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Damarov IS, Korbolina EE, Rykova EY, Merkulova TI. Multi-Omics Analysis Revealed the rSNPs Potentially Involved in T2DM Pathogenic Mechanism and Metformin Response. Int J Mol Sci 2024; 25:9297. [PMID: 39273245 PMCID: PMC11394919 DOI: 10.3390/ijms25179297] [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: 07/11/2024] [Revised: 08/14/2024] [Accepted: 08/26/2024] [Indexed: 09/15/2024] Open
Abstract
The goal of our study was to identify and assess the functionally significant SNPs with potentially important roles in the development of type 2 diabetes mellitus (T2DM) and/or their effect on individual response to antihyperglycemic medication with metformin. We applied a bioinformatics approach to identify the regulatory SNPs (rSNPs) associated with allele-asymmetric binding and expression events in our paired ChIP-seq and RNA-seq data for peripheral blood mononuclear cells (PBMCs) of nine healthy individuals. The rSNP outcomes were analyzed using public data from the GWAS (Genome-Wide Association Studies) and Genotype-Tissue Expression (GTEx). The differentially expressed genes (DEGs) between healthy and T2DM individuals (GSE221521), including metformin responders and non-responders (GSE153315), were searched for in GEO RNA-seq data. The DEGs harboring rSNPs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 14,796 rSNPs in the promoters of 5132 genes of human PBMCs. We found 4280 rSNPs to associate with both phenotypic traits (GWAS) and expression quantitative trait loci (eQTLs) from GTEx. Between T2DM patients and controls, 3810 rSNPs were detected in the promoters of 1284 DEGs. Based on the protein-protein interaction (PPI) network, we identified 31 upregulated hub genes, including the genes involved in inflammation, obesity, and insulin resistance. The top-ranked 10 enriched KEGG pathways for these hubs included insulin, AMPK, and FoxO signaling pathways. Between metformin responders and non-responders, 367 rSNPs were found in the promoters of 131 DEGs. Genes encoding transcription factors and transcription regulators were the most widely represented group and many were shown to be involved in the T2DM pathogenesis. We have formed a list of human rSNPs that add functional interpretation to the T2DM-association signals identified in GWAS. The results suggest candidate causal regulatory variants for T2DM, with strong enrichment in the pathways related to glucose metabolism, inflammation, and the effects of metformin.
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Affiliation(s)
- Igor S Damarov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Elena E Korbolina
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Elena Y Rykova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Department of Engineering Problems of Ecology, Novosibirsk State Technical University, 630087 Novosibirsk, Russia
| | - Tatiana I Merkulova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
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Momen YS, Mishra J, Kumar N. Brain-Gut and Microbiota-Gut-Brain Communication in Type-2 Diabetes Linked Alzheimer's Disease. Nutrients 2024; 16:2558. [PMID: 39125436 PMCID: PMC11313915 DOI: 10.3390/nu16152558] [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: 05/27/2024] [Revised: 06/20/2024] [Accepted: 06/25/2024] [Indexed: 08/12/2024] Open
Abstract
The gastrointestinal (GI) tract, home to the largest microbial population in the human body, plays a crucial role in overall health through various mechanisms. Recent advancements in research have revealed the potential implications of gut-brain and vice-versa communication mediated by gut-microbiota and their microbial products in various diseases including type-2 diabetes and Alzheimer's disease (AD). AD is the most common type of dementia where most of cases are sporadic with no clearly identified cause. However, multiple factors are implicated in the progression of sporadic AD which can be classified as non-modifiable (e.g., genetic) and modifiable (e.g. Type-2 diabetes, diet etc.). Present review focusses on key players particularly the modifiable factors such as Type-2 diabetes (T2D) and diet and their implications in microbiota-gut-brain (MGB) and brain-gut (BG) communication and cognitive functions of healthy brain and their dysfunction in Alzheimer's Disease. Special emphasis has been given on elucidation of the mechanistic aspects of the impact of diet on gut-microbiota and the implications of some of the gut-microbial products in T2D and AD pathology. For example, mechanistically, HFD induces gut dysbiosis with driven metabolites that in turn cause loss of integrity of intestinal barrier with concomitant colonic and systemic chronic low-grade inflammation, associated with obesity and T2D. HFD-induced obesity and T2D parallel neuroinflammation, deposition of Amyloid β (Aβ), and ultimately cognitive impairment. The review also provides a new perspective of the impact of diet on brain-gut and microbiota-gut-brain communication in terms of transcription factors as a commonly spoken language that may facilitates the interaction between gut and brain of obese diabetic patients who are at a higher risk of developing cognitive impairment and AD. Other commonality such as tyrosine kinase expression and functions maintaining intestinal integrity on one hand and the phagocytic clarence by migratory microglial functions in brain are also discussed. Lastly, the characterization of the key players future research that might shed lights on novel potential pharmacological target to impede AD progression are also discussed.
