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Ahmad F, Muhmood T. Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions. Colloids Surf B Biointerfaces 2024; 241:114041. [PMID: 38897022 DOI: 10.1016/j.colsurfb.2024.114041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/21/2024]
Abstract
Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical studies followed by regulatory hinderances. To tackle this, integrating the nanomedicine discovery with digital medicine provide technologies as tools of specific biological activity measurement. Hence, overcome the redundancies in nanomedicine discovery by the on-site data acquisition and analytics through integrating intelligent sensors and artificial intelligence (AI) or machine learning (ML). Integrated AI/ML wearable sensors directly gather clinically relevant biochemical information from the subject's body and process data for physicians to make right clinical decision(s) in a time and cost-effective way. This review summarizes insights and recommend the infusion of actionable big data computation enabled sensors in burgeoning field of nanomedicine at academia, research institutes, and pharmaceutical industries, with a potential of clinical translation. Furthermore, many blind spots are present in modern clinically relevant computation, one of which could prevent ML-guided low-cost new nanomedicine development from being successfully translated into the clinic was also discussed.
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Affiliation(s)
- Farooq Ahmad
- State Key Laboratory of Chemistry and Utilization of Carbon Based Energy Resources, College of Chemistry, Xinjiang University, Urumqi 830017, China.
| | - Tahir Muhmood
- International Iberian Nanotechnology Laboratory (INL), Avenida Mestre José Veiga, Braga 4715-330, Portugal.
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Jia ZC, Yang X, Wu YK, Li M, Das D, Chen MX, Wu J. The Art of Finding the Right Drug Target: Emerging Methods and Strategies. Pharmacol Rev 2024; 76:896-914. [PMID: 38866560 PMCID: PMC11334170 DOI: 10.1124/pharmrev.123.001028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/14/2024] Open
Abstract
Drug targets are specific molecules in biological tissues and body fluids that interact with drugs. Drug target discovery is a key component of drug discovery and is essential for the development of new drugs in areas such as cancer therapy and precision medicine. Traditional in vitro or in vivo target discovery methods are time-consuming and labor-intensive, limiting the pace of drug discovery. With the development of modern discovery methods, the discovery and application of various emerging technologies have greatly improved the efficiency of drug discovery, shortened the cycle time, and reduced the cost. This review provides a comprehensive overview of various emerging drug target discovery strategies, including computer-assisted approaches, drug affinity response target stability, multiomics analysis, gene editing, and nonsense-mediated mRNA degradation, and discusses the effectiveness and limitations of the various approaches, as well as their application in real cases. Through the review of the aforementioned contents, a general overview of the development of novel drug targets and disease treatment strategies will be provided, and a theoretical basis will be provided for those who are engaged in pharmaceutical science research. SIGNIFICANCE STATEMENT: Target-based drug discovery has been the main approach to drug discovery in the pharmaceutical industry for the past three decades. Traditional drug target discovery methods based on in vivo or in vitro validation are time-consuming and costly, greatly limiting the development of new drugs. Therefore, the development and selection of new methods in the drug target discovery process is crucial.
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Affiliation(s)
- Zi-Chang Jia
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Xue Yang
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Yi-Kun Wu
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Min Li
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Debatosh Das
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.) ;
| | - Mo-Xian Chen
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.) ;
| | - Jian Wu
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.) ;
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Jiang J, Ye Y, Li F, Luo H, Wang W. A commentary on 'Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis'. Int J Surg 2024; 110:5157-5158. [PMID: 38597383 PMCID: PMC11325880 DOI: 10.1097/js9.0000000000001452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 03/30/2024] [Indexed: 04/11/2024]
Affiliation(s)
- Jing Jiang
- Department of Pharmacy, Beilun District People's Hospital
| | - Yun Ye
- Department of Pharmacy, Beilun District People's Hospital
| | - Fangxian Li
- Department of Gastroenterology, Beilun District People's Hospital
| | - Hedan Luo
- Department of Information, Beilun District People's Hospital, Ningbo City, Zhejiang Province, People's Republic of China
| | - Wenbo Wang
- Department of Pharmacy, Beilun District People's Hospital
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Frasnetti E, Magni A, Castelli M, Serapian SA, Moroni E, Colombo G. Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence. Curr Opin Struct Biol 2024; 87:102835. [PMID: 38744148 DOI: 10.1016/j.sbi.2024.102835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/14/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods can bridge a wide range of length- and time-scales typically involved in such processes. Lately, automated learning and artificial intelligence methods have shown the potential to expand the reach of physics-based approaches, ushering in the possibility to model and even design complex protein architectures. The synergy between atomistic simulations and AI methods is an emerging frontier with a huge potential for advances in structural biology. Herein, we explore various examples and frameworks for these approaches, providing select instances and applications that illustrate their impact on fundamental biomolecular problems.
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Affiliation(s)
- Elena Frasnetti
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Andrea Magni
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Matteo Castelli
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Stefano A Serapian
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | | | - Giorgio Colombo
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy.
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Xiao K, Wang S, Chen W, Hu Y, Chen Z, Liu P, Zhang J, Chen B, Zhang Z, Li X. Identification of novel immune-related signatures for keloid diagnosis and treatment: insights from integrated bulk RNA-seq and scRNA-seq analysis. Hum Genomics 2024; 18:80. [PMID: 39014455 PMCID: PMC11251391 DOI: 10.1186/s40246-024-00647-z] [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: 06/01/2024] [Accepted: 07/01/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND Keloid is a disease characterized by proliferation of fibrous tissue after the healing of skin tissue, which seriously affects the daily life of patients. However, the clinical treatment of keloids still has limitations, that is, it is not effective in controlling keloids, resulting in a high recurrence rate. Thus, it is urgent to identify new signatures to improve the diagnosis and treatment of keloids. METHOD Bulk RNA seq and scRNA seq data were downloaded from the GEO database. First, we used WGCNA and MEGENA to co-identify keloid/immune-related DEGs. Subsequently, we used three machine learning algorithms (Randomforest, SVM-RFE, and LASSO) to identify hub immune-related genes of keloid (KHIGs) and investigated the heterogeneous expression of KHIGs during fibroblast subpopulation differentiation using scRNA-seq. Finally, we used HE and Masson staining, quantitative reverse transcription-PCR, western blotting, immunohistochemical, and Immunofluorescent assay to investigate the dysregulated expression and the mechanism of retinoic acid in keloids. RESULTS In the present study, we identified PTGFR, RBP5, and LIF as KHIGs and validated their diagnostic performance. Subsequently, we constructed a novel artificial neural network molecular diagnostic model based on the transcriptome pattern of KHIGs, which is expected to break through the current dilemma faced by molecular diagnosis of keloids in the clinic. Meanwhile, the constructed IG score can also effectively predict keloid risk, which provides a new strategy for keloid prevention. Additionally, we observed that KHIGs were also heterogeneously expressed in the constructed differentiation trajectories of fibroblast subtypes, which may affect the differentiation of fibroblast subtypes and thus lead to dysregulation of the immune microenvironment in keloids. Finally, we found that retinoic acid may treat or alleviate keloids by inhibiting RBP5 to differentiate pro-inflammatory fibroblasts (PIF) to mesenchymal fibroblasts (MF), which further reduces collagen secretion. CONCLUSION In summary, the present study provides novel immune signatures (PTGFR, RBP5, and LIF) for keloid diagnosis and treatment, and identifies retinoic acid as potential anti-keloid drugs. More importantly, we provide a new perspective for understanding the interactions between different fibroblast subtypes in keloids and the remodeling of their immune microenvironment.
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Affiliation(s)
- Kui Xiao
- Department of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China
| | - Sisi Wang
- Department of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China
| | - Wenxin Chen
- Department of Gynaecology and Obstetrics, Hengyang Central Hospital, Hunan Normal University, Hengyang, China
| | - Yiping Hu
- Department of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China
| | - Ziang Chen
- Department of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China
| | - Peng Liu
- Department of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China
| | - Jinli Zhang
- Department of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China
| | - Bin Chen
- Department of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China.
| | - Zhi Zhang
- Department of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China.
| | - Xiaojian Li
- Department of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China.
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Guo H, Xiao K, Zheng Y, Zong J. Integrating bioinformatics and multiple machine learning to identify mitophagy-related targets for the diagnosis and treatment of diabetic foot ulcers: evidence from transcriptome analysis and drug docking. Front Mol Biosci 2024; 11:1420136. [PMID: 39044840 PMCID: PMC11263085 DOI: 10.3389/fmolb.2024.1420136] [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: 04/19/2024] [Accepted: 06/20/2024] [Indexed: 07/25/2024] Open
Abstract
Background Diabetic foot ulcers are the most common and serious complication of diabetes mellitus, the high morbidity, mortality, and disability of which greatly diminish the quality of life of patients and impose a heavy socioeconomic burden. Thus, it is urgent to identify potential biomarkers and targeted drugs for diabetic foot ulcers. Methods In this study, we downloaded datasets related to diabetic foot ulcers from gene expression omnibus. Dysregulation of mitophagy-related genes was identified by differential analysis and weighted gene co-expression network analysis. Multiple machine algorithms were utilized to identify hub mitophagy-related genes, and a novel artificial neural network model for assisting in the diagnosis of diabetic foot ulcers was constructed based on their transcriptome expression patterns. Finally, potential drugs that can target hub mitophagy-related genes were identified using the Enrichr platform and molecular docking methods. Results In this study, we identified 702 differentially expressed genes related to diabetic foot ulcers, and enrichment analysis showed that these genes were associated with mitochondria and energy metabolism. Subsequently, we identified hexokinase-2, small ribosomal subunit protein us3, and l-lactate dehydrogenase A chain as hub mitophagy-related genes of diabetic foot ulcers using multiple machine learning algorithms and validated their diagnostic performance in a validation cohort independent of the present study (The areas under roc curve of hexokinase-2, small ribosomal subunit protein us3, and l-lactate dehydrogenase A chain are 0.671, 0.870, and 0.739, respectively). Next, we constructed a novel artificial neural network model for the molecular diagnosis of diabetic foot ulcers, and the diagnostic performance of the training cohort and validation cohort was good, with areas under roc curve of 0.924 and 0.840, respectively. Finally, we identified retinoic acid and estradiol as promising anti-diabetic foot ulcers by targeting hexokinase-2 (-6.6 and -7.2 kcal/mol), small ribosomal subunit protein us3 (-7.5 and -8.3 kcal/mol), and l-lactate dehydrogenase A chain (-7.6 and -8.5 kcal/mol). Conclusion The present study identified hexokinase-2, small ribosomal subunit protein us3 and l-lactate dehydrogenase A chain, and emphasized their critical roles in the diagnosis and treatment of diabetic foot ulcers through multiple dimensions, providing promising diagnostic biomarkers and targeted drugs for diabetic foot ulcers.
