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Khalilimeybodi A, Saucerman JJ, Rangamani P. Modeling cardiomyocyte signaling and metabolism predicts genotype-to-phenotype mechanisms in hypertrophic cardiomyopathy. Comput Biol Med 2024; 175:108499. [PMID: 38677172 PMCID: PMC11175993 DOI: 10.1016/j.compbiomed.2024.108499] [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: 01/20/2024] [Revised: 04/17/2024] [Accepted: 04/21/2024] [Indexed: 04/29/2024]
Abstract
Familial hypertrophic cardiomyopathy (HCM) is a significant precursor of heart failure and sudden cardiac death, primarily caused by mutations in sarcomeric and structural proteins. Despite the extensive research on the HCM genotype, the complex and context-specific nature of many signaling and metabolic pathways linking the HCM genotype to phenotype has hindered therapeutic advancements for patients. Here, we have developed a computational model of HCM encompassing cardiomyocyte signaling and metabolic networks and their associated interactions. Utilizing a stochastic logic-based ODE approach, we linked cardiomyocyte signaling to the metabolic network through a gene regulatory network and post-translational modifications. We validated the model against published data on activities of signaling species in the HCM context and transcriptomes of two HCM mouse models (i.e., R403Q-αMyHC and R92W-TnT). Our model predicts that HCM mutation induces changes in metabolic functions such as ATP synthase deficiency and a transition from fatty acids to carbohydrate metabolism. The model indicated major shifts in glutamine-related metabolism and increased apoptosis after HCM-induced ATP synthase deficiency. We predicted that the transcription factors STAT, SRF, GATA4, TP53, and FoxO are the key regulators of cardiomyocyte hypertrophy and apoptosis in HCM in alignment with experiments. Moreover, we identified shared (e.g., activation of PGC1α by AMPK, and FHL1 by titin) and context-specific mechanisms (e.g., regulation of Ca2+ sensitivity by titin in HCM patients) that may control genotype-to-phenotype transition in HCM across different species or mutations. We also predicted potential combination drug targets for HCM (e.g., mavacamten plus ROS inhibitors) preventing or reversing HCM phenotype (i.e., hypertrophic growth, apoptosis, and metabolic remodeling) in cardiomyocytes. This study provides new insights into mechanisms linking genotype to phenotype in familial hypertrophic cardiomyopathy and offers a framework for assessing new treatments and exploring variations in HCM experimental models.
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Affiliation(s)
- A Khalilimeybodi
- Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla CA 92093, United States of America
| | - Jeffrey J Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America; Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, United States of America
| | - P Rangamani
- Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla CA 92093, United States of America.
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Ginley-Hidinger M, Abewe H, Osborne K, Richey A, Kitchen N, Mortenson KL, Wissink EM, Lis J, Zhang X, Gertz J. Cis-regulatory control of transcriptional timing and noise in response to estrogen. CELL GENOMICS 2024; 4:100542. [PMID: 38663407 PMCID: PMC11099348 DOI: 10.1016/j.xgen.2024.100542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 10/26/2023] [Accepted: 03/27/2024] [Indexed: 05/07/2024]
Abstract
Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single-cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We found that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.
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Affiliation(s)
- Matthew Ginley-Hidinger
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | - Hosiana Abewe
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Kyle Osborne
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Alexandra Richey
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | - Noel Kitchen
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Katelyn L Mortenson
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Erin M Wissink
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - John Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Xiaoyang Zhang
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Jason Gertz
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA.
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3
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Ginley-Hidinger M, Abewe H, Osborne K, Richey A, Kitchen N, Mortenson KL, Wissink EM, Lis J, Zhang X, Gertz J. Cis-regulatory control of transcriptional timing and noise in response to estrogen. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.14.532457. [PMID: 36993565 PMCID: PMC10054948 DOI: 10.1101/2023.03.14.532457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We find that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.
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Affiliation(s)
- Matthew Ginley-Hidinger
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | - Hosiana Abewe
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Kyle Osborne
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Alexandra Richey
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | - Noel Kitchen
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Katelyn L. Mortenson
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Erin M. Wissink
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - John Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Xiaoyang Zhang
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Jason Gertz
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
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Kulkarni P, Salgia R, Rangarajan G. Intrinsically disordered proteins and conformational noise: The hypothesis a decade later. iScience 2023; 26:107109. [PMID: 37408690 PMCID: PMC10319216 DOI: 10.1016/j.isci.2023.107109] [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] [Indexed: 07/07/2023] Open
Abstract
Phenotypic plasticity is the ability of individual genotypes to produce different phenotypes in response to environmental perturbations. We previously postulated how conformational noise emanating from conformational dynamics of intrinsically disordered proteins (IDPs) which is distinct from transcriptional noise, can contribute to phenotypic switching by rewiring the cellular protein interaction network. Since most transcription factors are IDPs, we posited that conformational noise is an integral component of transcriptional noise implying that IDPs may amplify total noise in the system either stochastically or in response to environmental changes. Here, we review progress in elucidating the details of the hypothesis. We highlight empirical evidence supporting the hypothesis, discuss conceptual advances that underscore its fundamental importance and implications, and identify areas for future investigations.
