1
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Wertman E. Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology. J Clin Med 2024; 13:4202. [PMID: 39064242 PMCID: PMC11277671 DOI: 10.3390/jcm13144202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/12/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024] Open
Abstract
Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.
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Affiliation(s)
- Eli Wertman
- Department of Neurology, Hadassah University Hospital, The Hebrew University, Jerusalem 9190500, Israel;
- Section of Neuropsychology, Department of Psychology, The Hebrew University, Jerusalem 9190500, Israel
- Or’ad: Organization for Cognitive and Behavioral Changes in the Elderly, Jerusalem 9458118, Israel
- Merhav Neuropsychogeriatric Clinics, Nehalim 4995000, Israel
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2
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Hansen J, Jain AR, Nenov P, Robinson PN, Iyengar R. From transcriptomics to digital twins of organ function. Front Cell Dev Biol 2024; 12:1240384. [PMID: 38989060 PMCID: PMC11234175 DOI: 10.3389/fcell.2024.1240384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 05/30/2024] [Indexed: 07/12/2024] Open
Abstract
Cell level functions underlie tissue and organ physiology. Gene expression patterns offer extensive views of the pathways and processes within and between cells. Single cell transcriptomics provides detailed information on gene expression within cells, cell types, subtypes and their relative proportions in organs. Functional pathways can be scalably connected to physiological functions at the cell and organ levels. Integrating experimentally obtained gene expression patterns with prior knowledge of pathway interactions enables identification of networks underlying whole cell functions such as growth, contractility, and secretion. These pathways can be computationally modeled using differential equations to simulate cell and organ physiological dynamics regulated by gene expression changes. Such computational systems can be thought of as parts of digital twins of organs. Digital twins, at the core, need computational models that represent in detail and simulate how dynamics of pathways and networks give rise to whole cell level physiological functions. Integration of transcriptomic responses and numerical simulations could simulate and predict whole cell functional outputs from transcriptomic data. We developed a computational pipeline that integrates gene expression timelines and systems of coupled differential equations to generate cell-type selective dynamical models. We tested our integrative algorithm on the eicosanoid biosynthesis network in macrophages. Converting transcriptomic changes to a dynamical model allowed us to predict dynamics of prostaglandin and thromboxane synthesis and secretion by macrophages that matched published lipidomics data obtained in the same experiments. Integration of cell-level system biology simulations with genomic and clinical data using a knowledge graph framework will allow us to create explicit predictive models that mechanistically link genomic determinants to organ function. Such integration requires a multi-domain ontological framework to connect genomic determinants to gene expression and cell pathways and functions to organ level phenotypes in healthy and diseased states. These integrated scalable models of tissues and organs as accurate digital twins predict health and disease states for precision medicine.
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Affiliation(s)
- Jens Hansen
- Department of Pharmacological Science and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Abhinav R Jain
- Department of Pharmacological Science and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Philip Nenov
- Department of Pharmacological Science and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - Peter N Robinson
- Berlin Institute of Health at Charité Rahel Hirsch Center for Translational Medicine, Berlin, Germany
| | - Ravi Iyengar
- Department of Pharmacological Science and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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3
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Dutta S, Goodrich JM, Dolinoy DC, Ruden DM. Biological Aging Acceleration Due to Environmental Exposures: An Exciting New Direction in Toxicogenomics Research. Genes (Basel) 2023; 15:16. [PMID: 38275598 PMCID: PMC10815440 DOI: 10.3390/genes15010016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024] Open
Abstract
Biological clock technologies are designed to assess the acceleration of biological age (B-age) in diverse cell types, offering a distinctive opportunity in toxicogenomic research to explore the impact of environmental stressors, social challenges, and unhealthy lifestyles on health impairment. These clocks also play a role in identifying factors that can hinder aging and promote a healthy lifestyle. Over the past decade, researchers in epigenetics have developed testing methods that predict the chronological and biological age of organisms. These methods rely on assessing DNA methylation (DNAm) levels at specific CpG sites, RNA levels, and various biomolecules across multiple cell types, tissues, and entire organisms. Commonly known as 'biological clocks' (B-clocks), these estimators hold promise for gaining deeper insights into the pathways contributing to the development of age-related disorders. They also provide a foundation for devising biomedical or social interventions to prevent, reverse, or mitigate these disorders. This review article provides a concise overview of various epigenetic clocks and explores their susceptibility to environmental stressors.
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Affiliation(s)
- Sudipta Dutta
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA;
| | - Jaclyn M. Goodrich
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA; (J.M.G.); (D.C.D.)
| | - Dana C. Dolinoy
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA; (J.M.G.); (D.C.D.)
