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Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Mol Biotechnol 2024:10.1007/s12033-024-01133-6. [PMID: 38565775 DOI: 10.1007/s12033-024-01133-6] [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: 12/27/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
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Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Suzanna Abraham
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - K S Mukunthan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
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2
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Arora G, Banerjee M, Langthasa J, Bhat R, Chatterjee S. Targeting metabolic fluxes reverts metastatic transitions in ovarian cancer. iScience 2023; 26:108081. [PMID: 37876796 PMCID: PMC10590820 DOI: 10.1016/j.isci.2023.108081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/05/2023] [Accepted: 09/25/2023] [Indexed: 10/26/2023] Open
Abstract
The formation of spheroids during epithelial ovarian cancer progression is correlated with peritoneal metastasis, disease recurrence, and poor prognosis. Although metastasis has been demonstrated to be driven by metabolic changes in transformed cells, mechanistic associations between metabolism and phenotypic transitions remain ill-explored. We performed quantitative proteomics to identify protein signatures associated with three distinct phenotypic morphologies (2D monolayers and two geometrically distinct three-dimensional spheroidal states) of the high-grade serous ovarian cancer line OVCAR-3. We obtained disease-driving phenotype-specific metabolic reaction modules and elucidated gene knockout strategies to reduce metabolic alterations that could drive phenotypic transitions. Exploring the DrugBank database, we identified and evaluated drugs that could impair such transitions and, hence, cancer progression. Finally, we experimentally validated our predictions by confirming the ability of one of our predicted drugs, the neuraminidase inhibitor oseltamivir, to inhibit spheroidogenesis in three ovarian cancer cell lines without any cytotoxic effects on untransformed stromal mesothelia.
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Affiliation(s)
- Garhima Arora
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India
| | - Mallar Banerjee
- Developmental Biology and Genetics, Indian Institute of Science, Bangalore 560012, India
| | - Jimpi Langthasa
- Developmental Biology and Genetics, Indian Institute of Science, Bangalore 560012, India
| | - Ramray Bhat
- Developmental Biology and Genetics, Indian Institute of Science, Bangalore 560012, India
- BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Samrat Chatterjee
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India
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3
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Mekala JR, Adusumilli K, Chamarthy S, Angirekula HSR. Novel sights on therapeutic, prognostic, and diagnostics aspects of non-coding RNAs in glioblastoma multiforme. Metab Brain Dis 2023; 38:1801-1829. [PMID: 37249862 PMCID: PMC10227410 DOI: 10.1007/s11011-023-01234-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/09/2023] [Indexed: 05/31/2023]
Abstract
Glioblastoma Multiforme (GBM) is the primary brain tumor and accounts for 200,000 deaths each year worldwide. The standard therapy includes surgical resection followed by temozolomide (TMZ)-based chemotherapy and radiotherapy. The survival period of GBM patients is only 12-15 months. Therefore, novel treatment modalities for GBM treatment are urgently needed. Mounting evidence reveals that non-coding RNAs (ncRNAs) were involved in regulating gene expression, the pathophysiology of GBM, and enhancing therapeutic outcomes. The combinatory use of ncRNAs, chemotherapeutic drugs, and tumor suppressor gene expression induction might provide an innovative, alternative therapeutic approach for managing GBM. Studies have highlighted the role of Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in prognosis and diagnosis. Dysregulation of ncRNAs is observed in virtually all tumor types, including GBMs. Studies have also indicated the blood-brain barrier (BBB) as a crucial factor that hinders chemotherapy. Although several nanoparticle-mediated drug deliveries were degrading effectively against GBM in vitro conditions. However, the potential to cross the BBB and optimum delivery of oligonucleotide RNA into GBM cells in the brain is currently under intense clinical trials. Despite several advances in molecular pathogenesis, GBM remains resistant to chemo and radiotherapy. Targeted therapies have less clinical benefit due to high genetic heterogeneity and activation of alternative pathways. Thus, identifying GBM-specific prognostic pathways, essential genes, and genomic aberrations provide several potential benefits as subtypes of GBM. Also, these approaches will provide insights into new strategies to overcome the heterogenous nature of GBM, which will eventually lead to successful therapeutic interventions toward precision medicine and precision oncology.