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Affiliation(s)
- Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
- Department of Systems Biology, City of Hope National Medical Center, Duarte, CA, USA
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Govindan Rangarajan
- Department of Mathematics, Indian Institute of Science, Bangalore 560012, India
- Center for Neuroscience, Indian Institute of Science, Bangalore 560012, India
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Biswas S, Clawson W, Levin M. Learning in Transcriptional Network Models: Computational Discovery of Pathway-Level Memory and Effective Interventions. Int J Mol Sci 2022; 24:ijms24010285. [PMID: 36613729 PMCID: PMC9820177 DOI: 10.3390/ijms24010285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/23/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Trainability, in any substrate, refers to the ability to change future behavior based on past experiences. An understanding of such capacity within biological cells and tissues would enable a particularly powerful set of methods for prediction and control of their behavior through specific patterns of stimuli. This top-down mode of control (as an alternative to bottom-up modification of hardware) has been extensively exploited by computer science and the behavioral sciences; in biology however, it is usually reserved for organism-level behavior in animals with brains, such as training animals towards a desired response. Exciting work in the field of basal cognition has begun to reveal degrees and forms of unconventional memory in non-neural tissues and even in subcellular biochemical dynamics. Here, we characterize biological gene regulatory circuit models and protein pathways and find them capable of several different kinds of memory. We extend prior results on learning in binary transcriptional networks to continuous models and identify specific interventions (regimes of stimulation, as opposed to network rewiring) that abolish undesirable network behavior such as drug pharmacoresistance and drug sensitization. We also explore the stability of created memories by assessing their long-term behavior and find that most memories do not decay over long time periods. Additionally, we find that the memory properties are quite robust to noise; surprisingly, in many cases noise actually increases memory potential. We examine various network properties associated with these behaviors and find that no one network property is indicative of memory. Random networks do not show similar memory behavior as models of biological processes, indicating that generic network dynamics are not solely responsible for trainability. Rational control of dynamic pathway function using stimuli derived from computational models opens the door to empirical studies of proto-cognitive capacities in unconventional embodiments and suggests numerous possible applications in biomedicine, where behavior shaping of pathway responses stand as a potential alternative to gene therapy.
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Affiliation(s)
- Surama Biswas
- Allen Discovery Center, Tufts University, Medford, MA 02155, USA
- Department of Computer Science & Engineering and Information Technology, Meghnad Saha Institute of Technology, Kolkata 700150, India
| | - Wesley Clawson
- Allen Discovery Center, Tufts University, Medford, MA 02155, USA
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA 02155, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
- Correspondence: ; Tel.: +1-617-627-6161
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Kulkarni P, Leite VBP, Roy S, Bhattacharyya S, Mohanty A, Achuthan S, Singh D, Appadurai R, Rangarajan G, Weninger K, Orban J, Srivastava A, Jolly MK, Onuchic JN, Uversky VN, Salgia R. Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma. BIOPHYSICS REVIEWS 2022; 3:011306. [PMID: 38505224 PMCID: PMC10903413 DOI: 10.1063/5.0080512] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 02/17/2022] [Indexed: 03/21/2024]
Abstract
Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.
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Affiliation(s)
- Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California 91010, USA
| | - Vitor B. P. Leite
- Departamento de Física, Instituto de Biociências, Letras e Ciências Exatas, Universidade Estadual Paulista (UNESP), São José do Rio Preto, São Paulo 15054-000, Brazil
| | - Susmita Roy
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, India
| | - Supriyo Bhattacharyya
- Translational Bioinformatics, Center for Informatics, Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, California 91010, USA
| | - Atish Mohanty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California 91010, USA
| | - Srisairam Achuthan
- Center for Informatics, Division of Research Informatics, City of Hope National Medical Center, Duarte, California 91010, USA
| | - Divyoj Singh
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Rajeswari Appadurai
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Govindan Rangarajan
- Department of Mathematics, Indian Institute of Science, Bangalore 560012, India
| | - Keith Weninger
- Department of Physics, North Carolina State University, Raleigh, North Carolina 27695, USA
| | | | - Anand Srivastava
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Mohit Kumar Jolly
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Jose N. Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005-1892, USA
| | | | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California 91010, USA
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Gondal MN, Chaudhary SU. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Front Oncol 2021; 11:712505. [PMID: 34900668 PMCID: PMC8652070 DOI: 10.3389/fonc.2021.712505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/26/2021] [Indexed: 12/19/2022] Open
Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
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Affiliation(s)
- Mahnoor Naseer Gondal
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
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Arici MK, Tuncbag N. Performance Assessment of the Network Reconstruction Approaches on Various Interactomes. Front Mol Biosci 2021; 8:666705. [PMID: 34676243 PMCID: PMC8523993 DOI: 10.3389/fmolb.2021.666705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 07/14/2021] [Indexed: 01/04/2023] Open
Abstract
Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.
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Affiliation(s)
- M Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.,Foot and Mouth Diseases Institute, Ministry of Agriculture and Forestry, Ankara, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, Turkey.,School of Medicine, Koc University, Istanbul, Turkey
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