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Douglas M. Ruden
- C. S. Mott Center for Human Health and Development, Department of Obstetrics and Gynecology, Institute of Environmental Health Sciences, Wayne State University, Detroit, MI 48202, USA
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4
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Baumann C, Zhang X, Kandasamy MK, Mei X, Chen S, Tehrani KF, Mortensen LJ, Watford W, Lall A, De La Fuente R. Acute irradiation induces a senescence-like chromatin structure in mammalian oocytes. Commun Biol 2023; 6:1258. [PMID: 38086992 PMCID: PMC10716162 DOI: 10.1038/s42003-023-05641-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
The mechanisms leading to changes in mesoscale chromatin organization during cellular aging are unknown. Here, we used transcriptional activator-like effectors, RNA-seq and superresolution analysis to determine the effects of genotoxic stress on oocyte chromatin structure. Major satellites are organized into tightly packed globular structures that coalesce into chromocenters and dynamically associate with the nucleolus. Acute irradiation significantly enhanced chromocenter mobility in transcriptionally inactive oocytes. In transcriptionally active oocytes, irradiation induced a striking unfolding of satellite chromatin fibers and enhanced the expression of transcripts required for protection from oxidative stress (Fermt1, Smg1), recovery from DNA damage (Tlk2, Rad54l) and regulation of heterochromatin assembly (Zfp296, Ski-oncogene). Non-irradiated, senescent oocytes exhibit not only high chromocenter mobility and satellite distension but also a high frequency of extra chromosomal satellite DNA. Notably, analysis of biological aging using an oocyte-specific RNA clock revealed cellular communication, posttranslational protein modifications, chromatin and histone dynamics as the top cellular processes that are dysregulated in both senescent and irradiated oocytes. Our results indicate that unfolding of heterochromatin fibers following acute genotoxic stress or cellular aging induced the formation of distended satellites and that abnormal chromatin structure together with increased chromocenter mobility leads to chromosome instability in senescent oocytes.
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Affiliation(s)
- Claudia Baumann
- Department of Physiology and Pharmacology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
- Regenerative Biosciences Center (RBC), University of Georgia, Athens, GA, USA
| | - Xiangyu Zhang
- Department of Physiology and Pharmacology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
- Regenerative Biosciences Center (RBC), University of Georgia, Athens, GA, USA
| | | | - Xiaohan Mei
- Department of Physiology and Pharmacology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
- Division of Surgical Research, University of Missouri, School of Medicine, Columbia, MO, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Shiyou Chen
- Department of Physiology and Pharmacology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
- Division of Surgical Research, University of Missouri, School of Medicine, Columbia, MO, USA
| | - Kayvan F Tehrani
- Regenerative Biosciences Center (RBC), University of Georgia, Athens, GA, USA
- School of Chemical, Materials and Biomedical Engineering, University of Georgia, Athens, GA, USA
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Luke J Mortensen
- Regenerative Biosciences Center (RBC), University of Georgia, Athens, GA, USA
- School of Chemical, Materials and Biomedical Engineering, University of Georgia, Athens, GA, USA
| | - Wendy Watford
- Department of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - Ashley Lall
- Department of Physiology and Pharmacology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
- Regenerative Biosciences Center (RBC), University of Georgia, Athens, GA, USA
| | - Rabindranath De La Fuente
- Department of Physiology and Pharmacology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA.
- Regenerative Biosciences Center (RBC), University of Georgia, Athens, GA, USA.
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Sharma K, Zhang G, Hansen J, Bjornstad P, Lee HJ, Menon R, Hejazi L, Liu JJ, Franzone A, Looker HC, Choi BY, Fernandez R, Venkatachalam MA, Kugathasan L, Sridhar VS, Natarajan L, Zhang J, Sharma VS, Kwan B, Waikar SS, Himmelfarb J, Tuttle KR, Kestenbaum B, Fuhrer T, Feldman HI, de Boer IH, Tucci FC, Sedor J, Heerspink HL, Schaub J, Otto EA, Hodgin JB, Kretzler M, Anderton CR, Alexandrov T, Cherney D, Lim SC, Nelson RG, Gelfond J, Iyengar R. Endogenous adenine mediates kidney injury in diabetic models and predicts diabetic kidney disease in patients. J Clin Invest 2023; 133:e170341. [PMID: 37616058 PMCID: PMC10575723 DOI: 10.1172/jci170341] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023] Open
Abstract
Diabetic kidney disease (DKD) can lead to end-stage kidney disease (ESKD) and mortality; however, few mechanistic biomarkers are available for high-risk patients, especially those without macroalbuminuria. Urine from participants with diabetes from the Chronic Renal Insufficiency Cohort (CRIC) study, the Singapore Study of Macro-angiopathy and Micro-vascular Reactivity in Type 2 Diabetes (SMART2D), and the American Indian Study determined whether urine adenine/creatinine ratio (UAdCR) could be a mechanistic biomarker for ESKD. ESKD and mortality were associated with the highest UAdCR tertile in the CRIC study and SMART2D. ESKD was associated with the highest UAdCR tertile in patients without macroalbuminuria in the CRIC study, SMART2D, and the American Indian study. Empagliflozin lowered UAdCR in nonmacroalbuminuric participants. Spatial metabolomics localized adenine to kidney pathology, and single-cell transcriptomics identified ribonucleoprotein biogenesis as a top pathway in proximal tubules of patients without macroalbuminuria, implicating mTOR. Adenine stimulated matrix in tubular cells via mTOR and stimulated mTOR in mouse kidneys. A specific inhibitor of adenine production was found to reduce kidney hypertrophy and kidney injury in diabetic mice. We propose that endogenous adenine may be a causative factor in DKD.