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Affiliation(s)
- Janaki Ramaiah Mekala
- Department of Bio-Technology, Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram, Guntur, 522302, Andhra Pradesh, India.
| | - Kowsalya Adusumilli
- Department of Bio-Technology, Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram, Guntur, 522302, Andhra Pradesh, India
| | - Sahiti Chamarthy
- Department of Bio-Technology, Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram, Guntur, 522302, Andhra Pradesh, India
| | - Hari Sai Ram Angirekula
- Department of Bio-Technology, Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram, Guntur, 522302, Andhra Pradesh, India
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Mirhakkak MH, Chen X, Ni Y, Heinekamp T, Sae-Ong T, Xu LL, Kurzai O, Barber AE, Brakhage AA, Boutin S, Schäuble S, Panagiotou G. Genome-scale metabolic modeling of Aspergillus fumigatus strains reveals growth dependencies on the lung microbiome. Nat Commun 2023; 14:4369. [PMID: 37474497 PMCID: PMC10359302 DOI: 10.1038/s41467-023-39982-5] [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: 06/27/2022] [Accepted: 07/03/2023] [Indexed: 07/22/2023] Open
Abstract
Aspergillus fumigatus, an opportunistic human pathogen, frequently infects the lungs of people with cystic fibrosis and is one of the most common causes of infectious-disease death in immunocompromised patients. Here, we construct 252 strain-specific, genome-scale metabolic models of this important fungal pathogen to study and better understand the metabolic component of its pathogenic versatility. The models show that 23.1% of A. fumigatus metabolic reactions are not conserved across strains and are mainly associated with amino acid, nucleotide, and nitrogen metabolism. Profiles of non-conserved reactions and growth-supporting reaction fluxes are sufficient to differentiate strains, for example by environmental or clinical origin. In addition, shotgun metagenomics analysis of sputum from 40 cystic fibrosis patients (15 females, 25 males) before and after diagnosis with an A. fumigatus colonization suggests that the fungus shapes the lung microbiome towards a more beneficial fungal growth environment associated with aromatic amino acid availability and the shikimate pathway. Our findings are starting points for the development of drugs or microbiome intervention strategies targeting fungal metabolic needs for survival and colonization in the non-native environment of the human lung.
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Affiliation(s)
- Mohammad H Mirhakkak
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Xiuqiang Chen
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Yueqiong Ni
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Thorsten Heinekamp
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Tongta Sae-Ong
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Lin-Lin Xu
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Oliver Kurzai
- Institute for Hygiene and Microbiology, University of Würzburg, 97080, Würzburg, Germany
- Research Group Fungal Septomics, Leibniz Institute of Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
- National Reference Center for Invasive Fungal Infections (NRZMyk), Leibniz Institute of Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
| | - Amelia E Barber
- Junior Research Group Fungal Informatics, Institute of Microbiology, Friedrich-Schiller-University Jena, 07745, Jena, Germany
| | - Axel A Brakhage
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany
- Institute of Microbiology, Friedrich Schiller University Jena, 07745, Jena, Germany
| | - Sebastien Boutin
- Department of Infectious Diseases and Microbiology, University of Lübeck, 23562, Lübeck, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, 69120, Heidelberg, Germany
| | - Sascha Schäuble
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany.
| | - Gianni Panagiotou
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745, Jena, Germany.
- Department of Medicine and State Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong, Hong Kong, China.
- Friedrich Schiller University, Faculty of Biological Sciences, Jena, 07745, Germany.
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Identification of Therapeutic Targets for Medulloblastoma by Tissue-Specific Genome-Scale Metabolic Model. MOLECULES (BASEL, SWITZERLAND) 2023; 28:molecules28020779. [PMID: 36677837 PMCID: PMC9864031 DOI: 10.3390/molecules28020779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/02/2023] [Accepted: 01/04/2023] [Indexed: 01/15/2023]
Abstract
Medulloblastoma (MB), occurring in the cerebellum, is the most common childhood brain tumor. Because conventional methods decline life quality and endanger children with detrimental side effects, computer models are needed to imitate the characteristics of cancer cells and uncover effective therapeutic targets with minimum toxic effects on healthy cells. In this study, metabolic changes specific to MB were captured by the genome-scale metabolic brain model integrated with transcriptome data. To determine the roles of sphingolipid metabolism in proliferation and metastasis in the cancer cell, 79 reactions were incorporated into the MB model. The pathways employed by MB without a carbon source and the link between metastasis and the Warburg effect were examined in detail. To reveal therapeutic targets for MB, biomass-coupled reactions, the essential genes/gene products, and the antimetabolites, which might deplete the use of metabolites in cells by triggering competitive inhibition, were determined. As a result, interfering with the enzymes associated with fatty acid synthesis (FAs) and the mevalonate pathway in cholesterol synthesis, suppressing cardiolipin production, and tumor-supporting sphingolipid metabolites might be effective therapeutic approaches for MB. Moreover, decreasing the activity of succinate synthesis and GABA-catalyzing enzymes concurrently might be a promising strategy for metastatic MB.