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Affiliation(s)
- Hui Guo
- Department of Emergency, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Kui Xiao
- Department of Plastic Surgery, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China
| | - Yanhua Zheng
- Department of Critical Medicine, Wusong Hospital, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jianchun Zong
- Department of Emergency, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
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Müller J, Boubaker G, Müller N, Uldry AC, Braga-Lagache S, Heller M, Hemphill A. Investigating Antiprotozoal Chemotherapies with Novel Proteomic Tools-Chances and Limitations: A Critical Review. Int J Mol Sci 2024; 25:6903. [PMID: 39000012 PMCID: PMC11241152 DOI: 10.3390/ijms25136903] [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: 05/31/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 07/14/2024] Open
Abstract
Identification of drug targets and biochemical investigations on mechanisms of action are major issues in modern drug development. The present article is a critical review of the classical "one drug"-"one target" paradigm. In fact, novel methods for target deconvolution and for investigation of resistant strains based on protein mass spectrometry have shown that multiple gene products and adaptation mechanisms are involved in the responses of pathogens to xenobiotics rather than one single gene or gene product. Resistance to drugs may be linked to differential expression of other proteins than those interacting with the drug in protein binding studies and result in complex cell physiological adaptation. Consequently, the unraveling of mechanisms of action needs approaches beyond proteomics. This review is focused on protozoan pathogens. The conclusions can, however, be extended to chemotherapies against other pathogens or cancer.
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Affiliation(s)
- Joachim Müller
- Institute of Parasitology, Vetsuisse Faculty, University of Bern, Länggass-Strasse 122, 3012 Bern, Switzerland
| | - Ghalia Boubaker
- Institute of Parasitology, Vetsuisse Faculty, University of Bern, Länggass-Strasse 122, 3012 Bern, Switzerland
| | - Norbert Müller
- Institute of Parasitology, Vetsuisse Faculty, University of Bern, Länggass-Strasse 122, 3012 Bern, Switzerland
| | - Anne-Christine Uldry
- Proteomics and Mass Spectrometry Core Facility, Department for BioMedical Research (DBMR), University of Bern, Länggass-Strasse 122, 3012 Bern, Switzerland
| | - Sophie Braga-Lagache
- Proteomics and Mass Spectrometry Core Facility, Department for BioMedical Research (DBMR), University of Bern, Länggass-Strasse 122, 3012 Bern, Switzerland
| | - Manfred Heller
- Proteomics and Mass Spectrometry Core Facility, Department for BioMedical Research (DBMR), University of Bern, Länggass-Strasse 122, 3012 Bern, Switzerland
| | - Andrew Hemphill
- Institute of Parasitology, Vetsuisse Faculty, University of Bern, Länggass-Strasse 122, 3012 Bern, Switzerland
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Tan L, Qu J, Wang J. Development of novel lysosome-related signatures and their potential target drugs based on bulk RNA-seq and scRNA-seq for diabetic foot ulcers. Hum Genomics 2024; 18:62. [PMID: 38862997 PMCID: PMC11165785 DOI: 10.1186/s40246-024-00629-1] [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: 02/23/2024] [Accepted: 05/27/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Diabetic foot ulcers (DFU) is the most serious complication of diabetes mellitus, which has become a global health problem due to its high morbidity and disability rates and the poor efficacy of conventional treatments. Thus, it is urgent to identify novel molecular targets to improve the prognosis and reduce disability rate in DFU patients. RESULTS In the present study, bulk RNA-seq and scRNA-seq associated with DFU were downloaded from the GEO database. We identified 1393 DFU-related DEGs by differential analysis and WGCNA analysis together, and GO/KEGG analysis showed that these genes were associated with lysosomal and immune/inflammatory responses. Immediately thereafter, we identified CLU, RABGEF1 and ENPEP as DLGs for DFU using three machine learning algorithms (Randomforest, SVM-RFE and LASSO) and validated their diagnostic performance in a validation cohort independent of this study. Subsequently, we constructed a novel artificial neural network model for molecular diagnosis of DFU based on DLGs, and the diagnostic performance in the training and validation cohorts was sound. In single-cell sequencing, the heterogeneous expression of DLGs also provided favorable evidence for them to be potential diagnostic targets. In addition, the results of immune infiltration analysis showed that the abundance of mainstream immune cells, including B/T cells, was down-regulated in DFUs and significantly correlated with the expression of DLGs. Finally, we found latamoxef, parthenolide, meclofenoxate, and lomustine to be promising anti-DFU drugs by targeting DLGs. CONCLUSIONS CLU, RABGEF1 and ENPEP can be used as novel lysosomal molecular signatures of DFU, and by targeting them, latamoxef, parthenolide, meclofenoxate and lomustine were identified as promising anti-DFU drugs. The present study provides new perspectives for the diagnosis and treatment of DFU and for improving the prognosis of DFU patients.
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Affiliation(s)
- Longhai Tan
- Department of Dermatology, Tianjin Beichen Hospital, Tianjin, 300400, China.
| | - Junjun Qu
- Zhu Xianyi Memorial Hospital of Tianjin Medical University, Tianjin, 300134, China
| | - Junxia Wang
- Department of Dermatology, Tianjin Beichen Hospital, Tianjin, 300400, China
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Ilyas K, Iqbal H, Akash MSH, Rehman K, Hussain A. Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:37963-37987. [PMID: 38780845 DOI: 10.1007/s11356-024-33735-7] [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: 03/07/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024]
Abstract
Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.
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Affiliation(s)
- Kainat Ilyas
- Department of Pharmaceutical Chemistry, Government College University, Faisalabad, Pakistan
| | - Hajra Iqbal
- Department of Pharmaceutical Chemistry, Government College University, Faisalabad, Pakistan
| | | | - Kanwal Rehman
- Department of Pharmacy, The Women University, Multan, Pakistan
| | - Amjad Hussain
- Institute of Chemistry, University of Okara, Okara, Pakistan
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Tarzi C, Zampieri G, Sullivan N, Angione C. Emerging methods for genome-scale metabolic modeling of microbial communities. Trends Endocrinol Metab 2024; 35:533-548. [PMID: 38575441 DOI: 10.1016/j.tem.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
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Affiliation(s)
- Chaimaa Tarzi
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK
| | - Guido Zampieri
- Department of Biology, University of Padova, Padova, 35122, Veneto, Italy
| | - Neil Sullivan
- Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK.
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Wenteler A, Cabrera CP, Wei W, Neduva V, Barnes MR. AI approaches for the discovery and validation of drug targets. CAMBRIDGE PRISMS. PRECISION MEDICINE 2024; 2:e7. [PMID: 39258224 PMCID: PMC11383977 DOI: 10.1017/pcm.2024.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 05/04/2024] [Accepted: 05/08/2024] [Indexed: 09/12/2024]
Abstract
Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.
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Affiliation(s)
- Aaron Wenteler
- Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom
- Centre for Translational Bioinformatics, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- MSD Discovery Centre, London, United Kingdom
| | - Claudia P Cabrera
- Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom
- Centre for Translational Bioinformatics, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Wei Wei
- MSD Discovery Centre, London, United Kingdom
| | | | - Michael R Barnes
- Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom
- Centre for Translational Bioinformatics, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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Hernández ÁP, Chaparro-González L, Garzo-Sánchez O, Arias-Hidalgo C, Juanes-Velasco P, García PA, Castro MÁ, Fuentes M. Podophyllic Aldehyde, a Podophyllotoxin Derivate, Elicits Different Cell Cycle Profiles Depending on the Tumor Cell Line: A Systematic Proteomic Analysis. Int J Mol Sci 2024; 25:4631. [PMID: 38731850 PMCID: PMC11083757 DOI: 10.3390/ijms25094631] [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: 03/15/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
When new antitumor therapy drugs are discovered, it is essential to address new target molecules from the point of view of chemical structure and to carry out efficient and systematic evaluation. In the case of natural products and derived compounds, it is of special importance to investigate chemomodulation to further explore antitumoral pharmacological activities. In this work, the compound podophyllic aldehyde, a cyclolignan derived from the chemomodulation of the natural product podophyllotoxin, has been evaluated for its viability, influence on the cell cycle, and effects on intracellular signaling. We used functional proteomics characterization for the evaluation. Compared with the FDA-approved drug etoposide (another podophyllotoxin derivative), we found interesting results regarding the cytotoxicity of podophyllic aldehyde. In addition, we were able to observe the effect of mitotic arrest in the treated cells. The use of podophyllic aldehyde resulted in increased cytotoxicity in solid tumor cell lines, compared to etoposide, and blocked the cycle more successfully than etoposide. High-throughput analysis of the deregulated proteins revealed a selective antimitotic mechanism of action of podophyllic aldehyde in the HT-29 cell line, in contrast with other solid and hematological tumor lines. Also, the apoptotic profile of podophyllic aldehyde was deciphered. The cell death mechanism is activated independently of the cell cycle profile. The results of these targeted analyses have also shown a significant response to the signaling of kinases, key proteins involved in signaling cascades for cell proliferation or metastasis. Thanks to this comprehensive analysis of podophyllic aldehyde, remarkable cytotoxic, antimitotic, and other antitumoral features have been discovered that will repurpose this compound for further chemical transformations and antitumoral analysis.
