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Moyer DC, Reimertz J, Segrè D, Fuxman Bass JI. Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600481. [PMID: 38979177 PMCID: PMC11230171 DOI: 10.1101/2024.06.24.600481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. Results We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. Conclusions MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.
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Deng H, Fan R, Zhai Y, Li J, Huang Z, Peng L. Incidence of chemotherapy-related cardiac dysfunction in cancer patients. Clin Cardiol 2024; 47:e24269. [PMID: 38634453 PMCID: PMC11024952 DOI: 10.1002/clc.24269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Cancer patients are increasingly affected by chemotherapy-related cardiac dysfunction. The reported incidence of this condition vary significantly across different studies. HYPOTHESIS A better comprehensive understanding of chemotherapy-related cardiac dysfunction incidence in cancer patients is imperative. Therefore, we performed a meta-analysis to establish the overall incidence of chemotherapy-related cardiac dysfunction in cancer patients. METHODS We searched articles in PubMed and EMBASE from database inception to May 1, 2023. Studies that reported the incidence of chemotherapy-related cardiac dysfunction in cancer patients were included. RESULTS A total of 53 studies involving 35 651 individuals were finally included in the meta-analysis. The overall pooled incidence of chemotherapy-related cardiac dysfunction in cancer patients was 63.21 per 1000 person-years (95% CI: 57.28-69.14). The chemotherapy-related cardiac dysfunction incidence increased steeply within half a year of cancer chemotherapy. Also, the trend of chemotherapy-related cardiac dysfunction incidence appeared to have plateaued after a longer duration of follow-up. In addition, chemotherapy-related cardiac dysfunction incidence rates are significantly higher among patients with age ≥50 years versus patients with age <50 years (99.96 vs. 34.48 per 1000 person-years). The incidence rate of cardiac dysfunction was higher among breast cancer patients (72.97 per 1000 person-years), leukemia patients (65.21 per 1000 person-years), and lymphoma patients (55.43 per 1000 person-years). CONCLUSION Our meta-analysis unveiled a definitive overall incidence rate of chemotherapy-related cardiac dysfunction in cancer patients. In addition, it was found that the risk of developing this condition escalates within the initial 6 months postchemotherapy, subsequently tapering off to become statistically insignificant after a duration of 6 years.
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Affiliation(s)
- Hai‐Wei Deng
- Department of Cardiology, The First Affiliated HospitalSun Yat‐Sen UniversityGuangzhouChina
- Key Laboratory on Assisted Circulation Ministry of HealthGuangzhouChina
| | - Rui Fan
- Key Laboratory on Assisted Circulation Ministry of HealthGuangzhouChina
- Department of Medical Ultrasonics, The First Affiliated HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Yuan‐Sheng Zhai
- Department of Cardiology, The First Affiliated HospitalSun Yat‐Sen UniversityGuangzhouChina
- Key Laboratory on Assisted Circulation Ministry of HealthGuangzhouChina
| | - Jie Li
- Key Laboratory on Assisted Circulation Ministry of HealthGuangzhouChina
- Department of Medical Ultrasonics, The First Affiliated HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Zhi‐Bin Huang
- Department of Cardiology, The First Affiliated HospitalSun Yat‐Sen UniversityGuangzhouChina
- Key Laboratory on Assisted Circulation Ministry of HealthGuangzhouChina
| | - Long‐Yun Peng
- Department of Cardiology, The First Affiliated HospitalSun Yat‐Sen UniversityGuangzhouChina
- Key Laboratory on Assisted Circulation Ministry of HealthGuangzhouChina
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Mofidifar S, Yadegar A, Karimi-Jafari MH. A reconstructed genome-scale metabolic model of Helicobacter pylori for predicting putative drug targets in clarithromycin and rifampicin resistance conditions. Helicobacter 2024; 29:e13074. [PMID: 38615332 DOI: 10.1111/hel.13074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/27/2024] [Accepted: 04/01/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Helicobacter pylori is considered a true human pathogen for which rising drug resistance constitutes a drastic concern globally. The present study aimed to reconstruct a genome-scale metabolic model (GSMM) to decipher the metabolic capability of H. pylori strains in response to clarithromycin and rifampicin along with identification of novel drug targets. MATERIALS AND METHODS The iIT341 model of H. pylori was updated based on genome annotation data, and biochemical knowledge from literature and databases. Context-specific models were generated by integrating the transcriptomic data of clarithromycin and rifampicin resistance into the model. Flux balance analysis was employed for identifying essential genes in each strain, which were further prioritized upon being nonhomologs to humans, virulence factor analysis, druggability, and broad-spectrum analysis. Additionally, metabolic differences between sensitive and resistant strains were also investigated based on flux variability analysis and pathway enrichment analysis of transcriptomic data. RESULTS The reconstructed GSMM was named as HpM485 model. Pathway enrichment and flux variability analyses demonstrated reduced activity in the ribosomal pathway in both clarithromycin- and rifampicin-resistant strains. Also, a significant decrease was detected in the activity of metabolic pathways of clarithromycin-resistant strain. Moreover, 23 and 16 essential genes were exclusively detected in clarithromycin- and rifampicin-resistant strains, respectively. Based on prioritization analysis, cyclopropane fatty acid synthase and phosphoenolpyruvate synthase were identified as putative drug targets in clarithromycin- and rifampicin-resistant strains, respectively. CONCLUSIONS We present a robust and reliable metabolic model of H. pylori. This model can predict novel drug targets to combat drug resistance and explore the metabolic capability of H. pylori in various conditions.