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Sevrin T, Strasser L, Ternet C, Junk P, Caffarini M, Prins S, D’Arcy C, Catozzi S, Oliviero G, Wynne K, Kiel C, Luthert PJ. Whole-cell energy modeling reveals quantitative changes of predicted energy flows in RAS mutant cancer cell lines. iScience 2023; 26:105931. [PMID: 36711246 PMCID: PMC9874014 DOI: 10.1016/j.isci.2023.105931] [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: 06/08/2022] [Revised: 10/27/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Cellular utilization of available energy flows to drive a multitude of forms of cellular "work" is a major biological constraint. Cells steer metabolism to address changing phenotypic states but little is known as to how bioenergetics couples to the richness of processes in a cell as a whole. Here, we outline a whole-cell energy framework that is informed by proteomic analysis and an energetics-based gene ontology. We separate analysis of metabolic supply and the capacity to generate high-energy phosphates from a representation of demand that is built on the relative abundance of ATPases and GTPases that deliver cellular work. We employed mouse embryonic fibroblast cell lines that express wild-type KRAS or oncogenic mutations and with distinct phenotypes. We observe shifts between energy-requiring processes. Calibrating against Seahorse analysis, we have created a whole-cell energy budget with apparent predictive power, for instance in relation to protein synthesis.
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Affiliation(s)
- Thomas Sevrin
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Lisa Strasser
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Camille Ternet
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Philipp Junk
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Miriam Caffarini
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Stella Prins
- UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London EC1V 9EL, UK
| | - Cian D’Arcy
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Simona Catozzi
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Giorgio Oliviero
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Kieran Wynne
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Christina Kiel
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- Department of Molecular Medicine, University of Pavia, 27100 Pavia, Italy
- Corresponding author
| | - Philip J. Luthert
- UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London EC1V 9EL, UK
- NIHR Moorfields Biomedical Research Centre, University College London, 11-43 Bath Street, London EC1V 9EL, UK
- Corresponding author
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7
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Guan S, He Y, Su Y, Zhou L. A Risk Signature Consisting of Eight m 6A Methylation Regulators Predicts the Prognosis of Glioma. Cell Mol Neurobiol 2022; 42:2733-2743. [PMID: 34432221 DOI: 10.1007/s10571-021-01135-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 07/27/2021] [Indexed: 01/05/2023]
Abstract
Glioma progression seriously correlates to the epigenetic context. This study aims to identify glioma subtypes by clustering analysis of patients using the multi-omics data of N6-methyladenosine (m6A) methylation regulators and to construct a risk signature for investigating the role of m6A methylation regulators in the prognosis of glioma. Multi-omics data of glioma and normal control tissues were obtained through The Cancer Genome Atlas (TCGA) database. The clustering analysis of multi-omics data of patients was conducted using the R package iClusterPlus software. The risk model was constructed by univariate and multivariate Cox analysis, and the glioma expression data and related clinical data were obtained by Chinese Glioma Genome Atlas (CGGA) datasets to verify the risk model. By analyzing the glioma data in TCGA, we found that the risk signature could be constructed according to the eight genes with m6A methylation modification function, including ALKBH5, HNRNPA2B1, IGF2BP2, IGF2BP3, RBM15, WTAP, YTHDF1, and YTHDF2. Meanwhile, we found that IGF2BP2 and IGF2BP3 were highly expressed in glioma subtypes with high-risk scores and closely related to the prognosis of glioma patients. m6A methylation regulators, especially IGF2BP2 and IGF2BP3, play important roles in the malignant progression of glioma. The risk signature constructed by eight m6A methylation regulators can predict the prognosis of glioma. IGF2BP2 and IGF2BP3 may be the key regulatory factors of m6A methylation regulators involved in the occurrence and development of glioma, and can serve as molecular markers for the prognosis of glioma.