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Affiliation(s)
- Ángela-Patricia Hernández
- Department of Medicine and General Cytometry Service-Nucleus, CIBERONC CB16/12/00400, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), IBSAL, University of Salamanca-CSIC, Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain; (L.C.-G.); (O.G.-S.); (C.A.-H.); (P.J.-V.); (M.F.)
- Department of Pharmaceutical Sciences, Laboratory of Medicinal Chemistry, Faculty of Pharmacy, University of Salamanca, Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain; (P.A.G.); (M.Á.C.)
| | - Lorea Chaparro-González
- Department of Medicine and General Cytometry Service-Nucleus, CIBERONC CB16/12/00400, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), IBSAL, University of Salamanca-CSIC, Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain; (L.C.-G.); (O.G.-S.); (C.A.-H.); (P.J.-V.); (M.F.)
| | - Olga Garzo-Sánchez
- Department of Medicine and General Cytometry Service-Nucleus, CIBERONC CB16/12/00400, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), IBSAL, University of Salamanca-CSIC, Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain; (L.C.-G.); (O.G.-S.); (C.A.-H.); (P.J.-V.); (M.F.)
| | - Carlota Arias-Hidalgo
- Department of Medicine and General Cytometry Service-Nucleus, CIBERONC CB16/12/00400, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), IBSAL, University of Salamanca-CSIC, Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain; (L.C.-G.); (O.G.-S.); (C.A.-H.); (P.J.-V.); (M.F.)
| | - Pablo Juanes-Velasco
- Department of Medicine and General Cytometry Service-Nucleus, CIBERONC CB16/12/00400, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), IBSAL, University of Salamanca-CSIC, Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain; (L.C.-G.); (O.G.-S.); (C.A.-H.); (P.J.-V.); (M.F.)
| | - Pablo A. García
- Department of Pharmaceutical Sciences, Laboratory of Medicinal Chemistry, Faculty of Pharmacy, University of Salamanca, Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain; (P.A.G.); (M.Á.C.)
| | - Mª Ángeles Castro
- Department of Pharmaceutical Sciences, Laboratory of Medicinal Chemistry, Faculty of Pharmacy, University of Salamanca, Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain; (P.A.G.); (M.Á.C.)
| | - Manuel Fuentes
- Department of Medicine and General Cytometry Service-Nucleus, CIBERONC CB16/12/00400, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), IBSAL, University of Salamanca-CSIC, Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain; (L.C.-G.); (O.G.-S.); (C.A.-H.); (P.J.-V.); (M.F.)
- Proteomics Unit, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
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Jose A, Kulkarni P, Thilakan J, Munisamy M, Malhotra AG, Singh J, Kumar A, Rangnekar VM, Arya N, Rao M. Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine. Mol Cancer 2024; 23:50. [PMID: 38461268 PMCID: PMC10924370 DOI: 10.1186/s12943-023-01916-6] [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: 08/05/2023] [Accepted: 12/15/2023] [Indexed: 03/11/2024] Open
Abstract
Despite advancements in treatment protocols, cancer is one of the leading cause of deaths worldwide. Therefore, there is a need to identify newer and personalized therapeutic targets along with screening technologies to combat cancer. With the advent of pan-omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and lipidomics, the scientific community has witnessed an improved molecular and metabolomic understanding of various diseases, including cancer. In addition, three-dimensional (3-D) disease models have been efficiently utilized for understanding disease pathophysiology and as screening tools in drug discovery. An integrated approach utilizing pan-omics technologies and 3-D in vitro tumor models has led to improved understanding of the intricate network encompassing various signalling pathways and molecular cross-talk in solid tumors. In the present review, we underscore the current trends in omics technologies and highlight their role in understanding genotypic-phenotypic co-relation in cancer with respect to 3-D in vitro tumor models. We further discuss the challenges associated with omics technologies and provide our outlook on the future applications of these technologies in drug discovery and precision medicine for improved management of cancer.
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Affiliation(s)
- Anmi Jose
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Pallavi Kulkarni
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Jaya Thilakan
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Murali Munisamy
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Anvita Gupta Malhotra
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Jitendra Singh
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Ashok Kumar
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Vivek M Rangnekar
- Markey Cancer Center and Department of Radiation Medicine, University of Kentucky, Lexington, KY, 40536, USA
| | - Neha Arya
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India.
| | - Mahadev Rao
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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14
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Ahn HM, Park I, Kim CG, Ko YK, Gim JA. Factors related to tumor response rate from TCGA three omics data-variants, expression, methylation. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, TOXICOLOGY AND CARCINOGENESIS 2024; 42:173-188. [PMID: 38409772 DOI: 10.1080/26896583.2024.2319010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
The Cancer Genome Atlas (TCGA) and its patient-derived multi-omics datasets have been the backbone of cancer research, and with novel approaches, it continues to shed new insight into the disease. In this study, we delved into a method of multi-omics integration of patient datasets and the association of biological pathways related to the disease. First, across thirty-three types of cancer present in TCGA, we merged genomic mutations and drug response datasets and filtered for the viable variant-drug response combinations available in TCGA, containing more than three samples for each drug response label with RNA sequencing (RNA-seq) and genomic methylation data available for each patient. We identified two distinct combinations in TCGA, one being pancreatic adenocarcinoma patients with/without rs121913529 variant in KRAS gene treated with gemcitabine, and the other low-grade glioma with/without rs121913500 variant in IDH1 gene administered with temozolomide. In these two groups, different patterns of gene expression were observed in the pathways often associated with cancer progression, such as mTOR and PDGF between the patients with complete response and progressive disease. Our result will provide yet another example of the relevance of these biological pathways in cancer drug response and a way for multi-omics integration in cancer datasets.
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Affiliation(s)
- Hyung-Min Ahn
- Center for Lung Cancer, National Cancer Center Hospital, Goyang, South Korea
- BK21 Graduate Program, Department of Biomedical Sciences, Korea University College of Medicine, Seoul, South Korea
| | - Insu Park
- BK21 Graduate Program, Department of Biomedical Sciences, Korea University College of Medicine, Seoul, South Korea
- Department of Laboratory Medicine, Korea University Guro Hospital, Seoul, South Korea
| | - Chang Geun Kim
- Center for Lung Cancer, National Cancer Center Hospital, Goyang, South Korea
- Department of Thoracic and Cardiovascular Surgery, Korea University Guro Hospital, Seoul, South Korea
| | - Young Kyung Ko
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Seoul, South Korea
| | - Jeong-An Gim
- Department of Medical Science, Soonchunhyang University, Asan, South Korea
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15
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Maniam S, Maniam S. Screening Techniques for Drug Discovery in Alzheimer's Disease. ACS OMEGA 2024; 9:6059-6073. [PMID: 38371787 PMCID: PMC10870277 DOI: 10.1021/acsomega.3c07046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/22/2023] [Accepted: 12/25/2023] [Indexed: 02/20/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive and irreversible impairment of memory and other cognitive functions of the aging brain. Pathways such as amyloid beta neurotoxicity, tau pathogenesis and neuroinflammatory have been used to understand AD, despite not knowing the definite molecular mechanism which causes this progressive disease. This review attempts to summarize the small molecules that target these pathways using various techniques involving high-throughput screening, molecular modeling, custom bioassays, and spectroscopic detection tools. Novel and evolving screening methods developed to advance drug discovery initiatives in AD research are also highlighted.
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Affiliation(s)
- Sandra Maniam
- Department
of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia
| | - Subashani Maniam
- School
of Science, STEM College, RMIT University, Melbourne, Victoria 3001, Australia
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16
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Verma A, Patel R, Mahale A, Thorat RV, Rath SL, Sridhar E, Moiyadi A, Srivastava S. Multitarget Potential Drug Candidates for High-Grade Gliomas Identified by Multiple Reaction Monitoring Coupled with In Silico Drug Repurposing. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:59-75. [PMID: 38320249 DOI: 10.1089/omi.2023.0256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
High-grade gliomas (HGGs) are extremely aggressive primary brain tumors with high mortality rates. Despite notable progress achieved by clinical research and biomarkers emerging from proteomics studies, efficacious drugs and therapeutic targets are limited. This study used targeted proteomics, in silico molecular docking, and simulation-based drug repurposing to identify potential drug candidates for HGGs. Importantly, we performed multiple reaction monitoring (MRM) on differentially expressed proteins with putative roles in the development and progression of HGGs based on our previous work and the published literature. Furthermore, in silico molecular docking-based drug repurposing was performed with a customized library of FDA-approved drugs to identify multitarget-directed ligands. The top drug candidates such as Pazopanib, Icotinib, Entrectinib, Regorafenib, and Cabozantinib were explored for their drug-likeness properties using the SwissADME. Pazopanib exhibited binding affinities with a maximum number of proteins and was considered for molecular dynamic simulations and cell toxicity assays. HGG cell lines showed enhanced cytotoxicity and cell proliferation inhibition with Pazopanib and Temozolomide combinatorial treatment compared to Temozolomide alone. To the best of our knowledge, this is the first study combining MRM with molecular docking and simulation-based drug repurposing to identify potential drug candidates for HGG. While the present study identified five multitarget-directed potential drug candidates, future clinical studies in larger cohorts are crucial to evaluate the efficacy of these molecular candidates. The research strategy and methodology used in the present study offer new avenues for innovation in drug discovery and development which may prove useful, particularly for cancers with low cure rates.
