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Reverse Engineering of Cell Senescence

细胞衰老的逆向工程

基本信息

批准号：
10365131
负责人：
MARC Wallace KIRSCHNER
金额：
$ 63.84万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-15 至 2026-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10365131
关键词：
Address Affect Age Aging Animals Biochemical Biological Assay Cell Aging Cell Culture Techniques Cell Cycle Cell Cycle Arrest Cell Line Cell Size Cell Survival Cells Cellular biology Cessation of life Clustered Regularly Interspaced Short Palindromic Repeats Critical Pathways Cultured Cells DNA Damage Dasatinib Dependence Development Disease Event Exposure to FRAP1 gene Genes Goals Growth Growth Factor Hepatocyte Heterogeneity Hormones Human Hydrogen Peroxide Hypertrophy Inflammatory Insulin Ionizing radiation Kinetics Laboratories Learning Light Lipids Liver Logic Longevity Machine Learning Mass Spectrum Analysis Measures Methods Microscopic Microscopy Molecular Mus Mutagenesis Normal Cell Normal tissue morphology Oncogene Activation Pathology Pathway interactions Pattern Pharmaceutical Preparations Pharmacologic Substance Pharmacology Phenotype Phosphoproteins Phosphorylated Peptide Phosphorylation Site Phosphotransferases Physiological Plant Roots Process Proteins Proteomics Radiation Reactive Oxygen Species Research Personnel Reverse engineering Role Signal Transduction Sirolimus Site Small Interfering RNA Stress Symptoms Testing Time Tissues base cell growth cell type chemokine experimental study healthspan insight interest kinase inhibitor knock-down mTOR inhibition new technology novel therapeutics phosphoproteomics prevent programs proteostasis response senescence stressor tool

项目摘要

Abstract Cellular senescence is not just a symptom of aging, but a contributor to aging pathologies. Recent experiments in the mouse show that elimination of senescent cells (a.k.a. senolysis) can reverse several features of the aging process and extend life- and health-span. This has stimulated interest in cell senescence and in finding ways to suppress it that can be easily ported from mouse to human and made even more effective. This implies looking for such drugs as an early senolytic candidate – a kinase inhibitor dasatinib, whose mode of action is unclear. Taking senolytic therapies into human will also require a better understanding of the cell type specific progression towards senescence and diverse sub-types of senescence. In addition to aging per se cells respond to insults such as DNA damage, cell cycle arrest or oncogene activation by expressing aging phenotypes, such as hypertrophy, cell cycle arrest markers, and by secreting growth factors and chemokines, which are thought to produce degenerative responses in nearby cells. Hypertrophy may be one of the most pervasive senescent cell responses but has been hard to study, as until recently we have not had accurate enough means to measure cell mass and cell size, protein and lipid content. Yet, hypertrophy is of special interest because it is deeply connected to cell growth, which is normally under strict control in normal cells. We propose to study how cells, prompted by stressors like radiation or drugs or simply by age, become hypertrophic and how they come to express senescence markers. We have three goals: 1) to trace the progression of cell senescence in molecular terms by quantitative mass spectrometry and phospho-mass spectrometry, and in physiological terms by Raman microscopy, 2) to identify protein circuits responsible for cell senescence, and 3) to find drugs that will prevent, reverse, or eliminate senescent cells. As our tools of perturbation, we have chosen kinase inhibitors. Employing a machine learning approach, we will probe senescent cell development and senescent cell viability via a small set of well-characterized poly-specific kinase inhibitors; then regress a phenotype, such as senescence- specific signaling and senescent cell formation or death, to the activity of key kinases and their downstream circuits. Kinases implicated that way will be independently validated using siRNA knock downs and CRISPR. Additionally, extensive phospho-proteomic profiling will identify the phosphorylation sites on key proteins that most contribute to the phenotypes of senescence and which could be druggable by other means. Taken together, these methods will produce insight into the mode of action of already identified senolytic drugs and suggest new targets and new drugs for seno-therapies. This combination of pharmacology, machine learning, quantitative proteomics and new forms of microscopy can provide fresh insights into aging and suggest potential therapies for aging-related disease.

摘要