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Affiliation(s)
| | | | - Narendra Kumar
- Department of Pharmaceutical Sciences, ILR College of Pharmacy, Texas A&M Health Science Center, Kingsville, TX 78363, USA
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Huang ZM, Kang JQ, Chen PZ, Deng LF, Li JX, He YX, Liang J, Huang N, Luo TY, Lan QW, Chen HK, Guo XG. Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine Learning. Biochem Genet 2024; 62:2606-2630. [PMID: 37991568 DOI: 10.1007/s10528-023-10563-x] [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: 08/08/2023] [Accepted: 10/25/2023] [Indexed: 11/23/2023]
Abstract
The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coinfection may result in complications that make treatment more difficult. However, the molecular mechanisms underpinning the interaction between TB and COVID-19 are unclear. Accordingly, transcriptome analysis was used to detect the shared pathways and molecular biomarkers in TB and COVID-19, allowing us to determine the complex relationship between COVID-19 and TB. Two RNA-seq datasets (GSE114192 and GSE163151) from the Gene Expression Omnibus were used to find concerted differentially expressed genes (DEGs) between TB and COVID-19 to identify the common pathogenic mechanisms. A total of 124 common DEGs were detected and used to find shared pathways and drug targets. Several enterprising bioinformatics tools were applied to perform pathway analysis, enrichment analysis and networks analysis. Protein-protein interaction analysis and machine learning was used to identify hub genes (GAS6, OAS3 and PDCD1LG2) and datasets GSE171110, GSE54992 and GSE79362 were used for verification. The mechanism of protein-drug interactions may have reference value in the treatment of coinfection of COVID-19 and TB.
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Affiliation(s)
- Ze-Min Huang
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Jia-Qi Kang
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The First Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Pei-Zhen Chen
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Lin-Fen Deng
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Jia-Xin Li
- Department of Clinical Medicine, The First Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Ying-Xin He
- Clinical Laboratory Medicine, Guangzhou Medical University, Guangzhou, 510006, China
| | - Jie Liang
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Nan Huang
- Clinical Laboratory Medicine, Guangzhou Medical University, Guangzhou, 510006, China
| | - Tian-Ye Luo
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Qi-Wen Lan
- Department of Clinical Medicine, The Second Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Hao-Kai Chen
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Xu-Guang Guo
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.
- Department of Clinical Laboratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, King Med School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, 510000, China.
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Gao J, Zou Y, Lv XY, Chen L, Hou XG. Novel insights into immune-related genes associated with type 2 diabetes mellitus-related cognitive impairment. World J Diabetes 2024; 15:735-757. [PMID: 38680704 PMCID: PMC11045412 DOI: 10.4239/wjd.v15.i4.735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/21/2024] [Accepted: 03/04/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND The cognitive impairment in type 2 diabetes mellitus (T2DM) is a multifaceted and advancing state that requires further exploration to fully comprehend. Neuroinflammation is considered to be one of the main mechanisms and the immune system has played a vital role in the progression of the disease. AIM To identify and validate the immune-related genes in the hippocampus associated with T2DM-related cognitive impairment. METHODS To identify differentially expressed genes (DEGs) between T2DM and controls, we used data from the Gene Expression Omnibus database GSE125387. To identify T2DM module genes, we used Weighted Gene Co-Expression Network Analysis. All the genes were subject to Gene Set Enrichment Analysis. Protein-protein interaction network construction and machine learning were utilized to identify three hub genes. Immune cell infiltration analysis was performed. The three hub genes were validated in GSE152539 via receiver operating characteristic curve analysis. Validation experiments including reverse transcription quantitative real-time PCR, Western blotting and immunohistochemistry were conducted both in vivo and in vitro. To identify potential drugs associated with hub genes, we used the Comparative Toxicogenomics Database (CTD). RESULTS A total of 576 DEGs were identified using GSE125387. By taking the intersection of DEGs, T2DM module genes, and immune-related genes, a total of 59 genes associated with the immune system were identified. Afterward, machine learning was utilized to identify three hub genes (H2-T24, Rac3, and Tfrc). The hub genes were associated with a variety of immune cells. The three hub genes were validated in GSE152539. Validation experiments were conducted at the mRNA and protein levels both in vivo and in vitro, consistent with the bioinformatics analysis. Additionally, 11 potential drugs associated with RAC3 and TFRC were identified based on the CTD. CONCLUSION Immune-related genes that differ in expression in the hippocampus are closely linked to microglia. We validated the expression of three hub genes both in vivo and in vitro, consistent with our bioinformatics results. We discovered 11 compounds associated with RAC3 and TFRC. These findings suggest that they are co-regulatory molecules of immunometabolism in diabetic cognitive impairment.