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Affiliation(s)
- Ayushi Verma
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
| | - Rushda Patel
- Sinhgad College of Pharmacy, Savitribai Phule Pune University, Pune, India
| | - Atharva Mahale
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal, India
| | - Rujuta Vijay Thorat
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Soumya Lipsa Rath
- Department of Biotechnology, National Institute of Technology, Warangal, India
| | - Epari Sridhar
- Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Navi Mumbai, India
| | - Aliasgar Moiyadi
- Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Navi Mumbai, India
| | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
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17
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Kong X, Liu C, Zhang Z, Cheng M, Mei Z, Li X, Liu P, Diao L, Ma Y, Jiang P, Kong X, Nie S, Guo Y, Wang Z, Zhang X, Wang Y, Tang L, Guo S, Liu Z, Li D. BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins. Nucleic Acids Res 2024; 52:D1110-D1120. [PMID: 37904598 PMCID: PMC10767940 DOI: 10.1093/nar/gkad926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/24/2023] [Accepted: 10/09/2023] [Indexed: 11/01/2023] Open
Abstract
Traditional Chinese medicine (TCM) is increasingly recognized and utilized worldwide. However, the complex ingredients of TCM and their interactions with the human body make elucidating molecular mechanisms challenging, which greatly hinders the modernization of TCM. In 2016, we developed BATMAN-TCM 1.0, which is an integrated database of TCM ingredient-target protein interaction (TTI) for pharmacology research. Here, to address the growing need for a higher coverage TTI dataset, and using omics data to screen active TCM ingredients or herbs for complex disease treatment, we updated BATMAN-TCM to version 2.0 (http://bionet.ncpsb.org.cn/batman-tcm/). Using the same protocol as version 1.0, we collected 17 068 known TTIs by manual curation (with a 62.3-fold increase), and predicted ∼2.3 million high-confidence TTIs. In addition, we incorporated three new features into the updated version: (i) it enables simultaneous exploration of the target of TCM ingredient for pharmacology research and TCM ingredients binding to target proteins for drug discovery; (ii) it has significantly expanded TTI coverage; and (iii) the website was redesigned for better user experience and higher speed. We believe that BATMAN-TCM 2.0, as a discovery repository, will contribute to the study of TCM molecular mechanisms and the development of new drugs for complex diseases.
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Affiliation(s)
- Xiangren Kong
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Chao Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Zuzhen Zhang
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
| | - Meiqi Cheng
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Zhijun Mei
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Xiangdong Li
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Peng Liu
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Lihong Diao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yajie Ma
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Peng Jiang
- Beijing Geneworks Technology Co., Ltd, Beijing 100101, China
| | - Xiangya Kong
- Beijing Geneworks Technology Co., Ltd, Beijing 100101, China
| | - Shiyan Nie
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yingzi Guo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Ze Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xinlei Zhang
- Beijing Geneworks Technology Co., Ltd, Beijing 100101, China
| | - Yan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Liujun Tang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Shuzhen Guo
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Zhongyang Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Dong Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
- College of Life Sciences, Hebei University, Baoding 071002, China
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18
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Uatay A, Gall L, Irons L, Tewari SG, Zhu XS, Gibbs M, Kimko H. Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model. J Pharm Sci 2024; 113:11-21. [PMID: 37898164 DOI: 10.1016/j.xphs.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/21/2023] [Accepted: 10/21/2023] [Indexed: 10/30/2023]
Abstract
Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development.
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Affiliation(s)
- Aydar Uatay
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Cambridge, United Kingdom.
| | - Louis Gall
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Cambridge, United Kingdom
| | - Linda Irons
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Shivendra G Tewari
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Gaithersburg, MD, United States
| | - Xu Sue Zhu
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Megan Gibbs
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Gaithersburg, MD, United States.
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19
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Halder A, Drummond E. Strategies for translating proteomics discoveries into drug discovery for dementia. Neural Regen Res 2024; 19:132-139. [PMID: 37488854 PMCID: PMC10479849 DOI: 10.4103/1673-5374.373681] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/25/2023] [Accepted: 04/06/2023] [Indexed: 07/26/2023] Open
Abstract
Tauopathies, diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of frontotemporal dementia, make up the vast majority of dementia cases. Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments, ongoing progress is required to ensure these are effective, economical, and accessible for the globally ageing population. As such, continued identification of new potential drug targets and biomarkers is critical. "Big data" studies, such as proteomics, can generate information on thousands of possible new targets for dementia diagnostics and therapeutics, but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development. In this review, we discuss current tauopathy biomarkers and therapeutics, and highlight areas in need of improvement, particularly when addressing the needs of frail, comorbid and cognitively impaired populations. We highlight biomarkers which have been developed from proteomic data, and outline possible future directions in this field. We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development, and demonstrate its application to our group's recent tau interactome dataset as an example.
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Affiliation(s)
- Aditi Halder
- School of Medical Sciences and Brain & Mind Center, University of Sydney, NSW, Sydney, Australia
- Department of Aged Care, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Eleanor Drummond
- School of Medical Sciences and Brain & Mind Center, University of Sydney, NSW, Sydney, Australia
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20
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Nazli A, Qiu J, Tang Z, He Y. Recent Advances and Techniques for Identifying Novel Antibacterial Targets. Curr Med Chem 2024; 31:464-501. [PMID: 36734893 DOI: 10.2174/0929867330666230123143458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/30/2022] [Accepted: 11/11/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly. METHODS In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification. RESULTS Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well. CONCLUSION The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.
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Affiliation(s)
- Adila Nazli
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, P. R. China
| | - Jingyi Qiu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Chongqing, 400714, P. R. China
| | - Ziyi Tang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Chongqing, 400714, P. R. China
| | - Yun He
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, P. R. China
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21
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Matsumoto H, Ogura H, Oda J. Analysis of comprehensive biomolecules in critically ill patients via bioinformatics technologies. Acute Med Surg 2024; 11:e944. [PMID: 38596160 PMCID: PMC11002317 DOI: 10.1002/ams2.944] [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: 10/11/2023] [Revised: 02/23/2024] [Accepted: 03/10/2024] [Indexed: 04/11/2024] Open
Abstract
Each patient with a critical illness such as sepsis and severe trauma has a different genetic background, comorbidities, age, and sex. Moreover, pathophysiology changes dynamically over time even in the same patient. Therefore, individualized treatment is necessary to account for heterogeneity in patient backgrounds. Recently, the analysis of comprehensive biomolecular information using clinical specimens has revealed novel molecular pathological classifications called subtypes. In addition, comprehensive biomolecular information using clinical specimens has enabled reverse translational research, which is a data-driven approach to the identification of drug target molecules. The development of these methods is expected to visualize the heterogeneity of patient backgrounds and lead to personalized therapy.
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Affiliation(s)
- Hisatake Matsumoto
- Department of Traumatology and Acute Critical MedicineOsaka University Graduate School of MedicineSuitaOsakaJapan
| | - Hiroshi Ogura
- Department of Traumatology and Acute Critical MedicineOsaka University Graduate School of MedicineSuitaOsakaJapan
| | - Jun Oda
- Department of Traumatology and Acute Critical MedicineOsaka University Graduate School of MedicineSuitaOsakaJapan
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22
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Biswas A, Kumari A, Gaikwad DS, Pandey DK. Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:550-569. [PMID: 38100404 DOI: 10.1089/omi.2023.0197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.
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Affiliation(s)
- Aakanksha Biswas
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - Aditi Kumari
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - D S Gaikwad
- Amity Institute of Organic Agriculture, Amity University, Noida, India
| | - Dhananjay K Pandey
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
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Theuretzbacher U, Blasco B, Duffey M, Piddock LJV. Unrealized targets in the discovery of antibiotics for Gram-negative bacterial infections. Nat Rev Drug Discov 2023; 22:957-975. [PMID: 37833553 DOI: 10.1038/s41573-023-00791-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/15/2023] [Indexed: 10/15/2023]
Abstract
Advances in areas that include genomics, systems biology, protein structure determination and artificial intelligence provide new opportunities for target-based antibacterial drug discovery. The selection of a 'good' new target for direct-acting antibacterial compounds is the first decision, for which multiple criteria must be explored, integrated and re-evaluated as drug discovery programmes progress. Criteria include essentiality of the target for bacterial survival, its conservation across different strains of the same species, bacterial species and growth conditions (which determines the spectrum of activity of a potential antibiotic) and the level of homology with human genes (which influences the potential for selective inhibition). Additionally, a bacterial target should have the potential to bind to drug-like molecules, and its subcellular location will govern the need for inhibitors to penetrate one or two bacterial membranes, which is a key challenge in targeting Gram-negative bacteria. The risk of the emergence of target-based drug resistance for drugs with single targets also requires consideration. This Review describes promising but as-yet-unrealized targets for antibacterial drugs against Gram-negative bacteria and examples of cognate inhibitors, and highlights lessons learned from past drug discovery programmes.
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Affiliation(s)
| | - Benjamin Blasco
- Global Antibiotic Research and Development Partnership (GARDP), Geneva, Switzerland
| | - Maëlle Duffey
- Global Antibiotic Research and Development Partnership (GARDP), Geneva, Switzerland
| | - Laura J V Piddock
- Global Antibiotic Research and Development Partnership (GARDP), Geneva, Switzerland.
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24
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Noberini R, Bonaldi T. Proteomics contributions to epigenetic drug discovery. Proteomics 2023; 23:e2200435. [PMID: 37727062 DOI: 10.1002/pmic.202200435] [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: 06/09/2023] [Revised: 08/30/2023] [Accepted: 08/31/2023] [Indexed: 09/21/2023]
Abstract
The combined activity of epigenetic features, which include histone post-translational modifications, DNA methylation, and nucleosome positioning, regulates gene expression independently from changes in the DNA sequence, defining how the shared genetic information of an organism is used to generate different cell phenotypes. Alterations in epigenetic processes have been linked with a multitude of diseases, including cancer, fueling interest in the discovery of drugs targeting the proteins responsible for writing, erasing, or reading histone and DNA modifications. Mass spectrometry (MS)-based proteomics has emerged as a versatile tool that can assist drug discovery pipelines from target validation, through target deconvolution, to monitoring drug efficacy in vivo. Here, we provide an overview of the contributions of MS-based proteomics to epigenetic drug discovery, describing the main approaches that can be used to support different drug discovery pipelines and highlighting how they contributed to the development and characterization of epigenetic drugs.