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Affiliation(s)
- Kumar Sharma
- Center for Precision Medicine and
- Division of Nephrology, Department of Medicine, University of Texas Health Science Center at San Antonio, Texas, USA
| | - Guanshi Zhang
- Center for Precision Medicine and
- Division of Nephrology, Department of Medicine, University of Texas Health Science Center at San Antonio, Texas, USA
| | - Jens Hansen
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Petter Bjornstad
- Division of Nephrology, Department of Medicine and Section of Endocrinology, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Hak Joo Lee
- Center for Precision Medicine and
- Division of Nephrology, Department of Medicine, University of Texas Health Science Center at San Antonio, Texas, USA
| | - Rajasree Menon
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Leila Hejazi
- Center for Precision Medicine and
- SygnaMap Inc., San Antonio, Texas, USA
| | - Jian-Jun Liu
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore
| | | | - Helen C. Looker
- Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona, USA
| | - Byeong Yeob Choi
- Center for Precision Medicine and
- Department of Population Health Sciences and
| | | | - Manjeri A. Venkatachalam
- Center for Precision Medicine and
- Department of Pathology, University of Texas Health Science Center at San Antonio, Texas, USA
| | - Luxcia Kugathasan
- Department of Medicine, Division of Nephrology, University Health Network, Toronto, Ontario, Canada. Department of Physiology and Cardiovascular Sciences Collaborative Specialization, University of Toronto, Toronto, Canada
| | - Vikas S. Sridhar
- Department of Medicine, Division of Nephrology, University Health Network, Toronto, Ontario, Canada. Department of Physiology and Cardiovascular Sciences Collaborative Specialization, University of Toronto, Toronto, Canada
| | - Loki Natarajan
- Herbert Wertheim School of Public Health and
- Moores Cancer Center, University of California, San Diego, La Jolla, California, USA
| | - Jing Zhang
- Moores Cancer Center, University of California, San Diego, La Jolla, California, USA
| | - Varun S. Sharma
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Brian Kwan
- Department of Health Science, California State University, Long Beach, Long Beach, California, USA
| | - Sushrut S. Waikar
- Section of Nephrology, Department of Medicine, Boston Medical Center and Boston University, Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
| | - Jonathan Himmelfarb
- Department of Medicine, Division of Nephrology, Kidney Research Institute, University of Washington, Seattle, Washington, USA
| | - Katherine R. Tuttle
- Department of Medicine, Division of Nephrology, Kidney Research Institute, University of Washington, Seattle, Washington, USA
| | - Bryan Kestenbaum
- Department of Medicine, Division of Nephrology, Kidney Research Institute, University of Washington, Seattle, Washington, USA
| | - Tobias Fuhrer
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Harold I. Feldman
- Center for Clinical Epidemiology and Biostatistics and Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
- Patient-Centered Outcomes Research Institute, Washington, DC, USA
| | - Ian H. de Boer
- Department of Medicine, Division of Nephrology, Kidney Research Institute, University of Washington, Seattle, Washington, USA
| | | | | | - Hiddo Lambers Heerspink
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, Netherlands
- The George Institute for Global Health, Sydney, Australia
| | - Jennifer Schaub
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Edgar A. Otto
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Jeffrey B. Hodgin
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Matthias Kretzler
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Christopher R. Anderton
- Center for Precision Medicine and
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - David Cherney
- Department of Medicine, Division of Nephrology, University Health Network, Toronto, Ontario, Canada. Department of Physiology and Cardiovascular Sciences Collaborative Specialization, University of Toronto, Toronto, Canada
| | - Su Chi Lim
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore
- Diabetes Center, Admiralty Medical Center, Khoo Teck Puat Hospital, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Robert G. Nelson
- Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona, USA
| | - Jonathan Gelfond
- Center for Precision Medicine and
- Department of Population Health Sciences and
| | - Ravi Iyengar
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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6
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Sharma K, Zhang G, Hansen J, Bjornstad P, Lee HJ, Menon R, Hejazi L, Liu JJ, Franzone A, Looker HC, Choi BY, Fernandez R, Venkatachalam MA, Kugathasan L, Sridhar VS, Natarajan L, Zhang J, Sharma V, Kwan B, Waikar S, Himmelfarb J, Tuttle K, Kestenbaum B, Fuhrer T, Feldman H, de Boer IH, Tucci FC, Sedor J, Heerspink HL, Schaub J, Otto E, Hodgin JB, Kretzler M, Anderton C, Alexandrov T, Cherney D, Lim SC, Nelson RG, Gelfond J, Iyengar R. Role of endogenous adenine in kidney failure and mortality with diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.31.23290681. [PMID: 37398187 PMCID: PMC10312877 DOI: 10.1101/2023.05.31.23290681] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Diabetic kidney disease (DKD) can lead to end-stage kidney disease (ESKD) and mortality, however, few mechanistic biomarkers are available for high risk patients, especially those without macroalbuminuria. Urine from participants with diabetes from Chronic Renal Insufficiency Cohort (CRIC), Singapore Study of Macro-Angiopathy and Reactivity in Type 2 Diabetes (SMART2D), and the Pima Indian Study determined if urine adenine/creatinine ratio (UAdCR) could be a mechanistic biomarker for ESKD. ESKD and mortality were associated with the highest UAdCR tertile in CRIC (HR 1.57, 1.18, 2.10) and SMART2D (HR 1.77, 1.00, 3.12). ESKD was associated with the highest UAdCR tertile in patients without macroalbuminuria in CRIC (HR 2.36, 1.26, 4.39), SMART2D (HR 2.39, 1.08, 5.29), and Pima Indian study (HR 4.57, CI 1.37-13.34). Empagliflozin lowered UAdCR in non-macroalbuminuric participants. Spatial metabolomics localized adenine to kidney pathology and transcriptomics identified ribonucleoprotein biogenesis as a top pathway in proximal tubules of patients without macroalbuminuria, implicating mammalian target of rapamycin (mTOR). Adenine stimulated matrix in tubular cells via mTOR and stimulated mTOR in mouse kidneys. A specific inhibitor of adenine production was found to reduce kidney hypertrophy and kidney injury in diabetic mice. We propose that endogenous adenine may be a causative factor in DKD.