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Affiliation(s)
- Sepideh Mofidifar
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Abbas Yadegar
- Foodborne and Waterborne Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Wang M, Huang H, Sun Y, Wang M, Yang Z, Shi Y, Liu L. PEI functionalized cell membrane for tumor targeted and glutathione responsive gene delivery. Int J Biol Macromol 2024; 255:128354. [PMID: 37995795 DOI: 10.1016/j.ijbiomac.2023.128354] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 11/20/2023] [Accepted: 11/20/2023] [Indexed: 11/25/2023]
Abstract
Polyethylenimine (PEI) is a broadly exploited cationic polymer due to its remarkable gene-loading capacity. However, the high cytotoxicity caused by its high surface charge density has been reported in many cell lines, limiting its application significantly. In this study, two different molecular weights of PEI (PEI10k and PEI25k) were crosslinked with red blood cell membranes (RBCm) via disulfide bonds to form PEI derivatives (RMPs) with lower charge density. Furthermore, the targeting molecule folic acid (FA) molecules were further grafted onto the polymers to obtain FA-modified PEI-RBCm copolymers (FA-RMP25k) with tumor cell targeting and glutathione response. In vitro experiments showed that the FA-RMP25k/DNA complex had satisfactory uptake efficiency in both HeLa and 293T cells, and did not cause significant cytotoxicity. Furthermore, the uptake and transfection efficiency of the FA-RMP25k/DNA complex was significantly higher than that of the PEI25k/DNA complex, indicating that FA grafting can increase transfection efficiency by 15 %. These results suggest that FA-RMP25k may be a promising non-viral gene vector with potential applications in gene therapy.
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Affiliation(s)
- Mengying Wang
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China
| | - Haoxiang Huang
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China
| | - Yanlin Sun
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China
| | - Mingjie Wang
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China
| | - Zhaojun Yang
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China
| | - Yong Shi
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China
| | - Liang Liu
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China.
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Sharma A, Garg A, Ramana J, Gupta D. VirulentPred 2.0: An improved method for prediction of virulent proteins in bacterial pathogens. Protein Sci 2023; 32:e4808. [PMID: 37872744 PMCID: PMC10659933 DOI: 10.1002/pro.4808] [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: 03/12/2023] [Revised: 09/27/2023] [Accepted: 10/15/2023] [Indexed: 10/25/2023]
Abstract
Virulence proteins in pathogens are essential for causing disease in a host. They enable the pathogen to invade, survive and multiply within the host, thus enhancing its potential to cause disease while also causing evasion of host defense mechanisms. Identifying these factors, especially potential vaccine candidates or drug targets, is critical for vaccine or drug development research. In this context, we present an improved version of VirulentPred 1.0 for rapidly identifying virulent proteins. The VirulentPred 2.0 is based on training machine learning models with experimentally validated virulent protein sequences. VirulentPred 2.0 achieved 84.71% accuracy with the validation dataset and 85.18% on an independent test dataset. The models are trained and evaluated with the latest sequence datasets of virulent proteins, which are three times greater in number than the proteins used in the earlier version of VirulentPred. Moreover, a significant improvement of 11% in the prediction accuracy over the earlier version is achieved with the best position-specific scoring matrix (PSSM)-based model for the latest test dataset. VirulentPred 2.0 is available as a user-friendly web interface at https://bioinfo.icgeb.res.in/virulent2/ and a standalone application suitable for bulk predictions. With higher efficiency and availability as a standalone tool, VirulentPred 2.0 holds immense potential for high throughput yet efficient identification of virulent proteins in bacterial pathogens.
