalk. These rewirings effectively give the random walker an inductive bias and increase the accuracy of a logistic regression classifier applied to the node embedding on several benchmark data sets.

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Mapping Emergency Medicine Data to the Observational Medical Outcomes Partnership Common Data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency Data Registry

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JACEP Open 2025年第1期6卷 100016-100016页

作者： Cohen, Inessa Diao, Zihan Goyal, Pawan Gupta, Aarti Hawk, Kathryn Malcom, Bill Malicki, Caitlin Sharma, Dhruv Sweeney, Brian Weiner, Scott G. Venkatesh, Arjun Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States American College of Emergency Physicians Washington DC United States Department of Emergency Medicine Brigham and Women's Hospital Boston MA United States Center for Outcomes Research and Evaluation (CORE) Section of Cardiovascular Medicine Yale School of Medicine New Haven CT United States

Objectives: This study aims to conduct a gap analysis to determine the feasibility of mapping electronic health record data from the Clinical Emergency Data Registry (CEDR) to the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM). Methods: We employed a structured approach using a custom-built comparison matrix. This matrix facilitated the alignment of CEDR data fields with the corresponding elements in the OMOP-CDM schema. Each field was evaluated for compatibility, with categorization into 3 distinct types: direct matches, fields requiring transformation, and fields with no OMOP-CDM equivalent. The mapping process was informed by consultations with the Observational Health Data Sciences and Informatics community forums and was guided by existing documentation and best practices in data harmonization. We performed descriptive analyses, quantifying the extent of direct matches and identifying the specific transformations needed for each CEDR-CDM field to ensure compliance with the OMOP-CDM model. Results: Our analysis indicates a high degree of compatibility between CEDR and OMOP, with over 90% (244/269) of CEDR fields being successfully mapped. Specifically, 173 fields had direct matches, whereas 71 required transformations. Challenges identified include addressing fields unique to CEDR with no OMOP-CDM equivalent and managing the transformations required for proper alignment. Conclusion: The OMOP-CDM presents a promising framework for standardizing emergency medicine data, thereby enhancing future query automation, analytics, and cross-institutional collaboration. Despite the potential challenges in capturing unique CEDR fields and addressing necessary transformations, most emergency department data can be standardized within the OMOP-CDM, fostering broader insights and applications in research and public health. © 2024 The Author(s)

关键词： databases electronic health records emergency service informatics opioid/adverse effects quality of health care registries

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Tessellated granular metamaterials with tunable elastic moduli

arXiv

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arXiv 2023年

作者： Pashine, Nidhi Wang, Dong Zhang, Jerry Patiballa, Sree Kalyan Witthaus, Sven Shattuck, Mark D. O’Hern, Corey S. Kramer-Bottiglio, Rebecca Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Benjamin Levich Institute Physics Department The City College of the City University of New York New YorkNY10031 United States

Most granular packings possess shear moduli (G) that increase with the applied external pressure, and bulk moduli (B) that increase or remain constant with pressure. This paper presents "tessellated" granular metamaterials for which both G and the ratio G/B decrease with increasing pressure. The granular metamaterials are made from flexible tessellations forming a ring of closed cells, each containing a small number of solid particles. For under-constrained tessellations, the dominant contributions to G and B are the particle-particle and particle-cell interactions. With specific particle configurations in the cells, we limit the number of possible particle rearrangements to achieve decreasing G as we increase the pressure difference between the inside and outside of the tessellation, leading to G/B 1 at large pressures. We further study tessellated granular metamaterials with cells containing a single particle and many particles to determine the variables that control the mechanical response of particle-filled tessellations as a function of pressure. © 2023, CC BY.

关键词： Metamaterials

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Assessment of scoring functions for computational models of protein-protein interfaces

arXiv

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arXiv 2024年

作者： Sumner, Jacob Meng, Grace Brandt, Naomi Grigas, Alex T. Córdoba, Andrés Shattuck, Mark D. O’Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Chemistry Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States Benjamin Levich Institute Physics Department City College of New York New YorkNY10031 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

A goal of computational studies of protein-protein interfaces (PPIs) is to predict the binding site between two monomers that form a heterodimer. The simplest version of this problem is to rigidly re-dock the bound forms of the monomers, which involves generating computational models of the heterodimer and then scoring them to determine the most native-like models. Scoring functions have been assessed previously using rank- and classification-based metrics, however, these methods are sensitive to the number and quality of models in the scoring function training set. We assess the accuracy of seven PPI scoring functions by comparing their scores to a measure of structural similarity to the x-ray crystal structure (i.e. the DockQ score) for a non-redundant set of heterodimers from the Protein Data Bank. For each heterodimer, we generate re-docked models uniformly sampled over DockQ and calculate the Spearman correlation between the PPI scores and DockQ. For some targets, the scores and DockQ are highly correlated;however, for many targets, there are weak correlations. Several physical features can explain the difference between difficult- and easy-to-score targets. For example, strong correlations exist between the score and DockQ for targets with highly intertwined monomers and many interface contacts. We also develop a new score based on only three physical features that matches or exceeds the performance of current PPI scoring functions. These results emphasize that PPI prediction can be improved by focusing on correlations between the PPI score and DockQ and incorporating more discriminating physical features into PPI scoring functions. © 2024, CC BY.