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7
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Jung S, Arcos Hodar J, Del Sol A. Measuring biological age using a functionally interpretable multi-tissue RNA clock. Aging Cell 2023; 22:e13799. [PMID: 36929664 DOI: 10.1111/acel.13799] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 01/16/2023] [Accepted: 02/03/2023] [Indexed: 03/18/2023] Open
Abstract
The quantification of the biological age of cells yields great promises for accelerating the discovery of novel rejuvenation strategies. Here, we present MultiTIMER, the first multi-tissue aging clock that measures the biological, rather than chronological, age of cells from their transcriptional profiles by evaluating key cellular processes. We applied MultiTIMER to more than 70,000 transcriptional profiles and demonstrate that it accurately responds to cellular stressors and known interventions while informing about dysregulated cellular functions.
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Affiliation(s)
- Sascha Jung
- Computational Biology Group, CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Bizkaia Technology Park, Derio, Spain
| | - Javier Arcos Hodar
- Computational Biology Group, CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Bizkaia Technology Park, Derio, Spain
| | - Antonio Del Sol
- Computational Biology Group, CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Bizkaia Technology Park, Derio, Spain.,Computational Biology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.,Ikerbasque, Basque Foundation for Science, Bilbao, Spain
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8
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Dave J, Raad N, Mittal N, Zhang L, Fargnoli A, Oh JG, Savoia ME, Hansen J, Fava M, Yin X, Theofilatos K, Ceholski D, Kohlbrenner E, Jeong D, Wills L, Nonnenmacher M, Haghighi K, Costa KD, Turnbull IC, Mayr M, Cai CL, Kranias EG, Akar FG, Hajjar RJ, Stillitano F. Gene editing reverses arrhythmia susceptibility in humanized PLN-R14del mice: modelling a European cardiomyopathy with global impact. Cardiovasc Res 2022; 118:3140-3150. [PMID: 35191471 PMCID: PMC9732517 DOI: 10.1093/cvr/cvac021] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 02/18/2022] [Indexed: 12/16/2022] Open
Abstract
AIMS A mutation in the phospholamban (PLN) gene, leading to deletion of Arg14 (R14del), has been associated with malignant arrhythmias and ventricular dilation. Identifying pre-symptomatic carriers with vulnerable myocardium is crucial because arrhythmia can result in sudden cardiac death, especially in young adults with PLN-R14del mutation. This study aimed at assessing the efficiency and efficacy of in vivo genome editing, using CRISPR/Cas9 and a cardiotropic adeno-associated virus-9 (AAV9), in improving cardiac function in young adult mice expressing the human PLN-R14del. METHODS AND RESULTS Humanized mice were generated expressing human wild-type (hPLN-WT) or mutant (hPLN-R14del) PLN in the heterozygous state, mimicking human carriers. Cardiac magnetic resonance imaging at 12 weeks of age showed bi-ventricular dilation and increased stroke volume in mutant vs. WT mice, with no deficit in ejection fraction or cardiac output. Challenge of ex vivo hearts with isoproterenol and rapid pacing unmasked higher propensity for sustained ventricular tachycardia (VT) in hPLN-R14del relative to hPLN-WT. Specifically, the VT threshold was significantly reduced (20.3 ± 1.2 Hz in hPLN-R14del vs. 25.7 ± 1.3 Hz in WT, P < 0.01) reflecting higher arrhythmia burden. To inactivate the R14del allele, mice were tail-vein-injected with AAV9.CRISPR/Cas9/gRNA or AAV9 empty capsid (controls). CRISPR-Cas9 efficiency was evaluated by droplet digital polymerase chain reaction and NGS-based amplicon sequencing. In vivo gene editing significantly reduced end-diastolic and stroke volumes in hPLN-R14del CRISPR-treated mice compared to controls. Susceptibility to VT was also reduced, as the VT threshold was significantly increased relative to controls (30.9 ± 2.3 Hz vs. 21.3 ± 1.5 Hz; P < 0.01). CONCLUSIONS This study is the first to show that disruption of hPLN-R14del allele by AAV9-CRISPR/Cas9 improves cardiac function and reduces VT susceptibility in humanized PLN-R14del mice, offering preclinical evidence for translatable approaches to therapeutically suppress the arrhythmogenic phenotype in human patients with PLN-R14del disease.