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Affiliation(s)
- Arun Sharma
- Translational Bioinformatics GroupInternational Centre for Genetic Engineering and Biotechnology (ICGEB)New DelhiIndia
| | - Aarti Garg
- Translational Bioinformatics GroupInternational Centre for Genetic Engineering and Biotechnology (ICGEB)New DelhiIndia
| | - Jayashree Ramana
- Translational Bioinformatics GroupInternational Centre for Genetic Engineering and Biotechnology (ICGEB)New DelhiIndia
| | - Dinesh Gupta
- Translational Bioinformatics GroupInternational Centre for Genetic Engineering and Biotechnology (ICGEB)New DelhiIndia
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Gupta R, Singh M, Pathania R. Chemical genetic approaches for the discovery of bacterial cell wall inhibitors. RSC Med Chem 2023; 14:2125-2154. [PMID: 37974958 PMCID: PMC10650376 DOI: 10.1039/d3md00143a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 08/10/2023] [Indexed: 11/19/2023] Open
Abstract
Antimicrobial resistance (AMR) in bacterial pathogens is a worldwide health issue. The innovation gap in discovering new antibiotics has remained a significant hurdle in combating the AMR problem. Currently, antibiotics target various vital components of the bacterial cell envelope, nucleic acid and protein biosynthesis machinery and metabolic pathways essential for bacterial survival. The critical role of the bacterial cell envelope in cell morphogenesis and integrity makes it an attractive drug target. While a significant number of in-clinic antibiotics target peptidoglycan biosynthesis, several components of the bacterial cell envelope have been overlooked. This review focuses on various antibacterial targets in the bacterial cell wall and the strategies employed to find their novel inhibitors. This review will further elaborate on combining forward and reverse chemical genetic approaches to discover antibacterials that target the bacterial cell envelope.
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Affiliation(s)
- Rinki Gupta
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee Roorkee - 247 667 Uttarakhand India
| | - Mangal Singh
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee Roorkee - 247 667 Uttarakhand India
| | - Ranjana Pathania
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee Roorkee - 247 667 Uttarakhand India
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Barot S, Patel H, Yadav A, Ban I. Recent advancement in targeted therapy and role of emerging technologies to treat cancer. Med Oncol 2023; 40:324. [PMID: 37805624 DOI: 10.1007/s12032-023-02184-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 09/04/2023] [Indexed: 10/09/2023]
Abstract
Cancer is a complex disease that causes abnormal cell growth and spread. DNA mutations, chemical or environmental exposure, viral infections, chronic inflammation, hormone abnormalities, etc., are underlying factors that can cause cancer. Drug resistance and toxicity complicate cancer treatment. Additionally, the variability of cancer makes it difficult to establish universal treatment guidelines. Next-generation sequencing has made genetic testing inexpensive. This uncovers genetic mutations that can be treated with specialty drugs. AI (artificial intelligence), machine learning, biopsy, next-generation sequencing, and digital pathology provide personalized cancer treatment. This allows for patient-specific biological targets and cancer treatment. Monoclonal antibodies, CAR-T, and cancer vaccines are promising cancer treatments. Recent trial data incorporating these therapies have shown superiority in clinical outcomes and drug tolerability over conventional chemotherapies. Combinations of these therapies with new technology can change cancer treatment and help many. This review discusses the development and challenges of targeted therapies like monoclonal antibodies (mAbs), bispecific antibodies (BsAbs), bispecific T cell engagers (BiTEs), dual variable domain (DVD) antibodies, CAR-T therapy, cancer vaccines, oncolytic viruses, lipid nanoparticle-based mRNA cancer vaccines, and their clinical outcomes in various cancers. We will also study how artificial intelligence and machine learning help find new cancer treatment targets.