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Affiliation(s)
- Jing Gao
- Department of Endocrinology, Qilu Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong Province, China
| | - Ying Zou
- Department of Endocrinology, Qilu Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong Province, China
| | - Xiao-Yu Lv
- Department of Endocrinology, Qilu Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong Province, China
| | - Li Chen
- Department of Endocrinology, Qilu Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong Province, China
| | - Xin-Guo Hou
- Department of Endocrinology, Qilu Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong Province, China
- Institute of Endocrine and Metabolic Diseases, Shandong University, Jinan 250012, Shandong Province, China
- Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan 250012, Shandong Province, China
- Department of Endocrinology, Jinan Clinical Research Center for Endocrine and Metabolic Disease, Jinan 250012, Shandong Province, China
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Zhang X, Zhu Z, Huang Y, Shang X, O'Brien TJ, Kwan P, Ha J, Wang W, Liu S, Zhang X, Kiburg K, Bao Y, Wang J, Yu H, He M, Zhang L. Shared genetic aetiology of Alzheimer's disease and age-related macular degeneration by APOC1 and APOE genes. BMJ Neurol Open 2024; 6:e000570. [PMID: 38646507 PMCID: PMC11029327 DOI: 10.1136/bmjno-2023-000570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 04/04/2024] [Indexed: 04/23/2024] Open
Abstract
Background Alzheimer's disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights into the underlying pathogenesis and inform integrated prevention and treatment for both diseases. Methods A stratified quantile-quantile (QQ) plot was constructed to detect the pleiotropy among AD and AMD based on genome-wide association studies data from 17 008 patients with AD and 30 178 patients with AMD. A Bayesian conditional false discovery rate-based (cFDR) method was used to identify pleiotropic genes. UK Biobank was used to verify the pleiotropy analysis. Biological network and enrichment analysis were conducted to explain the biological reason for pleiotropy phenomena. A diagnostic test based on gene expression data was used to predict biomarkers for AD and AMD based on pleiotropic genes and their regulators. Results Significant pleiotropy was found between AD and AMD (significant leftward shift on QQ plots). APOC1 and APOE were identified as pleiotropic genes for AD-AMD (cFDR <0.01). Network analysis revealed that APOC1 and APOE occupied borderline positions on the gene co-expression networks. Both APOC1 and APOE genes were enriched on the herpes simplex virus 1 infection pathway. Further, machine learning-based diagnostic tests identified that APOC1, APOE (areas under the curve (AUCs) >0.65) and their upstream regulators, especially ZNF131, ADNP2 and HINFP, could be potential biomarkers for both AD and AMD (AUCs >0.8). Conclusion In this study, we confirmed the genetic pleiotropy between AD and AMD and identified APOC1 and APOE as pleiotropic genes. Further, the integration of multiomics data identified ZNF131, ADNP2 and HINFP as novel diagnostic biomarkers for AD and AMD.
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Affiliation(s)
- Xueli Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Zhuoting Zhu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yu Huang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xianwen Shang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Terence J O'Brien
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Jason Ha
- Alfred Health, Melbourne, Victoria, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Shunming Liu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiayin Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Katerina Kiburg
- Centre for Eye Research, University of Melbourne, East Melbourne, Victoria, Australia
| | - Yining Bao
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Jing Wang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Mingguang He
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Lei Zhang
- Clinical Medical Research Center, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
- School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
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Darwish AB, Mohsen AM, ElShebiney S, Elgohary R, Younis MM. Development of chitosan lipid nanoparticles to alleviate the pharmacological activity of piperine in the management of cognitive deficit in diabetic rats. Sci Rep 2024; 14:8247. [PMID: 38589438 PMCID: PMC11002014 DOI: 10.1038/s41598-024-58601-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/01/2024] [Indexed: 04/10/2024] Open
Abstract
The aim of the present study was to prepare and evaluate Piperine (PP) loaded chitosan lipid nanoparticles (PP-CLNPs) to evaluate its biological activity alone or in combination with the antidiabetic drug Metformin (MET) in the management of cognitive deficit in diabetic rats. Piperine was successfully loaded on CLNPs prepared using chitosan, stearic acid, Tween 80 and Tripolyphosphate (TPP) at different concentrations. The developed CLNPs exhibited high entrapment efficiency that ranged from 85.12 to 97.41%, a particle size in the range of 59.56-414 nm and a negatively charged zeta potential values (- 20.1 to - 43.9 mV). In vitro release study revealed enhanced PP release from CLNPs compared to that from free PP suspensions for up to 24 h. In vivo studies revealed that treatment with the optimized PP-CLNPs formulation (F2) exerted a cognitive enhancing effect and ameliorated the oxidative stress associated with diabetes. PP-CLNPs acted as an effective bio-enhancer which increased the potency of metformin in protecting brain tissue from diabetes-induced neuroinflammation and memory deterioration. These results suggested that CLNPs could be a promising drug delivery system for encapsulating PP and thus can be used as an adjuvant therapy in the management of high-risk diabetic cognitive impairment conditions.
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Affiliation(s)
- Asmaa Badawy Darwish
- Pharmaceutical Technology Department, National Research Centre (Affiliation ID: 60014618), El-Buhouth St., Dokki, Giza, 12622, Egypt.
| | - Amira Mohamed Mohsen
- Pharmaceutical Technology Department, National Research Centre (Affiliation ID: 60014618), El-Buhouth St., Dokki, Giza, 12622, Egypt
| | - Shaimaa ElShebiney
- Narcotics, Ergogenics, and Poisons Department, National Research Centre (Affiliation ID: 60014618), El-Buhouth St., Dokki, Giza, 12622, Egypt
| | - Rania Elgohary
- Narcotics, Ergogenics, and Poisons Department, National Research Centre (Affiliation ID: 60014618), El-Buhouth St., Dokki, Giza, 12622, Egypt
| | - Mostafa Mohamed Younis
- Pharmaceutical Technology Department, National Research Centre (Affiliation ID: 60014618), El-Buhouth St., Dokki, Giza, 12622, Egypt
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Wang X, Yang Z, Ma N, Sun X, Li H, Zhou J, Yu X. A novel hypoglycemia alarm framework for type 2 diabetes with high glycemic variability. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024; 40:e3799. [PMID: 38148660 DOI: 10.1002/cnm.3799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 10/29/2023] [Accepted: 11/27/2023] [Indexed: 12/28/2023]
Abstract
In patients with type 2 diabetes (T2D), accurate prediction of hypoglycemic events is crucial for maintaining glycemic control and reducing their frequency. However, individuals with high blood glucose variability experience significant fluctuations over time, posing a challenge for early warning models that rely on static features. This article proposes a novel hypoglycemia early alarm framework based on dynamic feature selection. The framework incorporates domain knowledge and introduces multi-scale blood glucose features, including predicted values, essential for early warnings. To address the complexity of the feature matrix, a dynamic feature selection mechanism (Relief-SVM-RFE) is designed to effectively eliminate redundancy. Furthermore, the framework employs online updates for the random forest model, enhancing the learning of more relevant features. The effectiveness of the framework was evaluated using a clinical dataset. For T2D patients with a high coefficient of variation (CV), the framework achieved a sensitivity of 81.15% and specificity of 98.14%, accurately predicting most hypoglycemic events. Notably, the proposed method outperformed other existing approaches. These results indicate the feasibility of anticipating hypoglycemic events in T2D patients with high CV using this innovative framework.