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Affiliation(s)
- Jaydev Dave
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nour Raad
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nishka Mittal
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lu Zhang
- Department of Pediatrics, Riley Heart Research Center, Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Anthony Fargnoli
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jae Gyun Oh
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Jens Hansen
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marika Fava
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiaoke Yin
- King’s British Heart Foundation Centre, King’s College London, London, UK
| | | | - Delaine Ceholski
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Erik Kohlbrenner
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dongtak Jeong
- Department of Molecular & Life Science, College of Science and Convergence Technology, Hanyang University-ERICA, Ansan-si, South Korea
| | - Lauren Wills
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mathieu Nonnenmacher
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kobra Haghighi
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Kevin D Costa
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Irene C Turnbull
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manuel Mayr
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- King’s British Heart Foundation Centre, King’s College London, London, UK
| | - Chen-Leng Cai
- Department of Pediatrics, Riley Heart Research Center, Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Evangelia G Kranias
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Fadi G Akar
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Francesca Stillitano
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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9
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Hansen J, Siddiq MM, Yadaw AS, Tolentino RE, Rabinovich V, Jayaraman G, Jain MR, Liu T, Li H, Xiong Y, Goldfarb J, Iyengar R. Whole cell response to receptor stimulation involves many deep and distributed subcellular biochemical processes. J Biol Chem 2022; 298:102325. [PMID: 35926710 PMCID: PMC9520007 DOI: 10.1016/j.jbc.2022.102325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 11/28/2022] Open
Abstract
Neurite outgrowth is an integrated whole cell response triggered by the cannabinoid-1 receptor. We sought to identify the many different biochemical pathways that contribute to this whole cell response. To understand underlying mechanisms, we identified subcellular processes (SCPs) composed of one or more biochemical pathways and their interactions required for this response. Differentially expressed genes and proteins were obtained from bulk transcriptomics and proteomic analysis of extracts from cells stimulated with a cannabinoid-1 receptor agonist. We used these differentially expressed genes and proteins to build networks of interacting SCPs by combining the expression data with prior pathway knowledge. From these SCP networks, we identified additional genes that when ablated, experimentally validated the SCP involvement in neurite outgrowth. Our experiments and informatics modeling allowed us to identify diverse SCPs such as those involved in pyrimidine metabolism, lipid biosynthesis, and mRNA splicing and stability, along with more predictable SCPs such as membrane vesicle transport and microtubule dynamics. We find that SCPs required for neurite outgrowth are widely distributed among many biochemical pathways required for constitutive cellular functions, several of which are termed ‘deep’, since they are distal to signaling pathways and the key SCPs directly involved in extension of the neurite. In contrast, ‘proximal’ SCPs are involved in microtubule growth and membrane vesicle transport dynamics required for neurite outgrowth. From these bioinformatics and dynamical models based on experimental data, we conclude that receptor-mediated regulation of subcellular functions for neurite outgrowth is both distributed, that is, involves many different biochemical pathways, and deep.
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Affiliation(s)
- Jens Hansen
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Mustafa M Siddiq
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Arjun Singh Yadaw
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Rosa E Tolentino
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Vera Rabinovich
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Gomathi Jayaraman
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Mohit Raja Jain
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers University, New Jersey Medical School, Newark, NY, 07103, United States
| | - Tong Liu
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers University, New Jersey Medical School, Newark, NY, 07103, United States
| | - Hong Li
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers University, New Jersey Medical School, Newark, NY, 07103, United States
| | - Yuguang Xiong
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Joseph Goldfarb
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Ravi Iyengar
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.
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10
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Hansen J, Sealfon R, Menon R, Eadon MT, Lake BB, Steck B, Anjani K, Parikh S, Sigdel TK, Zhang G, Velickovic D, Barwinska D, Alexandrov T, Dobi D, Rashmi P, Otto EA, Rivera M, Rose MP, Anderton CR, Shapiro JP, Pamreddy A, Winfree S, Xiong Y, He Y, de Boer IH, Hodgin JB, Barisoni L, Naik AS, Sharma K, Sarwal MM, Zhang K, Himmelfarb J, Rovin B, El-Achkar TM, Laszik Z, He JC, Dagher PC, Valerius MT, Jain S, Satlin LM, Troyanskaya OG, Kretzler M, Iyengar R, Azeloglu EU. A reference tissue atlas for the human kidney. SCIENCE ADVANCES 2022; 8:eabn4965. [PMID: 35675394 PMCID: PMC9176741 DOI: 10.1126/sciadv.abn4965] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 04/20/2022] [Indexed: 05/08/2023]
Abstract
Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.