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Affiliation(s)
- Roberta Noberini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Tiziana Bonaldi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hematology-Oncology, University of Milan, Milan, Italy
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25
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Bukva M, Dobra G, Gyukity-Sebestyen E, Boroczky T, Korsos MM, Meckes DG, Horvath P, Buzas K, Harmati M. Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis. Cell Commun Signal 2023; 21:333. [PMID: 37986165 PMCID: PMC10658864 DOI: 10.1186/s12964-023-01344-5] [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: 08/17/2023] [Accepted: 09/27/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods. METHODS On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas. RESULTS Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes. CONCLUSION Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.
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Affiliation(s)
- Matyas Bukva
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, Albert Szent-Györgyi Medical School, University of Szeged, 6720, Szeged, Hungary
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Gabriella Dobra
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, Albert Szent-Györgyi Medical School, University of Szeged, 6720, Szeged, Hungary
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Edina Gyukity-Sebestyen
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Timea Boroczky
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, Albert Szent-Györgyi Medical School, University of Szeged, 6720, Szeged, Hungary
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Marietta Margareta Korsos
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
| | - David G Meckes
- Department of Biomedical Sciences, Florida State University College of Medicine, Tallahassee, FL, 32306, USA
| | - Peter Horvath
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Krisztina Buzas
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Maria Harmati
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary.
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary.
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Li P, Ma X, Gu X. LncRNA MAFG-AS1 is involved in human cancer progression. Eur J Med Res 2023; 28:497. [PMID: 37941063 PMCID: PMC10631199 DOI: 10.1186/s40001-023-01486-9] [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: 10/12/2023] [Accepted: 10/30/2023] [Indexed: 11/10/2023] Open
Abstract
Long noncoding RNAs (lncRNAs) refer to a type of non-protein-coding transcript of more than 200 nucleotides. LncRNAs play fundamental roles in disease development and progression, and lncRNAs are dysregulated in many pathophysiological processes. Thus, lncRNAs may have potential value in clinical applications. The lncRNA, MAF BZIP Transcription Factor G (MAFG)-AS1, is dysregulated in several cancer, including breast cancer, lung cancer, liver cancer, bladder cancer, colorectal cancer, gastric cancer, esophagus cancer, prostate cancer, pancreatic cancer, ovarian cancer, and glioma. Altered MAFG-AS1 levels are also associated with diverse clinical characteristics and patient outcomes. Mechanistically, MAFG-AS1 mediates a variety of cellular processes via the regulation of target gene expression. Therefore, the diagnostic, prognostic, and therapeutic aspects of MAFG-AS1 have been widely explored. In this review, we discuss the expression, major roles, and molecular mechanisms of MAFG-AS1, the relationship between MAFG-AS1 and clinical features of diseases, and the clinical applications of MAFG-AS1.
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Affiliation(s)
- Penghui Li
- Department of Oncology, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang, 471000, Henan, China
| | - Xiao Ma
- Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
| | - Xinyu Gu
- Department of Oncology, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang, 471000, Henan, China.
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Kim N, Kim MH, Pyo J, Lee SM, Jang JS, Lee DW, Kim KW. CCR8 as a Therapeutic Novel Target: Omics-Integrated Comprehensive Analysis for Systematically Prioritizing Indications. Biomedicines 2023; 11:2910. [PMID: 38001911 PMCID: PMC10669377 DOI: 10.3390/biomedicines11112910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/10/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
Target identification is a crucial process in drug development, aiming to identify key proteins, genes, and signal pathways involved in disease progression and their relevance in potential therapeutic interventions. While C-C chemokine receptor 8 (CCR8) has been investigated as a candidate anti-cancer target, comprehensive multi-omics analyzes across various indications are limited. In this study, we conducted an extensive bioinformatics analysis integrating genomics, proteomics, and transcriptomics data to establish CCR8 as a promising anti-cancer drug target. Our approach encompassed data collection from diverse knowledge resources, gene function analysis, differential gene expression profiling, immune cell infiltration assessment, and strategic prioritization of target indications. Our findings revealed strong correlations between CCR8 and specific cancers, notably Breast Invasive Carcinoma (BRCA), Colon Adenocarcinoma (COAD), Head and Neck Squamous Cell Carcinoma (HNSC), Rectum adenocarcinoma (READ), Stomach adenocarcinoma (STAD), and Thyroid carcinoma (THCA). This research advances our understanding of CCR8 as a potential target for anti-cancer drug development, bridging the gap between molecular insights and creating opportunities for personalized treatment of solid tumors.
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Affiliation(s)
- Nari Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea;
| | - Mi-Hyun Kim
- Research Institute, Trial Informatics Inc., Seoul 05544, Republic of Korea;
| | - Junhee Pyo
- College of Pharmacy, Chungbuk National University, Cheongju 28644, Republic of Korea;
| | - Soo-Min Lee
- Samjin Pharmaceutical Co., Ltd., Seoul 04054, Republic of Korea;
| | - Ji-Sung Jang
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Republic of Korea;
| | - Do-Wan Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea;
| | - Kyung Won Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea;
- Research Institute, Trial Informatics Inc., Seoul 05544, Republic of Korea;
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea;
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Ivanisevic T, Sewduth RN. Multi-Omics Integration for the Design of Novel Therapies and the Identification of Novel Biomarkers. Proteomes 2023; 11:34. [PMID: 37873876 PMCID: PMC10594525 DOI: 10.3390/proteomes11040034] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 10/25/2023] Open
Abstract
Multi-omics is a cutting-edge approach that combines data from different biomolecular levels, such as DNA, RNA, proteins, metabolites, and epigenetic marks, to obtain a holistic view of how living systems work and interact. Multi-omics has been used for various purposes in biomedical research, such as identifying new diseases, discovering new drugs, personalizing treatments, and optimizing therapies. This review summarizes the latest progress and challenges of multi-omics for designing new treatments for human diseases, focusing on how to integrate and analyze multiple proteome data and examples of how to use multi-proteomics data to identify new drug targets. We also discussed the future directions and opportunities of multi-omics for developing innovative and effective therapies by deciphering proteome complexity.
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Affiliation(s)
| | - Raj N. Sewduth
- VIB-KU Leuven Center for Cancer Biology (VIB), 3000 Leuven, Belgium;
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29
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Gu R, Wu F, Huang Z. Role of Computer-Aided Drug Design in Drug Development. Molecules 2023; 28:7160. [PMID: 37894639 PMCID: PMC10609497 DOI: 10.3390/molecules28207160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
The introduction of computational techniques to pharmaceutical chemistry and molecular biology in the 20th century has changed the way people develop drugs [...].
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Affiliation(s)
- Ruoxu Gu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Fengxu Wu
- Hubei Key Laboratory of Wudang Local Chinese Medicine Research, School of Pharmaceutical Sciences, Hubei University of Medicine, Shiyan 442000, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
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30
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Dobbs Spendlove M, M. Gibson T, McCain S, Stone BC, Gill T, Pickett BE. Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets. PeerJ 2023; 11:e16088. [PMID: 37790614 PMCID: PMC10544355 DOI: 10.7717/peerj.16088] [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: 03/28/2023] [Accepted: 08/22/2023] [Indexed: 10/05/2023] Open
Abstract
Background Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.
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Affiliation(s)
- Mauri Dobbs Spendlove
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Trenton M. Gibson
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Shaney McCain
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Benjamin C. Stone
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | | | - Brett E. Pickett
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
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31
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Tian S, Li Y, Xu J, Zhang L, Zhang J, Lu J, Xu X, Luan X, Zhao J, Zhang W. COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming. Brief Bioinform 2023; 24:bbad346. [PMID: 37816138 PMCID: PMC10564268 DOI: 10.1093/bib/bbad346] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/16/2023] [Accepted: 09/13/2023] [Indexed: 10/12/2023] Open
Abstract
Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.
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Affiliation(s)
- Saisai Tian
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Yanan Li
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Jia Xu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
- College of Pharmacy, Henan University, Kaifeng 475000, China
| | - Lijun Zhang
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Jinbo Zhang
- Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China Department of Pharmacy, Tianjin Rehabilitation Center of Joint Logistics Support Force, Tianjin, 300110, China
| | - Jinyuan Lu
- College of Pharmacy, Anhui University of Chinese Medicine, Anhui 230012, China
| | - Xike Xu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Xin Luan
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Jing Zhao
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Weidong Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
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Yang X, Zhang B, Wang S, Lu Y, Chen K, Luo C, Sun A, Zhang H. OTTM: an automated classification tool for translational drug discovery from omics data. Brief Bioinform 2023; 24:bbad301. [PMID: 37594310 PMCID: PMC10516341 DOI: 10.1093/bib/bbad301] [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: 05/29/2023] [Revised: 07/22/2023] [Accepted: 07/31/2023] [Indexed: 08/19/2023] Open
Abstract
Omics data from clinical samples are the predominant source of target discovery and drug development. Typically, hundreds or thousands of differentially expressed genes or proteins can be identified from omics data. This scale of possibilities is overwhelming for target discovery and validation using biochemical or cellular experiments. Most of these proteins and genes have no corresponding drugs or even active compounds. Moreover, a proportion of them may have been previously reported as being relevant to the disease of interest. To facilitate translational drug discovery from omics data, we have developed a new classification tool named Omics and Text driven Translational Medicine (OTTM). This tool can markedly narrow the range of proteins or genes that merit further validation via drug availability assessment and literature mining. For the 4489 candidate proteins identified in our previous proteomics study, OTTM recommended 40 FDA-approved or clinical trial drugs. Of these, 15 are available commercially and were tested on hepatocellular carcinoma Hep-G2 cells. Two drugs-tafenoquine succinate (an FDA-approved antimalarial drug targeting CYC1) and branaplam (a Phase 3 clinical drug targeting SMN1 for the treatment of spinal muscular atrophy)-showed potent inhibitory activity against Hep-G2 cell viability, suggesting that CYC1 and SMN1 may be potential therapeutic target proteins for hepatocellular carcinoma. In summary, OTTM is an efficient classification tool that can accelerate the discovery of effective drugs and targets using thousands of candidate proteins identified from omics data. The online and local versions of OTTM are available at http://otter-simm.com/ottm.html.