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Affiliation(s)
- Xinzhuo Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Zi Yang
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Ning Ma
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Xiaoyu Sun
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Hongru Li
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Xia Yu
- College of Information Science and Engineering, Northeastern University, Shenyang, China
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Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenovic ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. J Korean Med Sci 2023; 38:e395. [PMID: 38013648 PMCID: PMC10681845 DOI: 10.3346/jkms.2023.38.e395] [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: 07/31/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
- Asia Pacific Vascular Society, New Delhi, India
| | - Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Bennett University, Greater Noida, India
| | - Mahesh Maindarkar
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- School of Bioengineering Sciences and Research, Maharashtra Institute of Technology's Art, Design and Technology University, Pune, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura Mentella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | | | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Inder Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, Beograd, Serbia
| | | | - Puneet Khanna
- Department of Anaesthesiology, AIIMS, New Delhi, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Jasjit S Suri
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, India.
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9
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Hasib RA, Ali MC, Rahman MH, Ahmed S, Sultana S, Summa SZ, Shimu MSS, Afrin Z, Jamal MAHM. Integrated gene expression profiling and functional enrichment analyses to discover biomarkers and pathways associated with Guillain-Barré syndrome and autism spectrum disorder to identify new therapeutic targets. J Biomol Struct Dyn 2023:1-23. [PMID: 37776011 DOI: 10.1080/07391102.2023.2262586] [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: 03/15/2023] [Accepted: 09/17/2023] [Indexed: 10/01/2023]
Abstract
Guillain-Barré syndrome (GBS) is one of the most prominent and acute immune-mediated peripheral neuropathy, while autism spectrum disorders (ASD) are a group of heterogeneous neurodevelopmental disorders. The complete mechanism regarding the neuropathophysiology of these disorders is still ambiguous. Even after recent breakthroughs in molecular biology, the link between GBS and ASD remains a mystery. Therefore, we have implemented well-established bioinformatic techniques to identify potential biomarkers and drug candidates for GBS and ASD. 17 common differentially expressed genes (DEGs) were identified for these two disorders, which later guided the rest of the research. Common genes identified the protein-protein interaction (PPI) network and pathways associated with both disorders. Based on the PPI network, the constructed hub gene and module analysis network determined two common DEGs, namely CXCL9 and CXCL10, which are vital in predicting the top drug candidates. Furthermore, coregulatory networks of TF-gene and TF-miRNA were built to detect the regulatory biomolecules. Among drug candidates, imatinib had the highest docking and MM-GBSA score with the well-known chemokine receptor CXCR3 and remained stable during the 100 ns molecular dynamics simulation validated by the principal component analysis and the dynamic cross-correlation map. This study predicted the gene-based disease network for GBS and ASD and suggested prospective drug candidates. However, more in-depth research is required for clinical validation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rizone Al Hasib
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
- Laboratory of Medical and Environmental Biotechnology Islamic University, Kushtia, Bangladesh
| | - Md Chayan Ali
- Department of Biochemistry, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Md Habibur Rahman
- Department of Computer Science and Engineering, Islamic University, Kushtia, Bangladesh
- Center for Advanced Bioinformatics and Artificial Intelligent Research, Islamic University, Kushtia, Bangladesh
| | - Sabbir Ahmed
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
| | - Shaharin Sultana
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
- Laboratory of Medical and Environmental Biotechnology Islamic University, Kushtia, Bangladesh
| | - Sadia Zannat Summa
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
- Laboratory of Medical and Environmental Biotechnology Islamic University, Kushtia, Bangladesh
| | | | - Zinia Afrin
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
| | - Mohammad Abu Hena Mostofa Jamal
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
- Laboratory of Medical and Environmental Biotechnology Islamic University, Kushtia, Bangladesh
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Zhu Z, Lu H, Zhao X, Sun Y, Yao J, Xue C, Huang B. circDYRK1A tethers biological behaviors of gastric carcinoma using novel bioinformatics analysis and experimental validations. Sci Rep 2023; 13:8265. [PMID: 37217530 DOI: 10.1038/s41598-023-33861-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/20/2023] [Indexed: 05/24/2023] Open
Abstract
Gastric cancer has been one of the wide public health burdens with its high morbidity and mortality over several decades. As the unconventional modules among RNA families, circular RNAs present their blazing biological effects during gastric carcinogenesis. Though diverse hypothetical mechanisms were reported, further tests were necessitated for authentication. Herein, this study pinpointed a representative circDYRK1A which screened from vast amounts of public data sets using surprisingly novel bioinformatics approaches together with validations from the in vitro findings and then concluded that circDYRK1A tethered the biological behavior and swayed the clinicopathological features with gastric cancer patients thus providing an in-depth awareness for gastric carcinoma.
