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From rare to more common: The emerging role of omics in improving understanding and treatment of severe inflammatory and hyperinflammatory conditions

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Journal of Allergy and Clinical Immunology 2025年第5期155卷 1435-1450页

作者： Keskitalo, Salla Seppänen, Mikko R.J. del Sol, Antonio Varjosalo, Markku Institute of Biotechnology Helsinki Institute of Life Science HiLIFE University of Helsinki Helsinki Finland Pediatric Research Center New Children's Hospital University of Helsinki and HUS Helsinki University Hospital Helsinki Finland Translational Immunology Research Program University of Helsinki Helsinki Finland European Reference Network Rare Immunodeficiency Autoinflammatory and Autoimmune Diseases Network (ERN RITA) Core Center Utrecht Netherlands Computational Biology Group Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg Computational Biology Group Basque Research and Technology Alliance (CIC bioGUNE-BRTA) Derio Spain Ikerbasque Basque Foundation for Science Bilbao Spain Department of Biochemistry and Developmental Biology and Translational Cancer Medicine Program Faculty of Medicine University of Helsinki Finland Helsinki Finland

Inflammation is a pathogenic driver of many diseases, including atherosclerosis and rheumatoid arthritis. Hyperinflammation can be seen as any inflammatory response that is deleterious to the host, regardless of cause. In medicine, hyperinflammation is defined as severe, deleterious, and fluctuating systemic or local inflammation with presence of a cytokine storm. It has been associated with rare autoinflammatory disorders. However, advances in omics technologies, including genomics, proteomics, and metabolomics, have revealed it to be more common, occurring in sepsis and severe coronavirus disease 2019. With a focus on proteomics, this review highlights the key role of omics in this shift. Through an exploration of research, we present how omics technologies have contributed to improved diagnostics, prognostics, and targeted therapeutics in the field of hyperinflammation. We also discuss the integration of advanced technologies, multiomics approaches, and artificial intelligence in analyzing complex datasets to develop targeted therapies, and we address their potential for revolutionizing the clinical aspects of hyperinflammation. We emphasize personalized medicine approaches for effective treatments and outline challenges, including the need for standardized methodologies, robust bioinformatics tools, and ethical considerations regarding data privacy. This review aims to provide a comprehensive overview of the molecular mechanisms underpinning hyperinflammation and underscores the potential of omics technologies in enabling successful clinical management. © 2025 The Authors

关键词： artificial intelligence biomarker discovery cytokine storm genomics Hyperinflammation inflammation metabolomics multiomics omics technologies proteomics

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Engineering mtDNA deletions by reconstituting end joining in human mitochondria

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Cell 2025年第10期188卷 2778-2793.e21,24页

作者： Fu, Yi Land, Max Kavlashvili, Tamar Cui, Ruobing Kim, Minsoo DeBitetto, Emily Lieber, Toby Ryu, Keun Woo Choi, Elim Masilionis, Ignas Saha, Rahul Takizawa, Meril Baker, Daphne Tigano, Marco Lareau, Caleb A. Reznik, Ed Sharma, Roshan Chaligne, Ronan Thompson, Craig B. Sfeir, Agnel Molecular Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY United States Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY United States Cancer Biology and Genetics Program Memorial Sloan Kettering Cancer Center New York NY United States Computational Oncology Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY United States Department of Pathology and Genomic Medicine Thomas Jefferson University Philadelphia PA United States Howard Hughes Medical Institute New York NY United States

Recent breakthroughs in the genetic manipulation of mitochondrial DNA (mtDNA) have enabled precise base substitutions and the efficient elimination of genomes carrying pathogenic mutations. However, reconstituting mtDNA deletions linked to mitochondrial myopathies remains challenging. Here, we engineered mtDNA deletions in human cells by co-expressing end-joining (EJ) machinery and targeted endonucleases. Using mitochondrial EJ (mito-EJ) and mito-ScaI, we generated a panel of clonal cell lines harboring a ∼3.5 kb mtDNA deletion across the full spectrum of heteroplasmy. Investigating these cells revealed a critical threshold of ∼75% deleted genomes, beyond which oxidative phosphorylation (OXPHOS) protein depletion, metabolic disruption, and impaired growth in galactose-containing media were observed. Single-cell multiomic profiling identified two distinct nuclear gene deregulation responses: one triggered at the deletion threshold and another progressively responding to heteroplasmy. Ultimately, we show that our method enables the modeling of disease-associated mtDNA deletions across cell types and could inform the development of targeted therapies. © 2025 Elsevier Inc.