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Affiliation(s)
- Sizhong Guan
- Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, 110001, People's Republic of China
| | - Ye He
- Department of Laboratory Medicine, The First Hospital of China Medical University, Shenyang, 110001, People's Republic of China
| | - Yanna Su
- Department of Laboratory Medicine, The First Hospital of China Medical University, Shenyang, 110001, People's Republic of China
| | - Liping Zhou
- Post Graduation Training Department, The First Hospital of China Medical University, No. 155, Northern Nanjing Road, Heping District, Shenyang, 110001, Liaoning Province, People's Republic of China.
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8
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Karakurt HU, Pir P. In silico analysis of metabolic effects of bipolar disorder on prefrontal cortex identified altered GABA, glutamate-glutamine cycle, energy metabolism and amino acid synthesis pathways. Integr Biol (Camb) 2022:zyac012. [PMID: 36241207 DOI: 10.1093/intbio/zyac012] [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: 02/04/2022] [Revised: 05/31/2022] [Accepted: 09/04/2022] [Indexed: 06/16/2023]
Abstract
Bipolar disorder (BP) is a lifelong psychiatric condition, which often disrupts the daily life of the patients. It is characterized by unstable and periodic mood changes, which cause patients to display unusual shifts in mood, energy, activity levels, concentration and the ability to carry out day-to-day tasks. BP is a major psychiatric condition, and it is still undertreated. The causes and neural mechanisms of bipolar disorder are unclear, and diagnosis is still mostly based on psychiatric examination, furthermore the unstable character of the disorder makes diagnosis challenging. Identification of the molecular mechanisms underlying the disease may improve the diagnosis and treatment rates. Single nucleotide polymorphisms (SNP) and transcriptome profiles of patients were studied along with signalling pathways that are thought to be associated with bipolar disorder. Here, we present a computational approach that uses publicly available transcriptome data from bipolar disorder patients and healthy controls. Along with statistical analyses, data are integrated with a genome-scale metabolic model and protein-protein interaction network. Healthy individuals and bipolar disorder patients are compared based on their metabolic profiles. We hypothesize that energy metabolism alterations in bipolar disorder relate to perturbations in amino-acid metabolism and neuron-astrocyte exchange reactions. Due to changes in amino acid metabolism, neurotransmitters and their secretion from neurons and metabolic exchange pathways between neurons and astrocytes such as the glutamine-glutamate cycle are also altered. Changes in negatively charged (-1) KIV and KMV molecules are also observed, and it indicates that charge balance in the brain is highly altered in bipolar disorder. Due to this fact, we also hypothesize that positively charged lithium ions may stabilize the disturbed charge balance in neurons in addition to its effects on neurotransmission. To the best of our knowledge, our approach is unique as it is the first study using genome-scale metabolic models in neuropsychiatric research.
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Affiliation(s)
- Hamza Umut Karakurt
- Gebze Technical University, Department of Bioengineering, 41400, Kocaeli, Turkey
| | - Pınar Pir
- Gebze Technical University, Department of Bioengineering, 41400, Kocaeli, Turkey
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9
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Kishk A, Pacheco MP, Heurtaux T, Sinkkonen L, Pang J, Fritah S, Niclou SP, Sauter T. Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases. Cells 2022; 11:cells11162486. [PMID: 36010563 PMCID: PMC9406599 DOI: 10.3390/cells11162486] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/28/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.