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Affiliation(s)
- Zirui Zhu
- Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, No.155 Nanjing North Street, Heping District, Shenyang, 110001, China
- School of Medicine, Xiamen University, Xiamen, 361000, China
| | - Huiwen Lu
- Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, No.155 Nanjing North Street, Heping District, Shenyang, 110001, China
| | - Xu Zhao
- Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, No.155 Nanjing North Street, Heping District, Shenyang, 110001, China
| | - Yimeng Sun
- Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, No.155 Nanjing North Street, Heping District, Shenyang, 110001, China
| | - Junqiao Yao
- Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, No.155 Nanjing North Street, Heping District, Shenyang, 110001, China
| | - Chi Xue
- Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, No.155 Nanjing North Street, Heping District, Shenyang, 110001, China
| | - Baojun Huang
- Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, No.155 Nanjing North Street, Heping District, Shenyang, 110001, China.
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Wu T, Jin Y, Chen F, Xuan X, Cao J, Liang Y, Wang Y, Zhan J, Zhao M, Huang C. Identification and characterization of bone/cartilage-associated signatures in common fibrotic skin diseases. Front Genet 2023; 14:1121728. [PMID: 37082197 PMCID: PMC10111020 DOI: 10.3389/fgene.2023.1121728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/22/2023] [Indexed: 04/07/2023] Open
Abstract
Background: Fibrotic skin diseases are characterized by excessive accumulation of the extracellular matrix (ECM) and activation of fibroblasts, leading to a global healthcare burden. However, effective treatments of fibrotic skin diseases remain limited, and their pathological mechanisms require further investigation. This study aims to investigate the common biomarkers and therapeutic targets in two major fibrotic skin diseases, namely, keloid and systemic sclerosis (SSc), by bioinformatics analysis.Methods: The keloid (GSE92566) and SSc (GSE95065) datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We then constructed a protein–protein interaction (PPI) network for the identification of hub genes. We explored the possibility of further functional enrichment analysis of hub genes on the Metascape, GeneMANIA, and TissueNexus platforms. Transcription factor (TF)–hub gene and miRNA–hub gene networks were established using NetworkAnalyst. We fixed GSE90051 and GSE76855 as the external validation datasets. Student’s t-test and receiver operating characteristic (ROC) curve were used for candidate hub gene validation. Hub gene expression was assessed in vitro by quantitative real-time PCR.Results: A total of 157 overlapping DEGs (ODEGs) were retrieved from the GSE92566 and GSE95065 datasets, and five hub genes (COL11A1, COL5A2, ASPN, COL10A1, and COMP) were identified and validated. Functional studies revealed that hub genes were predominantly enriched in bone/cartilage-related and collagen-related processes. FOXC1 and miR-335-5p were predicted to be master regulators at both transcriptional and post‐transcriptional levels.Conclusion: COL11A1, COL5A2, ASPN, COL10A1, and COMP may help understand the pathological mechanism of the major fibrotic skin diseases; moreover, FOXC1 and miR-355-5p could build a regulatory network in keloid and SSc.
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Affiliation(s)
- Ting Wu
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yifan Jin
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fangqi Chen
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiuyun Xuan
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Juanmei Cao
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Liang
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuqing Wang
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinshan Zhan
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mengjie Zhao
- Department of Dermatology, Zhongnan Hospital of Wuhan University, Wuhan, China
- *Correspondence: Mengjie Zhao, ; Changzheng Huang,
| | - Changzheng Huang
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Mengjie Zhao, ; Changzheng Huang,
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12
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Ding H, Xie M, Wang J, Ouyang M, Huang Y, Yuan F, Jia Y, Zhang X, Liu N, Zhang N. Shared genetics of psychiatric disorders and type 2 diabetes:a large-scale genome-wide cross-trait analysis. J Psychiatr Res 2023; 159:185-195. [PMID: 36738649 DOI: 10.1016/j.jpsychires.2023.01.037] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 12/31/2022] [Accepted: 01/26/2023] [Indexed: 01/29/2023]
Abstract
BACKGROUND Individuals with psychiatric disorders have elevated rates of type 2 diabetes comorbidity. Although little is known about the shared genetics and causality of this association. Thus, we aimed to investigate shared genetics and causal link between different type 2 diabetes and psychiatric disorders. METHODS We conducted a large-scale genome-wide cross-trait association study(GWAS) to investigate genetic overlap between type 2 diabetes and anorexia nervosa, attention deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. By post-GWAS functional analysis, we identify variants genes expression in various tissues. Enrichment pathways, potential protein interaction and mendelian randomization also provided to research the relationship between type 2 diabetes and psychiatric disorders. RESULTS We discovered that type 2 diabetes and psychiatric disorders had a significant correlation. We identified 138 related loci, 32 were novel loci. Post-GWAS analysis revealed that 86 differentially expressed genes were located in different brain regions and peripheral blood in type 2 diabetes and related psychiatric disorders. MAPK signaling pathway plays an important role in neural development and insulin signaling. In addition, there is a causal relationship between T2D and mental disorders. In PPI analysis, the central genes of the DEG PPI network were FTO and TCF7L2. CONCLUSION This large-scale genome-wide cross-trait analysis identified shared genetics andpotential causal links between type 2 diabetes and related psychiatric disorders, suggesting potential new biological functions in common among them.