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Affiliation(s)
- Jens Hansen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rachel Sealfon
- Princeton University, Princeton, NJ, USA
- Flatiron Institute, New York, NY, USA
| | - Rajasree Menon
- University of Michigan School of Medicine, Ann Arbor, MI, USA
| | | | - Blue B. Lake
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Becky Steck
- University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Kavya Anjani
- University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Samir Parikh
- Ohio State University College of Medicine, Columbus, OH, USA
| | - Tara K. Sigdel
- University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Guanshi Zhang
- University of Texas–Health San Antonio School of Medicine, San Antonio, TX, USA
| | | | - Daria Barwinska
- Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Dejan Dobi
- University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Priyanka Rashmi
- University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Edgar A. Otto
- University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Miguel Rivera
- University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Michael P. Rose
- University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Christopher R. Anderton
- University of Texas–Health San Antonio School of Medicine, San Antonio, TX, USA
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - John P. Shapiro
- Ohio State University College of Medicine, Columbus, OH, USA
| | - Annapurna Pamreddy
- University of Texas–Health San Antonio School of Medicine, San Antonio, TX, USA
| | - Seth Winfree
- Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yuguang Xiong
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yongqun He
- University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Ian H. de Boer
- Schools of Medicine and Public Health, University of Washington, Seattle, WA, USA
| | | | | | - Abhijit S. Naik
- University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Kumar Sharma
- University of Texas–Health San Antonio School of Medicine, San Antonio, TX, USA
| | - Minnie M. Sarwal
- University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Jonathan Himmelfarb
- Schools of Medicine and Public Health, University of Washington, Seattle, WA, USA
| | - Brad Rovin
- Ohio State University College of Medicine, Columbus, OH, USA
| | | | - Zoltan Laszik
- University of California San Francisco School of Medicine, San Francisco, CA, USA
| | | | | | - M. Todd Valerius
- Brigham and Women’s Hospital, Harvard Medical School, Cambridge, MA, USA
| | - Sanjay Jain
- Washington University in Saint Louis School of Medicine, St. Louis, MS, USA
| | - Lisa M. Satlin
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Olga G. Troyanskaya
- Princeton University, Princeton, NJ, USA
- Flatiron Institute, New York, NY, USA
| | | | - Ravi Iyengar
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Kidney Precision Medicine Project
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Princeton University, Princeton, NJ, USA
- Flatiron Institute, New York, NY, USA
- University of Michigan School of Medicine, Ann Arbor, MI, USA
- Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- University of California San Francisco School of Medicine, San Francisco, CA, USA
- Ohio State University College of Medicine, Columbus, OH, USA
- University of Texas–Health San Antonio School of Medicine, San Antonio, TX, USA
- Pacific Northwest National Laboratory, Richland, WA, USA
- European Molecular Biology Laboratory, Heidelberg, Germany
- Schools of Medicine and Public Health, University of Washington, Seattle, WA, USA
- Duke University School of Medicine, Durham, NC, USA
- Brigham and Women’s Hospital, Harvard Medical School, Cambridge, MA, USA
- Washington University in Saint Louis School of Medicine, St. Louis, MS, USA
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11
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Mehrpooya A, Saberi-Movahed F, Azizizadeh N, Rezaei-Ravari M, Saberi-Movahed F, Eftekhari M, Tavassoly I. High dimensionality reduction by matrix factorization for systems pharmacology. Brief Bioinform 2022; 23:bbab410. [PMID: 34891155 PMCID: PMC8898012 DOI: 10.1093/bib/bbab410] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/20/2021] [Accepted: 09/07/2021] [Indexed: 12/13/2022] Open
Abstract
The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other MF methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a tyrosine kinase inhibitor treatment response in the Cancer Cell Line Encyclopedia.
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Affiliation(s)
- Adel Mehrpooya
- School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farid Saberi-Movahed
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
| | - Najmeh Azizizadeh
- Department of Applied Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Iran
| | - Mohammad Rezaei-Ravari
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | | | - Mahdi Eftekhari
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Iman Tavassoly
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY10029, USA
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12
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Wooten DJ, Gebru M, Wang HG, Albert R. Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets. J Pers Med 2021; 11:jpm11030193. [PMID: 33799721 PMCID: PMC7998618 DOI: 10.3390/jpm11030193] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/05/2021] [Accepted: 03/07/2021] [Indexed: 02/06/2023] Open
Abstract
FLT3-mutant acute myeloid leukemia (AML) is an aggressive form of leukemia with poor prognosis. Treatment with FLT3 inhibitors frequently produces a clinical response, but the disease nevertheless often recurs. Recent studies have revealed system-wide gene expression changes in FLT3-mutant AML cell lines in response to drug treatment. Here we sought a systems-level understanding of how these cells mediate these drug-induced changes. Using RNAseq data from AML cells with an internal tandem duplication FLT3 mutation (FLT3-ITD) under six drug treatment conditions including quizartinib and dexamethasone, we identified seven distinct gene programs representing diverse biological processes involved in AML drug-induced changes. Based on the literature knowledge about genes from these modules, along with public gene regulatory network databases, we constructed a network of FLT3-ITD AML. Applying the BooleaBayes algorithm to this network and the RNAseq data, we created a probabilistic, data-driven dynamical model of acquired resistance to these drugs. Analysis of this model reveals several interventions that may disrupt targeted parts of the system-wide drug response. We anticipate co-targeting these points may result in synergistic treatments that can overcome resistance and prevent eventual recurrence.
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Affiliation(s)
- David J. Wooten
- Department of Physics, Pennsylvania State University, University Park, PA 16802, USA;
| | - Melat Gebru
- Department of Pediatrics, Penn State College of Medicine, Hershey, PA 17033, USA; (M.G.); (H.-G.W.)
| | - Hong-Gang Wang
- Department of Pediatrics, Penn State College of Medicine, Hershey, PA 17033, USA; (M.G.); (H.-G.W.)
| | - Réka Albert
- Department of Physics, Pennsylvania State University, University Park, PA 16802, USA;
- Correspondence:
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13
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Ong E, Wang LL, Schaub J, O'Toole JF, Steck B, Rosenberg AZ, Dowd F, Hansen J, Barisoni L, Jain S, de Boer IH, Valerius MT, Waikar SS, Park C, Crawford DC, Alexandrov T, Anderton CR, Stoeckert C, Weng C, Diehl AD, Mungall CJ, Haendel M, Robinson PN, Himmelfarb J, Iyengar R, Kretzler M, Mooney S, He Y. Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project. Nat Rev Nephrol 2020; 16:686-696. [PMID: 32939051 PMCID: PMC8012202 DOI: 10.1038/s41581-020-00335-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/24/2020] [Indexed: 12/29/2022]
Abstract
An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient's phenotype. The development of two new ontologies - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation - will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.