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Affiliation(s)
- Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Tony Heurtaux
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Luxembourg Center of Neuropathology, L-3555 Dudelange, Luxembourg
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Jun Pang
- Department of Computer Science, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg
| | - Sabrina Fritah
- NORLUX Neuro-Oncology Laboratory, Luxembourg Institute of Health, Department of Cancer Research, L-1526 Luxembourg, Luxembourg
| | - Simone P. Niclou
- NORLUX Neuro-Oncology Laboratory, Luxembourg Institute of Health, Department of Cancer Research, L-1526 Luxembourg, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Correspondence:
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10
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Metabolic modeling-based drug repurposing in Glioblastoma. Sci Rep 2022; 12:11189. [PMID: 35778411 PMCID: PMC9249780 DOI: 10.1038/s41598-022-14721-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 06/10/2022] [Indexed: 11/16/2022] Open
Abstract
The manifestation of intra- and inter-tumor heterogeneity hinders the development of ubiquitous cancer treatments, thus requiring a tailored therapy for each cancer type. Specifically, the reprogramming of cellular metabolism has been identified as a source of potential drug targets. Drug discovery is a long and resource-demanding process aiming at identifying and testing compounds early in the drug development pipeline. While drug repurposing efforts (i.e., inspecting readily available approved drugs) can be supported by a mechanistic rationale, strategies to further reduce and prioritize the list of potential candidates are still needed to facilitate feasible studies. Although a variety of ‘omics’ data are widely gathered, a standard integration method with modeling approaches is lacking. For instance, flux balance analysis is a metabolic modeling technique that mainly relies on the stoichiometry of the metabolic network. However, exploring the network’s topology typically neglects biologically relevant information. Here we introduce Transcriptomics-Informed Stoichiometric Modelling And Network analysis (TISMAN) in a recombinant innovation manner, allowing identification and validation of genes as targets for drug repurposing using glioblastoma as an exemplar.
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11
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Li G, Han F, Xiao F, Gu K, Shen Q, Xu W, Li W, Wang Y, Liang B, Huang J, Xiao W, Kong Q. System-level metabolic modeling facilitates unveiling metabolic signature in exceptional longevity. Aging Cell 2022; 21:e13595. [PMID: 35343058 PMCID: PMC9009231 DOI: 10.1111/acel.13595] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/17/2022] [Accepted: 03/10/2022] [Indexed: 12/29/2022] Open
Abstract
Although it is well known that metabolic control plays a crucial role in regulating the health span and life span of various organisms, little is known for the systems metabolic profile of centenarians, the paradigm of human healthy aging and longevity. Meanwhile, how to well characterize the system‐level metabolic states in an organism of interest remains to be a major challenge in systems metabolism research. To address this challenge and better understand the metabolic mechanisms of healthy aging, we developed a method of genome‐wide precision metabolic modeling (GPMM) which is able to quantitatively integrate transcriptome, proteome and kinetome data in predictive modeling of metabolic networks. Benchmarking analysis showed that GPMM successfully characterized metabolic reprogramming in the NCI‐60 cancer cell lines; it dramatically improved the performance of the modeling with an R2 of 0.86 between the predicted and experimental measurements over the performance of existing methods. Using this approach, we examined the metabolic networks of a Chinese centenarian cohort and identified the elevated fatty acid oxidation (FAO) as the most significant metabolic feature in these long‐lived individuals. Evidence from serum metabolomics supports this observation. Given that FAO declines with normal aging and is impaired in many age‐related diseases, our study suggests that the elevated FAO has potential to be a novel signature of healthy aging of humans.
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Affiliation(s)
- Gong‐Hua Li
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- Kunming Key Laboratory of Healthy Aging Study Kunming Yunnan China
| | - Feifei Han
- Harvard Medical School Immune and Metabolic Computational Center Massachusetts General Hospital Boston Massachusetts USA
| | - Fu‐Hui Xiao
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- Kunming Key Laboratory of Healthy Aging Study Kunming Yunnan China
| | - Kang‐Su‐Yun Gu
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- Kunming Key Laboratory of Healthy Aging Study Kunming Yunnan China
| | - Qiu Shen
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
| | - Weihong Xu
- Harvard Medical School Immune and Metabolic Computational Center Massachusetts General Hospital Boston Massachusetts USA
| | - Wen‐Xing Li
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
| | - Yan‐Li Wang
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- School of Life Sciences Center for Life Sciences Yunnan University Kunming Yunnan China
| | - Bin Liang
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- School of Life Sciences Center for Life Sciences Yunnan University Kunming Yunnan China
| | - Jing‐Fei Huang
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
| | - Wenzhong Xiao
- Harvard Medical School Immune and Metabolic Computational Center Massachusetts General Hospital Boston Massachusetts USA
| | - Qing‐Peng Kong
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- Kunming Key Laboratory of Healthy Aging Study Kunming Yunnan China
- CAS Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming Yunnan China
- KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases Kunming Yunnan China
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12
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Barata T, Vieira V, Rodrigues R, Neves RPD, Rocha M. Reconstruction of tissue-specific genome-scale metabolic models for human cancer stem cells. Comput Biol Med 2021; 142:105177. [PMID: 35026576 DOI: 10.1016/j.compbiomed.2021.105177] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 02/07/2023]
Abstract
Cancer Stem Cells (CSCs) contribute to cancer aggressiveness, metastasis, chemo/radio-therapy resistance, and tumor recurrence. Recent studies emphasized the importance of metabolic reprogramming of CSCs for the maintenance and progression of the cancer phenotype through both the fulfillment of the energetic requirements and the supply of substrates fundamental for fast-cell growth, as well as through metabolite-induced epigenetic regulation. Therefore, it is of paramount importance to develop therapeutic strategies tailored to target the metabolism of CSCs. In this work, we built computational Genome-Scale Metabolic Models (GSMMs) for CSCs of different tissues. Flux simulations were then used to predict metabolic phenotypes, identify potential therapeutic targets, and spot already-known Transcription Factors (TFs), miRNAs and antimetabolites that could be used as part of drug repurposing strategies against cancer. Results were in accordance with experimental evidence, provided insights of new metabolic mechanisms for already known agents, and allowed for the identification of potential new targets and compounds that could be interesting for further in vitro and in vivo validation.