关键词： DOGMA-seq end joining mitochondrial pathologies mtDNA mtDNA deletion

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The grand biological universe: A comprehensive geometric construction of genome space

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Innovation 2025年

作者： Yu, Hongyu Sun, Nan Ren, Ruohan Zhou, Tao Guan, Mengcen Zhao, Leqi Yau, Stephen S.-T. Department of Mathematical Sciences Tsinghua University Beijing 100084 China Beijing Institute of Mathematical Sciences and Applications (BIMSA) Beijing 101408 China Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York 10065 NY United States Tri-Institutional Training Program in Computational Biology and Medicine Weill Cornell Medicine New York 10065 NY United States

Analyzing the geometric relationships among genomic sequences from a mathematical perspective and revealing the laws hidden within these relationships is a crucial challenge in bioinformatics. The natural vector method constructs a genome space by extracting statistical moments of k-mers to illuminate the relationships among genomes. This approach highlights a fundamental law in biology known as the convex hull principle, which states that natural vectors corresponding to different types of biological sequences form distinct, non-overlapping convex hulls. Previous studies have validated this important principle across various datasets. However, they often focused on specific kingdoms and did not thoroughly analyze the significance of the dimensions required for the convex hull separation. In this study, we integrate all reliable sequences from different kingdoms to construct the grand biological universe, within which we comprehensively validate the multi-level convex hull principle. We demonstrate that the separation of convex hulls arises from biological properties rather than mathematical characteristics of high-dimensional spaces. Furthermore, we develop suitable metrics within the grand biological universe to facilitate efficient sequence classification. This research advances the convex hull principle through both theoretical development and experimental validation, making significant contributions to the understanding of the geometric structure of genome space. © 2025 The Author(s)

关键词： genome space grand biological universe multi-level convex hull principle natural vector

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Methods for multiplexing single-cell multi-omics

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Nature methods 2025年 2025 May 26页

作者： Laura C Kida Caleb A Lareau Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA. lareauc@***.

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Methylation-based biological age and cardiotoxicity risk in breast cancer patients treated with trastuzumab

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Cardio-Oncology 2025年第1期11卷 1-10页

作者： Mammadova, Jamila Richards, Alicia Gonzalez-Torriente, Adriana Adler, Evan R. Cruz, Rachel J. Palfi, Stefanie Lee, Dae Hyun Sam, Christine Al-Jumayli, Mohammed Berglund, Anders Park, Jong Y Alomar, Mohammed Kresovich, Jacob K. Department of Internal Medicine Vanderbilt University Medical Center Nashville TN United States Department of Cancer Epidemiology H. Lee Moffitt Cancer Center and Research Institute Tampa FL United States Department of Pediatrics University of Miami/Jackson Health Systems Miami FL United States Department of Internal Medicine University of Virginia Charlottesville VA United States Division of Cardiovascular Sciences Morsani College of Medicine University of South Florida Tampa United States Department of Cardio-Oncology H. Lee Moffitt Cancer Center and Research Institute Tampa FL United States Department of Individualized Cancer Management/Senior Adult Oncology Program H. Lee Moffitt Cancer Center and Research Institute Tampa FL United States Department of Oncological Sciences Morsani College of Medicine University of South Florida Tampa FL United States Department of Quantitative Health Sciences Division of Computational Biology Mayo Clinic Jacksonville FL United States Department of Breast Oncology H. Lee Moffitt Cancer Center and Research Institute Tampa FL United States