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Affiliation(s)
- Hui Ding
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Minyao Xie
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Jinyi Wang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Mengyuan Ouyang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Yanyuan Huang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Fangzheng Yuan
- School of Psychology, Nanjing Normal University, Nanjing, 210023, PR China
| | - Yunhan Jia
- School of Psychology, Nanjing Normal University, Nanjing, 210023, PR China
| | - Xuedi Zhang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Na Liu
- Department of Medical Psychology, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, PR China.
| | - Ning Zhang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China.
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Hossain MA, Rahman MH, Sultana H, Ahsan A, Rayhan SI, Hasan MI, Sohel M, Somadder PD, Moni MA. An integrated in-silico Pharmaco-BioInformatics approaches to identify synergistic effects of COVID-19 to HIV patients. Comput Biol Med 2023; 155:106656. [PMID: 36805222 PMCID: PMC9911982 DOI: 10.1016/j.compbiomed.2023.106656] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 01/18/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND With high inflammatory states from both COVID-19 and HIV conditions further result in complications. The ongoing confrontation between these two viral infections can be avoided by adopting suitable management measures. PURPOSE The aim of this study was to figure out the pharmacological mechanism behind apigenin's role in the synergetic effects of COVID-19 to the progression of HIV patients. METHOD We employed computer-aided methods to uncover similar biological targets and signaling pathways associated with COVID-19 and HIV, along with bioinformatics and network pharmacology techniques to assess the synergetic effects of apigenin on COVID-19 to the progression of HIV, as well as pharmacokinetics analysis to examine apigenin's safety in the human body. RESULT Stress-responsive, membrane receptor, and induction pathways were mostly involved in gene ontology (GO) pathways, whereas apoptosis and inflammatory pathways were significantly associated in the Kyoto encyclopedia of genes and genomes (KEGG). The top 20 hub genes were detected utilizing the shortest path ranked by degree method and protein-protein interaction (PPI), as well as molecular docking and molecular dynamics simulation were performed, revealing apigenin's strong interaction with hub proteins (MAPK3, RELA, MAPK1, EP300, and AKT1). Moreover, the pharmacokinetic features of apigenin revealed that it is an effective therapeutic agent with minimal adverse effects, for instance, hepatoxicity. CONCLUSION Synergetic effects of COVID-19 on the progression of HIV may still be a danger to global public health. Consequently, advanced solutions are required to give valid information regarding apigenin as a suitable therapeutic agent for the management of COVID-19 and HIV synergetic effects. However, the findings have yet to be confirmed in patients, suggesting more in vitro and in vivo studies.
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Affiliation(s)
- Md Arju Hossain
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
| | - Md Habibur Rahman
- Department of Computer Science and Engineering, Islamic University, Kushtia, 7003, Bangladesh; Center for Advanced Bioinformatics and Artificial Intelligent Research, Islamic University, Kushtia, 7003, Bangladesh.
| | - Habiba Sultana
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
| | - Asif Ahsan
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
| | - Saiful Islam Rayhan
- Department of Biochemistry and Molecular Biology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
| | - Md Imran Hasan
- Department of Computer Science and Engineering, Islamic University, Kushtia, 7003, Bangladesh
| | - Md Sohel
- Department of Biochemistry and Molecular Biology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
| | - Pratul Dipta Somadder
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia.
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14
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Alur V, Raju V, Vastrad B, Vastrad C, Kavatagimath S, Kotturshetti S. Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus. Clin Med Insights Endocrinol Diabetes 2023; 16:11795514231155635. [PMID: 36844983 PMCID: PMC9944228 DOI: 10.1177/11795514231155635] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 01/19/2023] [Indexed: 02/23/2023] Open
Abstract
Background Type 2 diabetes mellitus (T2DM) is the most common metabolic disorder. The aim of the present investigation was to identify gene signature specific to T2DM. Methods The next generation sequencing (NGS) dataset GSE81608 was retrieved from the gene expression omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs) between T2DM and normal controls. Then, Gene Ontology (GO) and pathway enrichment analysis, protein-protein interaction (PPI) network, modules, miRNA (micro RNA)-hub gene regulatory network construction and TF (transcription factor)-hub gene regulatory network construction, and topological analysis were performed. Receiver operating characteristic curve (ROC) analysis was also performed to verify the prognostic value of hub genes. Results A total of 927 DEGs (461 were up regulated and 466 down regulated genes) were identified in T2DM. GO and REACTOME results showed that DEGs mainly enriched in protein metabolic process, establishment of localization, metabolism of proteins, and metabolism. The top centrality hub genes APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1 were screened out as the critical genes. ROC analysis provides prognostic value of hub genes. Conclusion The potential crucial genes, especially APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1, might be linked with risk of T2DM. Our study provided novel insights of T2DM into genetics, molecular pathogenesis, and novel therapeutic targets.