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Affiliation(s)
- Xiaobo Yang
- ShanghaiTech University
- School of Life Science and Technology, ShanghaiTech University, 393 Huaxiazhong Road, Shanghai 200031, China
| | - Bei Zhang
- Shanghai Institute of Materia Medica
- Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Siqi Wang
- Beijing Proteome Research Center
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, and National Center for Protein Sciences (Beijing)
| | - Ye Lu
- Nanjing University of Chinese Medicine
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China; Chemical Biology Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Kaixian Chen
- academician medicinal scientist of the Chinese Academy of Sciences
- Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China; School of Life Science and Technology, ShanghaiTech University, 393 Huaxiazhong Road, Shanghai 200031, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Cheng Luo
- Shanghai Institute of Materia Medica
- Chemical Biology Research Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Aihua Sun
- Beijing Proteome Research Center
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics; Research Unit of Proteomics-driven Cancer Precision Medicine, Chinese Academy of Medical Sciences
| | - Hao Zhang
- Shanghai Institute of Materia Medica
- Chemical Biology Research Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
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Chakraborty C, Bhattacharya M, Lee SS. Artificial intelligence enabled ChatGPT and large language models in drug target discovery, drug discovery, and development. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 33:866-868. [PMID: 37680991 PMCID: PMC10481150 DOI: 10.1016/j.omtn.2023.08.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha 756020, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si, Gangwon-do 24252, Republic of Korea
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Vishweswaraiah S, Yilmaz A, Saiyed N, Khalid A, Koladiya PR, Pan X, Macias S, Robinson AC, Mann D, Green BD, Kerševičiūte I, Gordevičius J, Radhakrishna U, Graham SF. Integrative Analysis Unveils the Correlation of Aminoacyl-tRNA Biosynthesis Metabolites with the Methylation of the SEPSECS Gene in Huntington's Disease Brain Tissue. Genes (Basel) 2023; 14:1752. [PMID: 37761892 PMCID: PMC10530570 DOI: 10.3390/genes14091752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington's disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue (striatum and frontal lobe), and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj p-value = 0.0098) was the most significantly perturbed metabolic pathway with which two CpGs of the SEPSECS gene were correlated. This study improves our understanding of molecular biomarker connections and, importantly, increases our knowledge of metabolic alterations driving HD progression.
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Affiliation(s)
- Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (S.V.); (U.R.)
| | - Ali Yilmaz
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Nazia Saiyed
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Abdullah Khalid
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Purvesh R. Koladiya
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Xiaobei Pan
- Advanced Asset Technology Centre, Institute for Global Food Security, Queen’s University Belfast, Belfast BT9 5DL, UK; (X.P.); (S.M.); (B.D.G.)
| | - Shirin Macias
- Advanced Asset Technology Centre, Institute for Global Food Security, Queen’s University Belfast, Belfast BT9 5DL, UK; (X.P.); (S.M.); (B.D.G.)
| | - Andrew C. Robinson
- Faculty of Biology, Medicine and Health, School of Biological Sciences, Division of Neuroscience, The University of Manchester, Salford Royal Hospital, Salford M6 8HD, UK; (A.C.R.); (D.M.)
| | - David Mann
- Faculty of Biology, Medicine and Health, School of Biological Sciences, Division of Neuroscience, The University of Manchester, Salford Royal Hospital, Salford M6 8HD, UK; (A.C.R.); (D.M.)
| | - Brian D. Green
- Advanced Asset Technology Centre, Institute for Global Food Security, Queen’s University Belfast, Belfast BT9 5DL, UK; (X.P.); (S.M.); (B.D.G.)
| | - Ieva Kerševičiūte
- VUGENE, LLC, 625 Kenmoor Ave Suite 301 PMB 96578, Grand Rapids, MI 49546, USA; (I.K.); (J.G.)
| | - Juozas Gordevičius
- VUGENE, LLC, 625 Kenmoor Ave Suite 301 PMB 96578, Grand Rapids, MI 49546, USA; (I.K.); (J.G.)
| | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (S.V.); (U.R.)
| | - Stewart F. Graham
- Department of Obstetrics and Gynecology, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (S.V.); (U.R.)
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Rochester, MI 48309, USA
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35
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Zhang J, Qiu Z, Zhang Y, Wang G, Hao H. Intracellular spatiotemporal metabolism in connection to target engagement. Adv Drug Deliv Rev 2023; 200:115024. [PMID: 37516411 DOI: 10.1016/j.addr.2023.115024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/05/2023] [Accepted: 07/26/2023] [Indexed: 07/31/2023]
Abstract
The metabolism in eukaryotic cells is a highly ordered system involving various cellular compartments, which fluctuates based on physiological rhythms. Organelles, as the smallest independent sub-cell unit, are important contributors to cell metabolism and drug metabolism, collectively designated intracellular metabolism. However, disruption of intracellular spatiotemporal metabolism can lead to disease development and progression, as well as drug treatment interference. In this review, we systematically discuss spatiotemporal metabolism in cells and cell subpopulations. In particular, we focused on metabolism compartmentalization and physiological rhythms, including the variation and regulation of metabolic enzymes, metabolic pathways, and metabolites. Additionally, the intricate relationship among intracellular spatiotemporal metabolism, metabolism-related diseases, and drug therapy/toxicity has been discussed. Finally, approaches and strategies for intracellular spatiotemporal metabolism analysis and potential target identification are introduced, along with examples of potential new drug design based on this.
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Affiliation(s)
- Jingwei Zhang
- State Key Laboratory of Natural Medicines, Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, China
| | - Zhixia Qiu
- Center of Drug Metabolism and Pharmacokinetics, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yongjie Zhang
- Clinical Pharmacokinetics Laboratory, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Guangji Wang
- State Key Laboratory of Natural Medicines, Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, China; Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, Research Unit of PK-PD Based Bioactive Components and Pharmacodynamic Target Discovery of Natural Medicine of Chinese Academy of Medical Sciences, China Pharmaceutical University, Nanjing, China.
| | - Haiping Hao
- State Key Laboratory of Natural Medicines, Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, China.
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36
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Dash R, Yadav M, Biswal J, Samanta S, Sharma T, Mohapatra S. Drug repurposing a compelling cancer strategy with bottomless opportunities: Recent advancements in computational methods and molecular mechanisms. Indian J Pharmacol 2023; 55:322-331. [PMID: 37929411 PMCID: PMC10751526 DOI: 10.4103/ijp.ijp_626_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 08/05/2023] [Accepted: 08/29/2023] [Indexed: 11/07/2023] Open
Abstract
Drug discovery has customarily focused on a de novo design approach, which is extremely expensive and takes several years to evolve before reaching the market. Discovering novel therapeutic benefits for the current drugs could contribute to new treatment alternatives for individuals with complex medical demands that are safe, inexpensive, and timely. In this consequence, when pharmaceutically yield and oncology drug efficacy appear to have hit a stalemate, drug repurposing is a fascinating method for improving cancer treatment. This review gathered about how in silico drug repurposing offers the opportunity to quickly increase the anticancer drug arsenal and, more importantly, overcome some of the limits of existing cancer therapies against both old and new therapeutic targets in oncology. The ancient nononcology compounds' innovative potential targets and important signaling pathways in cancer therapy are also discussed. This review also includes many plant-derived chemical compounds that have shown potential anticancer properties in recent years. Here, we have also tried to bring the spotlight on the new mechanisms to support clinical research, which may become increasingly essential in the future; at the same time, the unsolved or failed clinical trial study should be reinvestigated further based on the techniques and information provided. These encouraging findings, combined together, will through new insight on repurposing more non-oncology drugs for the treatment of cancer.
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Affiliation(s)
- Rasmita Dash
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
- Department of Pharmaceutics, School of Pharmacy and Life Sciences, Centurion University of Technology and Management, Bhubaneswar, Odisha, India
| | - Madhulika Yadav
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Jyotirmaya Biswal
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Shrabani Samanta
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Tripti Sharma
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Sujata Mohapatra
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
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37
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Vallés-Martí A, Mantini G, Manoukian P, Waasdorp C, Sarasqueta AF, de Goeij-de Haas RR, Henneman AA, Piersma SR, Pham TV, Knol JC, Giovannetti E, Bijlsma MF, Jiménez CR. Phosphoproteomics guides effective low-dose drug combinations against pancreatic ductal adenocarcinoma. Cell Rep 2023; 42:112581. [PMID: 37269289 DOI: 10.1016/j.celrep.2023.112581] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 04/04/2023] [Accepted: 05/16/2023] [Indexed: 06/05/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited set of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a readout of aberrant signaling and has the potential to identify new targets and guide treatment decisions. Using two-step sequential phosphopeptide enrichment, we generate a comprehensive phosphoproteome and proteome of nine PDAC cell lines, encompassing more than 20,000 phosphosites on 5,763 phospho-proteins, including 316 protein kinases. By using integrative inferred kinase activity (INKA) scoring, we identify multiple (parallel) activated kinases that are subsequently matched to kinase inhibitors. Compared with high-dose single-drug treatments, INKA-tailored low-dose 3-drug combinations against multiple targets demonstrate superior efficacy against PDAC cell lines, organoid cultures, and patient-derived xenografts. Overall, this approach is particularly more effective against the aggressive mesenchymal PDAC model compared with the epithelial model in both preclinical settings and may contribute to improved treatment outcomes in PDAC patients.