Background: Trastuzumab is an effective treatment for HER2-positive cancers that has known cardiotoxic properties. Discovering biomarkers that assess cardiotoxicity risk before trastuzumab therapy is essential for protecting the cardiovascular health of cancer patients. Objective: To examine the associations between pre-treatment epigenetic age acceleration, circulating leukocyte composition, and candidate single nucleotide polymorphisms (SNPs) with cardiotoxicity risk in breast cancer patients receiving trastuzumab. Methods: Among a retrospective cohort of HER2-positive breast cancer patients treated with trastuzumab at Moffitt cancer Center, we profiled blood DNA methylation and genetic profiles. Epigenetic clocks and circulating leukocyte subsets were derived from MethylationEPIC BeadChip data, and candidate SNPs were measured using the Global Screening Array. Cardiotoxicity events (i.e., reductions in left ventricular ejection fraction, symptomatic heart failure), were identified in medical records. Logistic regression models, adjusted for traditional risk factors, estimated odds ratios (ORs) for biomarker associations with cardiotoxicity risk. Results: Among 157 patients selected for this study, 39 (25%) experienced cardiotoxicities within one year of treatment initiation. rs776746 was inversely associated with cardiotoxicity risk (OR: 0.38, 95% CI: 0.14, 1.00, P = 0.05). After adjusting for traditional risk factors and leukocyte composition, the Hannum AgeAccel, Horvath AgeAccel, and Horvath Skin and Blood AgeAccel metrics were significantly positively associated with cardiotoxicity risk (ORs ranging between 1.62 and 1.89). Adding Horvath Skin and Blood AgeAccel to traditional cardiotoxicity risk factors significantly improved cardiotoxicity risk prediction (AUC: 0.75 vs. 0.79;P-diff = 0.04). Conclusions: Pre-treatment epigenetic age acceleration appears to be a novel biomarker for cardiotoxicity risk that improves cardiotoxicity risk prediction. © The Author(

关键词： Biological age Breast cancer Cardiotoxicity Epigenetic clocks Trastuzumab

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BPS2025 - High-throughput fluorescence-based binding assay enables systematic analysis of kinase inhibitors for cancer therapy

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Biophysical Journal 2025年第3期124卷 221a-221a页

作者： Mark Polk Sukrit Singh Erica Goldberger Sonya M. Hanson John D. Chodera Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Flatiron Institute New York NY USA

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BPS2025 - Characterizing oligomeric amyloid-β42 and POPC interactions through molecular dynamics: A computational approach for understanding Alzheimer’s disease

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Biophysical Journal 2025年第3期124卷 70a-70a页

作者： Emma M. Cleveland Kelsie M. King Anne M. Brown Systems Biology Virginia Tech Blacksburg VA USA Genetics Bioinformatics and Computational Biology program Virginia Tech Christiansburg VA USA Department of Biochemistry Virginia Tech University Blacksburg VA USA

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Characterization of cell fate probabilities in single-cell data with Palantir (vol 37, pg 451, 2019)

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NATURE BIOTECHNOLOGY 2019年第10期37卷 1237-1237页

作者： Setty, Manu Kiseliovas, Vaidotas Levine, Jacob Gayoso, Adam Mazutis, Linas Pe'er, Dana Program for Computational and Systems Biology Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY USA

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir’s resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.

关键词： computational models Gene regulation Gene regulatory networks Haematopoiesis Machine learning

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Correction to: Multiplexed inhibition of immunosuppressive genes with Cas13d for combinatorial cancer immunotherapy (Nature Biotechnology, (2025), 10.1038/s41587-024-02535-2)

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Nature Biotechnology 2025年

作者： Zhang, Feifei Chow, Ryan D. He, Emily Dong, Chuanpeng Xin, Shan Mirza, Daniyal Feng, Yanzhi Tian, Xiaolong Verma, Nipun Majety, Medha Zhang, Yueqi Wang, Guangchuan Chen, Sidi Department of Genetics Yale University School of Medicine New HavenCT United States System Biology Institute Yale University West HavenCT United States Center for Cancer Systems Biology Yale University West HavenCT United States M.D.–Ph.D. Program Yale University West HavenCT United States Molecular Cell Biology Genetics and Development Program Yale University New HavenCT United States Yale College New HavenCT United States Immunobiology Program Yale University New HavenCT United States Department of Therapeutic Radiology Yale University School of Medicine New HavenCT United States Department of Neurosurgery Yale University School of Medicine New HavenCT United States Yale Comprehensive Cancer Center Yale University School of Medicine New HavenCT United States Yale Stem Cell Center Yale University School of Medicine New HavenCT United States Yale Center for Biomedical Data Science Yale University School of Medicine New HavenCT United States Key Laboratory of Multi-Cell Systems Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China

Correction to: Nature Biotechnologyhttps://***/10.1038/s41587-024-02535-2, published online 16 January 2025. In the version of the article initially published, Sidi Chen was incorrectly associated with a present address. This has now been corrected in the HTML and PDF versions of the article. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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BPS2025 - Mechanism-based modeling of drug-resistant mutations in cancer

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Biophysical Journal 2025年第3期124卷 505a-505a页

作者： Sukrit Singh John D. Chodera Markus Seeliger Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA State University of New York Stony Brook Stony Brook NY USA

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