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Affiliation(s)
- Varun Alur
- Department of Endocrinology, J.J.M
Medical College, Davanagere, Karnataka, India
| | - Varshita Raju
- Department of Obstetrics and
Gynecology, J.J.M Medical College, Davanagere, Karnataka, India
| | - Basavaraj Vastrad
- Department of Pharmaceutical Chemistry,
K.L.E. College of Pharmacy, Gadag, Karnataka, India
| | | | - Satish Kavatagimath
- Department of Pharmacognosy, K.L.E.
College of Pharmacy, Belagavi, Karnataka, India
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Hossain MA, Sohel M, Rahman MH, Hasan MI, Khan MS, Amin MA, Islam MZ, Peng S. Bioinformatics and In silico approaches to identify novel biomarkers and key pathways for cancers that are linked to the progression of female infertility: A comprehensive approach for drug discovery. PLoS One 2023; 18:e0265746. [PMID: 36608061 PMCID: PMC9821510 DOI: 10.1371/journal.pone.0265746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 03/07/2022] [Indexed: 01/07/2023] Open
Abstract
Despite modern treatment, infertility remains one of the most common gynecologic diseases causing severe health effects worldwide. The clinical and epidemiological data have shown that several cancerous risk factors are strongly linked to Female Infertility (FI) development, but the exact causes remain unknown. Understanding how these risk factors affect FI-affected cell pathways might pave the door for the discovery of critical signaling pathways and hub proteins that may be targeted for therapeutic intervention. To deal with this, we have used a bioinformatics pipeline to build a transcriptome study of FI with four carcinogenic risk factors: Endometrial Cancer (EC), Ovarian Cancer (OC), Cervical Cancer (CC), and Thyroid Cancer (TC). We identified FI sharing 97, 211, 87 and 33 differentially expressed genes (DEGs) with EC, OC, CC, and TC, respectively. We have built gene-disease association networks from the identified genes based on the multilayer network and neighbour-based benchmarking. Identified TNF signalling pathways, ovarian infertility genes, cholesterol metabolic process, and cellular response to cytokine stimulus were significant molecular and GO pathways, both of which improved our understanding the fundamental molecular mechanisms of cancers associated with FI progression. For therapeutic intervention, we have targeted the two most significant hub proteins VEGFA and PIK3R1, out of ten proteins based on Maximal Clique Centrality (MCC) value of cytoscape and literature analysis for molecular docking with 27 phytoestrogenic compounds. Among them, sesamin, galangin and coumestrol showed the highest binding affinity for VEGFA and PIK3R1 proteins together with favourable ADMET properties. We recommended that our identified pathway, hub proteins and phytocompounds may be served as new targets and therapeutic interventions for accurate diagnosis and treatment of multiple diseases.
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Affiliation(s)
- Md. Arju Hossain
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md Sohel
- Department of Biochemistry and Molecular Biology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md Habibur Rahman
- Department of Computer Science and Engineering, Islamic University, Kushtia, Bangladesh
- * E-mail:
| | - Md Imran Hasan
- Department of Computer Science and Engineering, Islamic University, Kushtia, Bangladesh
| | - Md. Sharif Khan
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md. Al Amin
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md. Zahidul Islam
- Department of Electronics, Graduate School of Engineering, Nagoya University, Nagoya, Japan
| | - Silong Peng
- Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
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Snigdha M, Akter A, Amin MA, Islam MZ. Bioinformatics approach to analyse COVID-19 biomarkers accountable for generation of intracranial aneurysm in COVID-19 patients. INFORMATICS IN MEDICINE UNLOCKED 2023; 39:101247. [PMID: 37159621 PMCID: PMC10141791 DOI: 10.1016/j.imu.2023.101247] [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: 03/20/2023] [Revised: 04/17/2023] [Accepted: 04/17/2023] [Indexed: 05/11/2023] Open
Abstract
COVID-19 became a health emergency on January 30, 2020. SARS-CoV-2 is the causative agent of the coronavirus disease known as COVID-19 and can develop cardiometabolic and neurological disorders. Intracranial aneurysm (IA) is considered the most significant reason for hemorrhagic stroke,and it accounts for approximately 85% of all subarachnoid hemorrhages (SAH). Retinoid signaling abnormalities may explain COVID-19's pathogenesis with inhibition of AEH2, from which COVID-19 infection may enhance aneurysm formation and rupture due to abrupt blood pressure changes, endothelial cell injury, and systemic inflammation. The objective of this study was to investigate the potential biomarkers, differentially expressed genes (DEGs), and metabolic pathways associated with both COVID-19 and intracranial aneurysm (IA) using simulation databases like DIsGeNET. The purpose was to confirm prior findings and gain a comprehensive understanding of the underlying mechanisms that contribute to the development of these conditions. We combined the regulated genes to describe intracranial aneurysm formation in COVID-19. To determine DEGs in COVID-19 and IA patient tissues, we compared gene expression transcriptomic datasets from healthy and diseased individuals. There were 41 differentially expressed genes (DEGs) shared by both the COVID-19 and IA datasets (27 up-regulated genes and 14 down-regulated genes). Using protein-protein interaction analysis, we were able to identify hub proteins (C3, NCR1, IL10RA, OXTR, RSAD2, CD38, IL10RB, MX1, IL10, GFAP, IFIT3, XAF1, USP18, OASL, IFI6, EPSTI1, CMPK2, and ISG15), which were not described as key proteins for both COVID-19 and IA before. We also used Gene Ontology analysis (6 significant ontologies were validated), Pathway analysis (the top 20 were validated), TF-Gene interaction analysis, Gene miRNA analysis, and Drug-Protein interaction analysis methods to comprehend the extensive connection between COVID-19 and IA. In Drug-Protein interaction analysis, we have gotten the following three drugs: LLL-3348, CRx139, and AV41 against IL10 which was both common for COVID-19 and IA disease. Our study with different cabalistic methods has showed the interaction between the proteins and pathways with drug analysis which may direct further treatment development for certain diseases.