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Affiliation(s)
- Tânia Barata
- CNC - Center for Neuroscience and Cell Biology, CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-517, Coimbra, Portugal
| | - Vítor Vieira
- Centre of Biological Engineering, University of Minho - Campus de Gualtar, Braga, Portugal
| | - Rúben Rodrigues
- Centre of Biological Engineering, University of Minho - Campus de Gualtar, Braga, Portugal
| | - Ricardo Pires das Neves
- CNC - Center for Neuroscience and Cell Biology, CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-517, Coimbra, Portugal; IIIUC-Institute of Interdisciplinary Research, University of Coimbra, 3030-789, Coimbra, Portugal.
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho - Campus de Gualtar, Braga, Portugal; Department of Informatics, University of Minho.
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13
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Passi A, Tibocha-Bonilla JD, Kumar M, Tec-Campos D, Zengler K, Zuniga C. Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data. Metabolites 2021; 12:14. [PMID: 35050136 PMCID: PMC8778254 DOI: 10.3390/metabo12010014] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 11/16/2022] Open
Abstract
Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.
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Affiliation(s)
- Anurag Passi
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
| | - Juan D. Tibocha-Bonilla
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA;
| | - Manish Kumar
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
| | - Diego Tec-Campos
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
- Facultad de Ingeniería Química, Campus de Ciencias Exactas e Ingenierías, Universidad Autónoma de Yucatán, Merida 97203, Yucatan, Mexico
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA
- Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0403, USA
| | - Cristal Zuniga
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
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14
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Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme. Int J Mol Sci 2021; 22:ijms222413213. [PMID: 34948010 PMCID: PMC8706582 DOI: 10.3390/ijms222413213] [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: 11/04/2021] [Revised: 11/30/2021] [Accepted: 12/04/2021] [Indexed: 11/29/2022] Open
Abstract
Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.
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15
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Lee SM, Kim HU. Development of computational models using omics data for the identification of effective cancer metabolic biomarkers. Mol Omics 2021; 17:881-893. [PMID: 34608924 DOI: 10.1039/d1mo00337b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Identification of novel biomarkers has been an active area of study for the effective diagnosis, prognosis and treatment of cancers. Among various types of cancer biomarkers, metabolic biomarkers, including enzymes, metabolites and metabolic genes, deserve attention as they can serve as a reliable source for diagnosis, prognosis and treatment of cancers. In particular, efforts to identify novel biomarkers have been greatly facilitated by a rapid increase in the volume of multiple omics data generated for a range of cancer cells. These omics data in turn serve as ingredients for developing computational models that can help derive deeper insights into the biology of cancer cells, and identify metabolic biomarkers. In this review, we provide an overview of omics data generated for cancer cells, and discuss recent studies on computational models that were developed using omics data in order to identify effective cancer metabolic biomarkers.