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Affiliation(s)
- Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Lucy L Wang
- Allen Institute for Artificial Intelligence, Seattle, WA, USA
| | - Jennifer Schaub
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - John F O'Toole
- Department of Nephrology and Hypertension, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Becky Steck
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Avi Z Rosenberg
- Department of Pathology, Johns Hopkins University, Baltimore, MD, USA
| | - Frederick Dowd
- UW Medicine Research IT, University of Washington, Seattle, WA, USA
| | - Jens Hansen
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura Barisoni
- Division of AI/Computational Pathology, Department of Pathology, and Division of Nephrology, Department of Medicine, Duke University, Durham, NC, USA
| | - Sanjay Jain
- Division of Nephrology, School of Medicine, Washington University in St. Louis, St Louis, MO, USA
| | - Ian H de Boer
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - M Todd Valerius
- Division of Renal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Sushrut S Waikar
- Section of Nephrology, Boston University Medical Center, Boston, MA, USA
| | - Christopher Park
- Kidney Research Institute, University of Washington, Seattle, WA, USA
| | - Dana C Crawford
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Cleveland, OH, USA
| | - Theodore Alexandrov
- Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | | | - Christian Stoeckert
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania Philadelphia, Philadelphia, PA, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Alexander D Diehl
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Christopher J Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Melissa Haendel
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Jonathan Himmelfarb
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA, USA
- Kidney Research Institute, University of Washington, Seattle, WA, USA
| | - Ravi Iyengar
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthias Kretzler
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Sean Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
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14
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Dorvash M, Farahmandnia M, Tavassoly I. A Systems Biology Roadmap to Decode mTOR Control System in Cancer. Interdiscip Sci 2019; 12:1-11. [PMID: 31531812 DOI: 10.1007/s12539-019-00347-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/26/2019] [Accepted: 09/04/2019] [Indexed: 12/23/2022]
Abstract
Mechanistic target of rapamycin (mTOR) is a critical protein in the regulation of cell fate decision making, especially in cancer cells. mTOR acts as a signal integrator and is one of the main elements of interactions among the pivotal cellular processes such as cell death, autophagy, metabolic reprogramming, cell growth, and cell cycle. The temporal control of these processes is essential for the cellular homeostasis and dysregulation of mTOR signaling pathway results in different phenotypes, including aging, oncogenesis, cell survival, cell growth, senescence, quiescence, and cell death. In this paper, we have proposed a systems biology roadmap to study mTOR control system, which introduces the theoretical and experimental modalities to decode temporal and dynamical characteristics of mTOR signaling in cancer.
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Affiliation(s)
- Mohammadreza Dorvash
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Cell and Molecular Medicine Student Research Group, Faculty of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Farahmandnia
- Cell and Molecular Medicine Student Research Group, Faculty of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Iman Tavassoly
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA.
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15
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Tavassoly I, Hu Y, Zhao S, Mariottini C, Boran A, Chen Y, Li L, Tolentino RE, Jayaraman G, Goldfarb J, Gallo J, Iyengar R. Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses. Mol Oncol 2019; 13:1725-1743. [PMID: 31116490 PMCID: PMC6670022 DOI: 10.1002/1878-0261.12521] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The ability to predict responsiveness to drugs in individual patients is limited. We hypothesized that integrating molecular information from databases would yield predictions that could be experimentally tested to develop transcriptomic signatures for specific drugs. We analyzed lung adenocarcinoma patient data from The Cancer Genome Atlas and identified a subset of patients in which xanthine dehydrogenase (XDH) expression correlated with decreased survival. We tested allopurinol, an FDA‐approved drug that inhibits XDH, on human non‐small‐cell lung cancer (NSCLC) cell lines obtained from the Broad Institute Cancer Cell Line Encyclopedia and identified sensitive and resistant cell lines. We utilized the transcriptomic profiles of these cell lines to identify six‐gene signatures for allopurinol‐sensitive and allopurinol‐resistant cell lines. Transcriptomic networks identified JAK2 as an additional target in allopurinol‐resistant lines. Treatment of resistant cell lines with allopurinol and CEP‐33779 (a JAK2 inhibitor) resulted in cell death. The effectiveness of allopurinol alone or allopurinol and CEP‐33779 was verified in vivo using tumor formation in NCR‐nude mice. We utilized the six‐gene signatures to predict five additional allopurinol‐sensitive NSCLC cell lines and four allopurinol‐resistant cell lines susceptible to combination therapy. We searched the transcriptomic data from a library of patient‐derived NSCLC tumors from the Jackson Laboratory to identify tumors that would be predicted to be sensitive to allopurinol or allopurinol + CEP‐33779 treatment. Patient‐derived tumors showed the predicted drug sensitivity in vivo. These data indicate that we can use integrated molecular information from cancer databases to predict drug responsiveness in individual patients and thus enable precision medicine.