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Affiliation(s)
- Shrikant Barot
- College of Pharmacy and Health Sciences, St. John's University, 8000 Utopia Parkway, Queens, NY, 11439, USA.
| | - Henis Patel
- College of Pharmacy and Health Sciences, St. John's University, 8000 Utopia Parkway, Queens, NY, 11439, USA
| | - Anjali Yadav
- College of Pharmacy and Health Sciences, St. John's University, 8000 Utopia Parkway, Queens, NY, 11439, USA
| | - Igor Ban
- College of Pharmacy and Health Sciences, St. John's University, 8000 Utopia Parkway, Queens, NY, 11439, USA
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Zangene E, Marashi SA, Montazeri H. SL-scan identifies synthetic lethal interactions in cancer using metabolic networks. Sci Rep 2023; 13:15763. [PMID: 37737478 PMCID: PMC10516981 DOI: 10.1038/s41598-023-42992-4] [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: 05/25/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023] Open
Abstract
Exploiting synthetic lethality is a promising strategy for developing targeted cancer therapies. However, identifying clinically significant synthetic lethal (SL) interactions among a large number of gene combinations is a challenging computational task. In this study, we developed the SL-scan pipeline based on metabolic network modeling to discover SL interaction. The SL-scan pipeline identifies the association between simulated Flux Balance Analysis knockout scores and mutation data across cancer cell lines and predicts putative SL interactions. We assessed the concordance of the SL pairs predicted by SL-scan with those of obtained from analysis of the CRISPR, shRNA, and PRISM datasets. Our results demonstrate that the SL-scan pipeline outperformed existing SL prediction approaches based on metabolic networks in identifying SL pairs in various cancers. This study emphasizes the importance of integrating multiple data sources, particularly mutation data, when identifying SL pairs for targeted cancer therapies. The findings of this study may lead to the development of novel targeted cancer therapies.
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Affiliation(s)
- Ehsan Zangene
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
| | - Hesam Montazeri
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Sarmah DT, Parveen R, Kundu J, Chatterjee S. Latent tuberculosis and computational biology: A less-talked affair. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 178:17-31. [PMID: 36781150 DOI: 10.1016/j.pbiomolbio.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/13/2023]
Abstract
Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.
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Affiliation(s)
- Dipanka Tanu Sarmah
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India
| | - Rubi Parveen
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India
| | - Jayendrajyoti Kundu
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India
| | - Samrat Chatterjee
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India.
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Strain B, Morrissey J, Antonakoudis A, Kontoravdi C. Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems. Comput Struct Biotechnol J 2023; 21:1543-1549. [PMID: 36879884 PMCID: PMC9984296 DOI: 10.1016/j.csbj.2023.02.011] [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: 11/15/2022] [Revised: 02/06/2023] [Accepted: 02/06/2023] [Indexed: 02/10/2023] Open
Abstract
With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.
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Affiliation(s)
- Benjamin Strain
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - James Morrissey
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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Wang FS, Chen PR, Chen TY, Zhang HX. Fuzzy optimization for identifying anti-cancer targets with few side effects in constraint-based models of head and neck cancer. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220633. [PMID: 36303939 PMCID: PMC9597175 DOI: 10.1098/rsos.220633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Computer-aided methods can be used to screen potential candidate targets and to reduce the time and cost of drug development. In most of these methods, synthetic lethality is used as a therapeutic criterion to identify drug targets. However, these methods do not consider the side effects during the identification stage. This study developed a fuzzy multi-objective optimization for identifying anti-cancer targets that not only evaluated cancer cell mortality, but also minimized side effects due to treatment. We identified potential anti-cancer enzymes and antimetabolites for the treatment of head and neck cancer (HNC). The identified one- and two-target enzymes were primarily involved in six major pathways, namely, purine and pyrimidine metabolism and the pentose phosphate pathway. Most of the identified targets can be regulated by approved drugs; thus, these drugs are potential candidates for drug repurposing as a treatment for HNC. Furthermore, we identified antimetabolites involved in pathways similar to those identified using a gene-centric approach. Moreover, HMGCR knockdown could not block the growth of HNC cells. However, the two-target combinations of (UMPS, HMGCR) and (CAD, HMGCR) could achieve cell mortality and improve metabolic deviation grades over 22% without reducing the cell viability grade.