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Affiliation(s)
- Sang Mi Lee
- Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Hyun Uk Kim
- Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. .,KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea.,BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
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16
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Kishk A, Pacheco MP, Sauter T. DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling. iScience 2021; 24:103331. [PMID: 34723158 PMCID: PMC8536485 DOI: 10.1016/j.isci.2021.103331] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/29/2021] [Accepted: 10/19/2021] [Indexed: 12/15/2022] Open
Abstract
The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).
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Affiliation(s)
- Ali Kishk
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Maria Pires Pacheco
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Thomas Sauter
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
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17
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Ye M, Lin Y, Pan S, Wang ZW, Zhu X. Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis. Front Oncol 2021; 11:745808. [PMID: 34631583 PMCID: PMC8497990 DOI: 10.3389/fonc.2021.745808] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/08/2021] [Indexed: 12/29/2022] Open
Abstract
Ovarian cancer ranks as the fifth most common cause of cancer-related death in females. The molecular mechanisms of ovarian carcinogenesis need to be explored in order to identify effective clinical therapies for ovarian cancer. Recently, multi-omics approaches have been applied to determine the mechanisms of ovarian oncogenesis at genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites) levels. Multi-omics approaches can identify some diagnostic and prognostic biomarkers and therapeutic targets for ovarian cancer, and these molecular signatures are beneficial for clarifying the development and progression of ovarian cancer. Moreover, the discovery of molecular signatures and targeted therapy strategies could noticeably improve the prognosis of ovarian cancer patients.
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Affiliation(s)
- Miaomiao Ye
- Center of Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yibin Lin
- Center of Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shuya Pan
- Center of Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhi-Wei Wang
- Center of Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xueqiong Zhu
- Center of Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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18
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Han XX, Cai C, Yu LM, Wang M, Hu DY, Ren J, Zhang MH, Zhu LY, Zhang WH, Huang W, He H, Gao Z. A Fast and Efficient Approach to Obtaining High-Purity Glioma Stem Cell Culture. Front Genet 2021; 12:639858. [PMID: 34295351 PMCID: PMC8291338 DOI: 10.3389/fgene.2021.639858] [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: 12/10/2020] [Accepted: 04/16/2021] [Indexed: 12/15/2022] Open
Abstract
Glioma is the most common and malignant primary brain tumor. Patients with malignant glioma usually have a poor prognosis due to drug resistance and disease relapse. Cancer stem cells contribute to glioma initiation, progression, resistance, and relapse. Hence, quick identification and efficient understanding of glioma stem cells (GSCs) are of profound importance for therapeutic strategies and outcomes. Ideally, therapeutic approaches will only kill cancer stem cells without harming normal neural stem cells (NSCs) that can inhibit GSCs and are often beneficial. It is key to identify the differences between cancer stem cells and normal NSCs. However, reports detailing an efficient and uniform protocol are scarce, as are comparisons between normal neural and cancer stem cells. Here, we compared different protocols and developed a fast and efficient approach to obtaining high-purity glioma stem cell by tracking observation and optimizing culture conditions. We examined the proliferative and differentiative properties confirming the identities of the GSCs with relevant markers such as Ki67, SRY-box containing gene 2, an intermediate filament protein member nestin, glial fibrillary acidic protein, and s100 calcium-binding protein (s100-beta). Finally, we identified distinct expression differences between GSCs and normal NSCs including cyclin-dependent kinase 4 and tumor protein p53. This study comprehensively describes the features of GSCs, their properties, and regulatory genes with expression differences between them and normal stem cells. Effective approaches to quickly obtaining high-quality GSCs from patients should have the potential to not only help understand the diseases and the resistances but also enable target drug screening and personalized medicine for brain tumor treatment.
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Affiliation(s)
- Xin-Xin Han
- Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases, Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Chunhui Cai
- Tongji University Cancer Center, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Li-Ming Yu
- Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases, Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Min Wang
- School of Medicine, Jiaxing University, Jiaxing, China
| | - Dai-Yu Hu
- Tongji University Cancer Center, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jie Ren
- Tongji University Cancer Center, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Meng-Han Zhang
- Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases, Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Lu-Ying Zhu
- Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases, Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Wei-Hua Zhang
- Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases, Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Wei Huang
- Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases, Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Hua He
- Department of Neurosurgery, Changzheng Hospital, Second Military Medical University, Shanghai, China.,Department of Neurosurgery, Third Affiliated Hospital of Second Military Medical University, Shanghai, China
| | - Zhengliang Gao
- Tongji University Cancer Center, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
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