关键词： Monomers

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Resistance to BRAF inhibitors drives melanoma sensitivity to Chk1 inhibition

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Pharmacological research 2025年 217卷 107797页

作者： Danielle G Carvalho Juliana C N Kenski Daniel A Moreira Matheus A Rajão Oscar Krijgsman Carolina Furtado Mariana Boroni João P B Viola Daniel S Peeper Patricia A Possik Program of Immunology and Tumor Biology Division of Basic and Experimental Research Brazilian National Cancer Institute Rio de Janeiro Brazil. Division of Molecular Oncology and Immunology Oncode institute The Netherlands Cancer Institute Amsterdam the Netherlands. Laboratory of Bioinformatics and Computational Biology Division of Basic and Experimental Research Brazilian National Cancer Institute Rio de Janeiro Brazil Bioinformatics Core Facility Oswaldo Cruz Foundation Rio de Janeiro Brazil. Genomics core facility Brazilian National Cancer Institute Rio de Janeiro Brazil. Laboratory of Bioinformatics and Computational Biology Division of Basic and Experimental Research Brazilian National Cancer Institute Rio de Janeiro Brazil. Program of Immunology and Tumor Biology Division of Basic and Experimental Research Brazilian National Cancer Institute Rio de Janeiro Brazil Wellcome Sanger Institute Cambridgeshire Hinxton United Kingdom.

BRAF inhibitor-resistant melanomas (BRAFiR) acquire (epi)genetic and functional alterations that enable them to evade alternative treatments. Identifying these alterations is critical to advancing treatment strategies. Here, we explored the effect of Chk1 inhibition (Chk1i) on BRAFiR cells, revealing higher sensitivity compared to treatment-naïve cells both in vitro and in vivo. Using FUCCI-labeling and time-lapse microscopy, we show that S phase progression is required for Chk1i-induced cytotoxicity in BRAFiR cells, but not in treatment-naïve cells. Replication stress markers, including reduced BrdU incorporation and increased phospho-RPA and γH2AX, were observed mostly in BRAFiR cells with increased sensitivity to Chk1i. Untreated BRAFiR cells exhibited upregulated DNA replication genes, reduced progressing forks and increased origin firing, suggesting intrinsic replication changes. MAPK pathway reactivation in treatment-naïve cells mimicked BRAFiR traits, increasing sensitivity to Chk1i. These findings indicate that Chk1i exploits elevated replication stress specifically in BRAFiR cells, highlighting its therapeutic potential in overcoming MAPK inhibitor resistance in BRAF-mutant melanoma.

关键词： BRAF Melanoma Replication Stress Targeted therapy

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A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation

arXiv

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arXiv 2024年

作者： Tang, Xiangru Dai, Howard Knight, Elizabeth Wu, Fang Li, Yunyang Li, Tianxiao Gerstein, Mark Department of Computer Science Yale University New HavenCT06520 United States School of Medicine Yale University New HavenCT06520 United States Computer Science Department Stanford University CA94305 United States Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Department of Statistics & Data Science Yale University New HavenCT06520 United States Department of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://***/gersteinlab/GenAI4Drug. © 2024, CC BY.

关键词： Proteins

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Estimating Classification Confidence Using Kernel Densities

arXiv

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arXiv 2022年

作者： Salamon, Peter Salamon, David Cantu, V. Adrian An, Michelle Perry, Tyler Edwards, Robert A. Segall, Anca M. The Department of Mathematics and Statistics The Computational Science Research Center The Viral Information Institute San Diego State University San DiegoCA92182 United States The Department of Mathematics and Statistics San Diego State University San DiegoCA92182 United States The Computational Science Research Center The Department of Biology San Diego State University San DiegoCA92182 United States The Perelman School of Medicine The University of Pennsylvania 425 Johnson Pavilion 3610 Hamilton Walk PhiladelphiaPA19104 United States The Computational Science Research Center San Diego State University San DiegoCA92182 United States Google 1600 Amphitheater Parkway Mountain ViewCA94043 United States The Bioinformatics and Medical Informatics Program San Diego State University San DiegoCA92182 United States Helix 9875 Towne Center Drive San DiegoCA92121 United States The Department of Biology The Computational Science Research Center The Viral Information Institute The Bioinformatics and Medical Informatics Program San Diego State University San DiegoCA92182 United States

This paper investigates the post-hoc calibration of confidence for "exploratory" machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the "one-versus-all" approach (top-label calibration) must be used rather than the "calibrate-the-full-response-matrix" approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel, bulletproof algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) [1], [2] as well as the classic MNIST benchmark [3]. Finally, our analysis argues that post-hoc calibration should always be performed, should be based only on the test dataset, and should be sanity-checked visually. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

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Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

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JMIR Medical Education 2024年 10卷 e57594页

作者： Gilson, Aidan Safranek, Conrad W. Huang, Thomas Socrates, Vimig Chi, Ling Taylor, Richard Andrew Chartash, David Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States School of Medicine University College Dublin National University of Ireland Dublin Dublin Ireland Section for Biomedical Informatics and Data Science Yale University School of Medicine 300 George Street Suite 501 New Haven 06511 CT United States

In “How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge” (MIR Med Educ 2023;9:e45312) three additions were made to enhance discoverability. The title originally appeared as: How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment And has been changed to: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge In the “Objective” section of the Abstract, the following sentence: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Has been changed to read as: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination (USMLE) Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Finally, the abbreviation “USMLE” has been added to the Keywords section. The correction will appear in the online version of the paper on the JMIR Publications website on February 27, 2024 together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories. © 2024 JMIR Publications Inc.. All rights reserved.

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