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Affiliation(s)
- Andrea Vallés-Martí
- Amsterdam University Medical Center, VU University, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, OncoProteomics Laboratory, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer Biology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Pharmacology Laboratory, Amsterdam, the Netherlands
| | - Giulia Mantini
- Amsterdam University Medical Center, VU University, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, OncoProteomics Laboratory, Amsterdam, the Netherlands; Cancer Center Amsterdam, Pharmacology Laboratory, Amsterdam, the Netherlands; Cancer Pharmacology Lab, AIRC Start-Up Unit, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy
| | - Paul Manoukian
- Cancer Center Amsterdam, Cancer Biology, Amsterdam, the Netherlands; Amsterdam University Medical Center, University of Amsterdam, Center for Experimental and Molecular Medicine, Laboratory for Experimental Oncology and Radiobiology, Amsterdam, the Netherlands
| | - Cynthia Waasdorp
- Cancer Center Amsterdam, Cancer Biology, Amsterdam, the Netherlands; Amsterdam University Medical Center, University of Amsterdam, Center for Experimental and Molecular Medicine, Laboratory for Experimental Oncology and Radiobiology, Amsterdam, the Netherlands
| | | | - Richard R de Goeij-de Haas
- Amsterdam University Medical Center, VU University, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, OncoProteomics Laboratory, Amsterdam, the Netherlands
| | - Alex A Henneman
- Amsterdam University Medical Center, VU University, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, OncoProteomics Laboratory, Amsterdam, the Netherlands
| | - Sander R Piersma
- Amsterdam University Medical Center, VU University, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, OncoProteomics Laboratory, Amsterdam, the Netherlands
| | - Thang V Pham
- Amsterdam University Medical Center, VU University, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, OncoProteomics Laboratory, Amsterdam, the Netherlands
| | - Jaco C Knol
- Amsterdam University Medical Center, VU University, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, OncoProteomics Laboratory, Amsterdam, the Netherlands
| | - Elisa Giovannetti
- Amsterdam University Medical Center, VU University, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Pharmacology Laboratory, Amsterdam, the Netherlands; Cancer Pharmacology Lab, AIRC Start-Up Unit, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy
| | - Maarten F Bijlsma
- Cancer Center Amsterdam, Cancer Biology, Amsterdam, the Netherlands; Amsterdam University Medical Center, University of Amsterdam, Center for Experimental and Molecular Medicine, Laboratory for Experimental Oncology and Radiobiology, Amsterdam, the Netherlands
| | - Connie R Jiménez
- Amsterdam University Medical Center, VU University, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, OncoProteomics Laboratory, Amsterdam, the Netherlands.
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38
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Mathews J, Chang A(J, Devlin L, Levin M. Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine. PATTERNS (NEW YORK, N.Y.) 2023; 4:100737. [PMID: 37223267 PMCID: PMC10201306 DOI: 10.1016/j.patter.2023.100737] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Many aspects of health and disease are modeled using the abstraction of a "pathway"-a set of protein or other subcellular activities with specified functional linkages between them. This metaphor is a paradigmatic case of a deterministic, mechanistic framework that focuses biomedical intervention strategies on altering the members of this network or the up-/down-regulation links between them-rewiring the molecular hardware. However, protein pathways and transcriptional networks exhibit interesting and unexpected capabilities such as trainability (memory) and information processing in a context-sensitive manner. Specifically, they may be amenable to manipulation via their history of stimuli (equivalent to experiences in behavioral science). If true, this would enable a new class of biomedical interventions that target aspects of the dynamic physiological "software" implemented by pathways and gene-regulatory networks. Here, we briefly review clinical and laboratory data that show how high-level cognitive inputs and mechanistic pathway modulation interact to determine outcomes in vivo. Further, we propose an expanded view of pathways from the perspective of basal cognition and argue that a broader understanding of pathways and how they process contextual information across scales will catalyze progress in many areas of physiology and neurobiology. We argue that this fuller understanding of the functionality and tractability of pathways must go beyond a focus on the mechanistic details of protein and drug structure to encompass their physiological history as well as their embedding within higher levels of organization in the organism, with numerous implications for data science addressing health and disease. Exploiting tools and concepts from behavioral and cognitive sciences to explore a proto-cognitive metaphor for the pathways underlying health and disease is more than a philosophical stance on biochemical processes; at stake is a new roadmap for overcoming the limitations of today's pharmacological strategies and for inferring future therapeutic interventions for a wide range of disease states.
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Affiliation(s)
- Juanita Mathews
- Allen Discovery Center at Tufts University, Medford, MA, USA
| | | | - Liam Devlin
- Allen Discovery Center at Tufts University, Medford, MA, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
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Thafar MA, Albaradei S, Uludag M, Alshahrani M, Gojobori T, Essack M, Gao X. OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features. Front Genet 2023; 14:1139626. [PMID: 37091791 PMCID: PMC10117673 DOI: 10.3389/fgene.2023.1139626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 03/24/2023] [Indexed: 04/08/2023] Open
Abstract
Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.
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Affiliation(s)
- Maha A. Thafar
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- College of Computers and Information Technology, Computer Science Department, Taif University, Taif, Saudi Arabia
| | - Somayah Albaradei
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Mona Alshahrani
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- *Correspondence: Xin Gao, ; Magbubah Essack,
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- *Correspondence: Xin Gao, ; Magbubah Essack,
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40
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Messner CB, Demichev V, Wang Z, Hartl J, Kustatscher G, Mülleder M, Ralser M. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023; 23:e2200013. [PMID: 36349817 DOI: 10.1002/pmic.202200013] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.
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Affiliation(s)
- Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Vadim Demichev
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ziyue Wang
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Hartl
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Singh P, Mohsin M, Sultan A, Jha P, Khan MM, Syed MA, Chopra M, Serajuddin M, Rahmani AH, Almatroodi SA, Alrumaihi F, Dohare R. Combined Multiomics and In Silico Approach Uncovers PRKAR1A as a Putative Therapeutic Target in Multi-Organ Dysfunction Syndrome. ACS OMEGA 2023; 8:9555-9568. [PMID: 36936296 PMCID: PMC10018728 DOI: 10.1021/acsomega.3c00020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Despite all epidemiological, clinical, and experimental research efforts, therapeutic concepts in sepsis and sepsis-induced multi-organ dysfunction syndrome (MODS) remain limited and unsatisfactory. Currently, gene expression data sets are widely utilized to discover new biomarkers and therapeutic targets in diseases. In the present study, we analyzed MODS expression profiles (comprising 13 sepsis and 8 control samples) retrieved from NCBI-GEO and found 359 differentially expressed genes (DEGs), among which 170 were downregulated and 189 were upregulated. Next, we employed the weighted gene co-expression network analysis (WGCNA) to establish a MODS-associated gene co-expression network (weighted) and identified representative module genes having an elevated correlation with age. Based on the results, a turquoise module was picked as our hub module. Further, we constructed the PPI network comprising 35 hub module DEGs. The DEGs involved in the highest-confidence PPI network were utilized for collecting pathway and gene ontology (GO) terms using various libraries. Nucleotide di- and triphosphate biosynthesis and interconversion was the most significant pathway. Also, 3 DEGs within our PPI network were involved in the top 5 significantly enriched ontology terms, with hypercortisolism being the most significant term. PRKAR1A was the overlapping gene between top 5 significant pathways and GO terms, respectively. PRKAR1A was considered as a therapeutic target in MODS, and 2992 ligands were screened for binding with PRKAR1A. Among these ligands, 3 molecules based on CDOCKER score (molecular dynamics simulated-based score, which allows us to rank the binding poses according to their quality and to identify the best pose for each system) and crucial interaction with human PRKAR1A coding protein and protein kinase-cyclic nucleotide binding domains (PKA RI alpha CNB-B domain) via active site binding residues, viz. Val283, Val302, Gln304, Val315, Ile327, Ala336, Ala337, Val339, Tyr373, and Asn374, were considered as lead molecules.
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Affiliation(s)
- Prithvi Singh
- Centre
for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Mohd Mohsin
- Department
of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Armiya Sultan
- Department
of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Prakash Jha
- Laboratory
of Molecular Modeling and Anticancer Drug Development, Dr. B. R. Ambedkar
Center for Biomedical Research, University
of Delhi, New Delhi 110007, India
| | - Mohd Mabood Khan
- Department
of Zoology, University of Lucknow, Lucknow, Uttar Pradesh, 226007, India
| | - Mansoor Ali Syed
- Department
of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Madhu Chopra
- Laboratory
of Molecular Modeling and Anticancer Drug Development, Dr. B. R. Ambedkar
Center for Biomedical Research, University
of Delhi, New Delhi 110007, India
| | - Mohammad Serajuddin
- Department
of Zoology, University of Lucknow, Lucknow, Uttar Pradesh, 226007, India
| | - Arshad Husain Rahmani
- Department
of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Saleh A. Almatroodi
- Department
of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Faris Alrumaihi
- Department
of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Ravins Dohare
- Centre
for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
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Chakraborty S, Kannihalli A, Mohanty A, Ray S. The Promises of Proteomics and Metabolomics for Unravelling the Mechanism and Side Effect Landscape of Beta-Adrenoceptor Antagonists in Cardiovascular Therapeutics. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:87-92. [PMID: 36854142 DOI: 10.1089/omi.2023.0003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Cardiovascular medicine witnessed notable advances for the past decade. Multiomics research offers a new lens for precision/personalized medicine for existing and emerging drugs used in the cardiovascular clinic. Beta-blockers are vital in treating hypertension and chronic heart failure. However, clinical use of beta-blockers is also associated with side effects and person-to-person variations in their pharmacokinetics and pharmacodynamics. A comprehensive understanding of the mechanisms that underpin the side effect landscape of beta-blockers is imperative to optimize their therapeutic value. In addition, current research emphasizes the circadian clock's vital roles in regulating pharmacological parameters. Administration of the beta-blockers at specific dosing times could potentially improve their effectiveness and reduce their toxic effects. The rapid development of mass spectrometry technologies with chemical proteomics and thermal proteome profiling methods has also substantially advanced our understanding of underlying side effects mechanisms by unbiased deconvolution of drug targets and off-targets. Metabolomics is steadily demonstrating its utility for conducting mechanistic and toxicological analyses of pharmacological compounds. This article discusses the promises of cutting-edge proteomics and metabolomics approaches to investigate the molecular targets, mechanism of action, adverse effects, and dosing time dependency of beta-blockers.