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Affiliation(s)
- Iman Tavassoly
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuan Hu
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Clinical Pharmacology and Pharmacy, Chinese PLA General Hospital, Beijing, China
| | - Shan Zhao
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chiara Mariottini
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aislyn Boran
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yibang Chen
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lisa Li
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rosa E Tolentino
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gomathi Jayaraman
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph Goldfarb
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - James Gallo
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ravi Iyengar
- Department of Pharmacological Sciences, Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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16
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Yadaw AS, Siddiq MM, Rabinovich V, Tolentino R, Hansen J, Iyengar R. Dynamic balance between vesicle transport and microtubule growth enables neurite outgrowth. PLoS Comput Biol 2019; 15:e1006877. [PMID: 31042702 PMCID: PMC6546251 DOI: 10.1371/journal.pcbi.1006877] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 06/03/2019] [Accepted: 02/18/2019] [Indexed: 11/18/2022] Open
Abstract
Whole cell responses involve multiple subcellular processes (SCPs). To understand how balance between SCPs controls the dynamics of whole cell responses we studied neurite outgrowth in rat primary cortical neurons in culture. We used a combination of dynamical models and experiments to understand the conditions that permitted growth at a specified velocity and when aberrant growth could lead to the formation of dystrophic bulbs. We hypothesized that dystrophic bulb formation is due to quantitative imbalances between SCPs. Simulations predict redundancies between lower level sibling SCPs within each type of high level SCP. In contrast, higher level SCPs, such as vesicle transport and exocytosis or microtubule growth characteristic of each type need to be strictly coordinated with each other and imbalances result in stalling of neurite outgrowth. From these simulations, we predicted the effect of changing the activities of SCPs involved in vesicle exocytosis or microtubule growth could lead to formation of dystrophic bulbs. siRNA ablation experiments verified these predictions. We conclude that whole cell dynamics requires balance between the higher-level SCPs involved and imbalances can terminate whole cell responses such as neurite outgrowth. Mechanisms that cause a change of state of a cell arise from unique patterns of interactions between multiple subcellular processes (SCPs). Neurite outgrowth (NOG) is such a change of cell state where a neuron puts out a long process that eventually becomes the axon. We used a top-down based approach to mathematically model interactions between SCPs involved in NOG. These include membrane production at the cell body, membrane delivery from the cell body to the neurite tip and microtubule growth within the neurite. Our analyses show how the different SCPs interact with each other to enable NOG at a given velocity under physiological conditions. This approach is different from the commonly used bottom-up approaches that focus on predicting cell functions based on the activity of molecular interaction networks. Our simulations predict that lower-level sibling SCPs (e.g. vesicle tethering at and vesicle fusion with the plasma membrane) within a group can compensate for each other under physiological conditions, while such simple relationships do not exist between higher level SCPs (e.g. vesicle exocytosis and vesicle transport along microtubules). We predicted that imbalances of activities between higher-level SCPs induce dystrophic bulbs (a pathological response) and validated these predictions via siRNA ablation experiments.
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Affiliation(s)
- Arjun Singh Yadaw
- Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Mustafa M. Siddiq
- Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Vera Rabinovich
- Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Rosa Tolentino
- Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Jens Hansen
- Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- * E-mail: (JH); (RI)
| | - Ravi Iyengar
- Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- * E-mail: (JH); (RI)
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17
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He Y, Duncan WD, Cooper DJ, Hansen J, Iyengar R, Ong E, Walker K, Tibi O, Smith S, Serra LM, Zheng J, Sarntivijai S, Schürer S, O'Shea KS, Diehl AD. OSCI: standardized stem cell ontology representation and use cases for stem cell investigation. BMC Bioinformatics 2019; 20:180. [PMID: 31272389 PMCID: PMC6509805 DOI: 10.1186/s12859-019-2723-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Background Stem cells and stem cell lines are widely used in biomedical research. The Cell Ontology (CL) and Cell Line Ontology (CLO) are two community-based OBO Foundry ontologies in the domains of in vivo cells and in vitro cell line cells, respectively. Results To support standardized stem cell investigations, we have developed an Ontology for Stem Cell Investigations (OSCI). OSCI imports stem cell and cell line terms from CL and CLO, and investigation-related terms from existing ontologies. A novel focus of OSCI is its application in representing metadata types associated with various stem cell investigations. We also applied OSCI to systematically categorize experimental variables in an induced pluripotent stem cell line cell study related to bipolar disorder. In addition, we used a semi-automated literature mining approach to identify over 200 stem cell gene markers. The relations between these genes and stem cells are modeled and represented in OSCI. Conclusions OSCI standardizes stem cells found in vivo and in vitro and in various stem cell investigation processes and entities. The presented use cases demonstrate the utility of OSCI in iPSC studies and literature mining related to bipolar disorder.
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Affiliation(s)
- Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, USA.
| | | | | | - Jens Hansen
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,SBCNY, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ravi Iyengar
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,SBCNY, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edison Ong
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Kendal Walker
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Omar Tibi
- John Hopkins Unversity, Baltimore, MD, USA
| | | | - Lucas M Serra
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Jie Zheng
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - K Sue O'Shea
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Alexander D Diehl
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
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18
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Systems biology primer: the basic methods and approaches. Essays Biochem 2018; 62:487-500. [PMID: 30287586 DOI: 10.1042/ebc20180003] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 08/22/2018] [Accepted: 08/24/2018] [Indexed: 12/16/2022]
Abstract
Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overview of current experimental and computational methods used in systems biology.
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