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Affiliation(s)
- Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Pei-Rong Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Ting-Yu Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Hao-Xiang Zhang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
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Paul A, Azhar S, Das PN, Bairagi N, Chatterjee S. Elucidating the metabolic characteristics of pancreatic β-cells from patients with type 2 diabetes (T2D) using a genome-scale metabolic modeling. Comput Biol Med 2022; 144:105365. [PMID: 35276551 DOI: 10.1016/j.compbiomed.2022.105365] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/24/2022] [Accepted: 02/27/2022] [Indexed: 11/27/2022]
Abstract
Diabetes is a global health problem caused primarily by the inability of pancreatic β-cells to secrete adequate insulin. Despite extensive research, the identity of factors contributing to the dysregulated metabolism-secretion coupling in the β-cells remains elusive. The present study attempts to capture some of these factors responsible for the impaired β-cell metabolism-secretion coupling that contributes to diabetes pathogenesis. The metabolic-flux profiles of pancreatic β-cells were predicted using genome-scale metabolic modeling for ten diabetic patients and ten control subjects. Analysis of these flux states shows reduction in the mitochondrial fatty acid oxidation and mitochondrial oxidative phosphorylation pathways, that leads to decreased insulin secretion in diabetes. We also observed elevated reactive oxygen species (ROS) generation through peroxisomal fatty acid β-oxidation. In addition, cellular antioxidant defense systems were found to be attenuated in diabetes. Our analysis also uncovered the possible changes in the plasma metabolites in diabetes due to the β-cells failure. These efforts subsequently led to the identification of seven metabolites associated with cardiovascular disease (CVD) pathogenesis, thus establishing its link as a secondary complication of diabetes.
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Affiliation(s)
- Abhijit Paul
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, 121001, India
| | - Salman Azhar
- Geriatric Research, Education and Clinical Center, VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA; Division of Endocrinology, Gerontology and Metabolism, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Phonindra Nath Das
- Department of Mathematics, Ramakrishna Mission Vivekananda Centenary College, Rahara, Kolkata, 700118, India
| | - Nandadulal Bairagi
- Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata, 700032, India
| | - Samrat Chatterjee
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, 121001, India.
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Gouda G, Gupta MK, Donde R, Behera L, Vadde R. Metabolic pathway-based target therapy to hepatocellular carcinoma: a computational approach. THERANOSTICS AND PRECISION MEDICINE FOR THE MANAGEMENT OF HEPATOCELLULAR CARCINOMA, VOLUME 2 2022:83-103. [DOI: 10.1016/b978-0-323-98807-0.00003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
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Fang Z, Zou JL. Recombinant COL6 α2 as a Self-Organization Factor That Triggers Orderly Nerve Regeneration Without Guidance Cues. Front Cell Neurosci 2021; 15:816781. [PMID: 35002632 PMCID: PMC8732766 DOI: 10.3389/fncel.2021.816781] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
Collagen VI (COL6) in the microenvironment was recently identified as an extracellular signal that bears the function of promoting orderly axon bundle formation. However, the large molecular weight of COL6 (≈2,000 kDa) limits its production and clinical application. It remains unclear whether the smaller subunit α chains of COL6 can exert axon bundling and ordering effects independently. Herein, based on a dorsal root ganglion (DRG) ex vivo model, the contributions of three main COL6 α chains on orderly nerve bundle formation were analyzed, and COL6 α2 showed the largest contribution weight. A recombinant COL6 α2 chain was produced and demonstrated to promote the formation of orderly axon bundles through the NCAM1-mediated pathway. The addition of COL6 α2 in conventional hydrogel triggered orderly nerve regeneration in a rat sciatic nerve defect model. Immunogenicity assessment showed weaker immunogenicity of COL6 α2 compared to that of the COL6 complex. These findings suggest that recombinant COL6 α2 is a promising material for orderly nerve regeneration.
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Affiliation(s)
- Zhou Fang
- Institute of Neuroscience and the Second Affiliated Hospital of Guangzhou Medical University, Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou, China
- Key Laboratory of Neurological Function and Health, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
| | - Jian-Long Zou
- Institute of Neuroscience and the Second Affiliated Hospital of Guangzhou Medical University, Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou, China
- Key Laboratory of Neurological Function and Health, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
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