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Affiliation(s)
| | - Arpita Kannihalli
- Department of Biotechnology, Indian Institute of Technology Hyderabad, Sangareddy, India
| | - Abhishek Mohanty
- Cardiology Department, Continental Hospitals, Nanakaramguda, India
| | - Sandipan Ray
- Department of Biotechnology, Indian Institute of Technology Hyderabad, Sangareddy, India
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Rayinda T, Trisnowati N, Danarti R. Current challenges of genodermatosis in a limited-resource country: An Indonesian perspective. Int J Dermatol 2023; 62:e109-e111. [PMID: 36331132 DOI: 10.1111/ijd.16490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 10/08/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Tuntas Rayinda
- Department of Dermatology and Venereology, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.,Department of Dermatology and Venereology, Dr Sardjito General Hospital, Yogyakarta, Indonesia
| | - Niken Trisnowati
- Department of Dermatology and Venereology, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.,Department of Dermatology and Venereology, Dr Sardjito General Hospital, Yogyakarta, Indonesia
| | - Retno Danarti
- Department of Dermatology and Venereology, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.,Department of Dermatology and Venereology, Dr Sardjito General Hospital, Yogyakarta, Indonesia
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Kuepfer L, Fuellen G, Stahnke T. Quantitative systems pharmacology of the eye: Tools and data for ocular QSP. CPT Pharmacometrics Syst Pharmacol 2023; 12:288-299. [PMID: 36708082 PMCID: PMC10014063 DOI: 10.1002/psp4.12918] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 12/21/2022] [Accepted: 01/02/2023] [Indexed: 01/29/2023] Open
Abstract
Good eyesight belongs to the most-valued attributes of health, and diseases of the eye are a significant healthcare burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PKs) and pharmacodynamics (PDs). Ocular quantitative systems pharmacology models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug-response in specific segments of the eye. In particular, ocular physiologically-based PK (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models using molecular data from specific cellular systems are needed to develop dose-response correlations. We here describe the current status of PK/PBPK as well as PD models for the eyes and discuss cellular systems, data repositories, as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for postoperative fibrosis after glaucoma surgery.
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Affiliation(s)
- Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, Germany
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Aging Research (IBIMA), Rostock University Medical Center, Rostock, Germany
| | - Thomas Stahnke
- Institute for ImplantTechnology and Biomaterials e.V., Rostock, Germany.,Department of Ophthalmology, Rostock University Medical Center, Rostock, Germany
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Dara D, Drabovich AP. Assessment of risks, implications, and opportunities of waterborne neurotoxic pesticides. J Environ Sci (China) 2023; 125:735-741. [PMID: 36375955 DOI: 10.1016/j.jes.2022.03.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Pesticides are a well-known family of chemicals that have contaminated water systems globally. Four common subfamilies of pesticides include organochlorines, organophosphates, pyrethroids, and carbamate insecticides which have been shown to adversely affect the human nervous system. Studies have shown a link between pesticide exposure and decreased viability, proliferation, migration, and differentiation of murine neural stem cells. Besides human exposure directly through water systems, additional factors such as pesticide bioaccumulation, biomagnification and potential synergism due to co-exposure to other environmental contaminants must be considered. A possible avenue to investigate the molecular mechanisms and biomolecules impacted by the various classes of pesticides includes the field of -omics. Discovery of the precise molecular mechanisms behind pesticide-mediated neurodegenerative disorders may facilitate development of targeted therapeutics. Likewise, discovery of pesticide biodegradation pathways may enable novel approaches for water system bioremediation using genetically engineered microorganisms. In this mini-review, we discuss recently established harmful impacts of various categories of pesticides on the nervous system and the application of -omics field for discovery, validation, and mitigation of pesticide neurotoxicity.
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Affiliation(s)
- Delaram Dara
- Division of Analytical and Environmental Toxicology, Department of Laboratory Medicine and Pathology, Faculty of Medicine and Dentistry, University of Alberta, Alberta T6G 2G3, Canada
| | - Andrei P Drabovich
- Division of Analytical and Environmental Toxicology, Department of Laboratory Medicine and Pathology, Faculty of Medicine and Dentistry, University of Alberta, Alberta T6G 2G3, Canada.
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Kamran M, Bhattacharjee R, Das S, Mukherjee S, Ali N. The paradigm of intracellular parasite survival and drug resistance in leishmanial parasite through genome plasticity and epigenetics: Perception and future perspective. Front Cell Infect Microbiol 2023; 13:1001973. [PMID: 36814446 PMCID: PMC9939536 DOI: 10.3389/fcimb.2023.1001973] [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: 07/24/2022] [Accepted: 01/16/2023] [Indexed: 02/09/2023] Open
Abstract
Leishmania is an intracellular, zoonotic, kinetoplastid eukaryote with more than 1.2 million cases all over the world. The leishmanial chromosomes are divided into polymorphic chromosomal ends, conserved central domains, and antigen-encoding genes found in telomere-proximal regions. The genome flexibility of chromosomal ends of the leishmanial parasite is known to cause drug resistance and intracellular survival through the evasion of host defense mechanisms. Therefore, in this review, we discuss the plasticity of Leishmania genome organization which is the primary cause of drug resistance and parasite survival. Moreover, we have not only elucidated the causes of such genome plasticity which includes aneuploidy, epigenetic factors, copy number variation (CNV), and post-translation modification (PTM) but also highlighted their impact on drug resistance and parasite survival.
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Affiliation(s)
| | | | - Sonali Das
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, West Bengal, India
| | - Sohitri Mukherjee
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, West Bengal, India
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Yang H, Gan L, Chen R, Li D, Zhang J, Wang Z. From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach. Brief Bioinform 2023; 24:6896032. [PMID: 36515158 DOI: 10.1093/bib/bbac528] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/31/2022] [Accepted: 11/07/2022] [Indexed: 12/15/2022] Open
Abstract
The development of targeted drugs allows precision medicine in cancer treatment and optimal targeted therapies. Accurate identification of cancer druggable genes helps strengthen the understanding of targeted cancer therapy and promotes precise cancer treatment. However, rare cancer-druggable genes have been found due to the multi-omics data's diversity and complexity. This study proposes deep forest for cancer druggable genes discovery (DF-CAGE), a novel machine learning-based method for cancer-druggable gene discovery. DF-CAGE integrated the somatic mutations, copy number variants, DNA methylation and RNA-Seq data across ˜10 000 TCGA profiles to identify the landscape of the cancer-druggable genes. We found that DF-CAGE discovers the commonalities of currently known cancer-druggable genes from the perspective of multi-omics data and achieved excellent performance on OncoKB, Target and Drugbank data sets. Among the ˜20 000 protein-coding genes, DF-CAGE pinpointed 465 potential cancer-druggable genes. We found that the candidate cancer druggable genes (CDG) are clinically meaningful and divided the CDG into known, reliable and potential gene sets. Finally, we analyzed the omics data's contribution to identifying druggable genes. We found that DF-CAGE reports druggable genes mainly based on the copy number variations (CNVs) data, the gene rearrangements and the mutation rates in the population. These findings may enlighten the future study and development of new drugs.
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Affiliation(s)
- Hai Yang
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China
| | - Lipeng Gan
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China
| | - Rui Chen
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China
| | - Jing Zhang
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China
| | - Zhe Wang
- Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China
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48
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Lightfoot HL, Smith GF. Targeting RNA with small molecules-A safety perspective. Br J Pharmacol 2023. [PMID: 36631428 DOI: 10.1111/bph.16027] [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: 02/24/2022] [Revised: 06/30/2022] [Accepted: 12/20/2022] [Indexed: 01/13/2023] Open
Abstract
RNA is a major player in cellular function, and consequently can drive a number of disease pathologies. Over the past several years, small molecule-RNA targeting (smRNA targeting) has developed into a promising drug discovery approach. Numerous techniques, tools, and assays have been developed to support this field, and significant investments have been made by pharmaceutical and biotechnology companies. To date, the focus has been on identifying disease validated primary targets for smRNA drug development, yet RNA as a secondary (off) target for all small molecule drug programs largely has been unexplored. In this perspective, we discuss structure, target, and mechanism-driven safety aspects of smRNAs and highlight how these parameters can be evaluated in drug discovery programs to produce potentially safer drugs.
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Affiliation(s)
- Helen L Lightfoot
- Safety and Mechanistic Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Graham F Smith
- Data Science and AI, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
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Reolizo L, Matsuda M, Seki E. Experimental Workflow for Preclinical Studies of Human Antifibrotic Therapies. Methods Mol Biol 2023; 2669:285-306. [PMID: 37247068 DOI: 10.1007/978-1-0716-3207-9_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Chronic liver diseases accompanied by liver fibrosis have caused significant morbidity and mortality in the world with increasing prevalence. Nonetheless, there are no approved antifibrotic therapies. Although numerous preclinical studies showed satisfactory results in targeting fibrotic pathways, these animal studies have not led to success in humans. In this chapter, we summarize the experimental approaches currently available, including in vitro cell culture models, in vivo animal models, and new experimental tools relevant to humans, and discuss how we translate laboratory results to clinical trials. We will also address the obstacles in transitioning promising therapies from preclinical studies to human antifibrotic treatments.
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Affiliation(s)
- Lien Reolizo
- Karsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michitaka Matsuda
- Karsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ekihiro Seki
- Karsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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50
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Nisar N, Mir SA, Kareem O, Pottoo FH. Proteomics approaches in the identification of cancer biomarkers and drug discovery. Proteomics 2023. [DOI: 10.1016/b978-0-323-95072-5.00001-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
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