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Affiliation(s)
- Mahajabin Snigdha
- Department of Pharmacy, Islamic University, Kushtia, 7003, Bangladesh
| | - Azifa Akter
- Department of Pharmacy, Islamic University, Kushtia, 7003, Bangladesh
| | - Md Al Amin
- Department of Computer Science & Engineering, Prime University, Dhaka, 1216, Bangladesh
| | - Md Zahidul Islam
- Department of Information & Communication Technology, Islamic University, Kushtia, 7003, Bangladesh
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Suresh NT, E R V, Krishnakumar U. Topology Driven Analysis of Protein - Protein Interactome for Prioritizing Key Comorbid Genes via Sub Graph Based Average Path Length Centrality. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:742-751. [PMID: 34986099 DOI: 10.1109/tcbb.2022.3140388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In gene-based therapies, local perturbations associated with one disease can lead to comorbidity as it influences the pathways involved with the other diseases. The key genes orchestrating the common biological mechanisms are need to be prioritized for addressing the challenges introduced by the cross talks between disease modules. Here, a local centrality measure named Sub graph based Average Path length Double Specific Betweenness centrality (SAPDSB) for prioritizing the comorbid genes via Protein-Protein Interaction Network (PPIN) analysis is presented. This approach can be used to identify putative biomarkers which can be repurposed for the management of comorbidity. Proposed network based topological measure is designed specifically to prioritize the comorbid genes that are most likely to be present in the overlap of disease modules. In order to attain this, the estimated average path length of the seed network which holds Protein-Protein Interactions (PPIs) of the disease genes is exploited. Prioritized comorbid genes are further pruned using centrality-based cut-off values and specificity scores. The biological significance of the resultant genes is corroborated with connectivity analysis using leave-one-out method, pathway enrichment analysis and a comparative analysis using single disease-based gene prioritization tools. For performance analysis, proposed approach is tested using case studies involving common diseases and rare neurodegenerative diseases. For case study1, diseases such as Diabetes, Carcinoma and Alzheimer's are considered in a pairwise manner while for case study2, Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) are considered. As outcome, prioritized candidate genes and biological pathways associated with respective disease pairs have been found. The associations from top 10 candidate genes in different disease pair combinations of Diabetes-Carcinoma-Alzheimer's revealed common genes like CREBBP, TP53, HSP90AA1 and the common pathway namely p53 pathway feedback loops 2. Out of the pathways retrieved from the top 10 genes associated with ALS-SMA disease pair, 60% of unique pathways are found to be leading to both diseases and its comorbidities. Comparative analysis of the proposed method with recent similar approach also reported a clear degree of benefits in performance.
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Bioinformatics approach to identify the core ontologies, pathways, signature genes and drug molecules of prostate cancer. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
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Hossain MA, Al Ashik SA, Mahin MR, Al Amin M, Rahman MH, Khan MA, Emran AA. Systems biology and in silico-based analysis of PCOS revealed the risk of metabolic disorders. Heliyon 2022; 8:e12480. [PMID: 36619413 PMCID: PMC9816984 DOI: 10.1016/j.heliyon.2022.e12480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 07/18/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
Background Polycystic ovarian syndrome (PCOS) is a common condition of hyperandrogenism, chronic ovulation, and polycystic ovaries in females during the reproduction and maturation of the ovum. Although PCOS has been associated with metabolic disorders, including type 2 diabetes (T2D), obesity (OBE), and cardiovascular disease (CVD), Causal connection and molecular features are still unknown. Purpose Therefore, we investigated the shared common differentially expressed genes (DEGs), pathways, and networks of associated proteins in PCOS and metabolic diseases with therapeutic intervention. Methods We have used a bioinformatics pipeline to analyze transcriptome data for the polycystic ovarian syndrome (PCOS), type 2 diabetes (T2D), obesity (OBE), and cardiovascular diseases (CVD) in female patients. Then we employed gene-disease association network, gene ontology (GO) and signaling pathway analysis, selection of hub genes from protein-protein interaction (PPI) network, molecular docking, and gold benchmarking approach to screen potential hub proteins. Result We discovered 2225 DEGs in PCOS patients relative to healthy controls and 34, 91, and 205 significant DEGs with T2D, Obesity, and CVD, respectively. Gene Ontology analysis revealed several significant shared and metabolic pathways from signaling pathway analysis. Furthermore, we identified ten potential hub proteins from PPI analysis that may serve as a therapeutic intervention in the future. Finally, we targeted one significant hub protein, IGF2R (PDB ID: 2V5O), out of ten hub proteins based on the Maximal clique centrality (MCC) algorithm and literature review for molecular docking study. Enzastaurin (-12.5), Kaempferol (-9.1), Quercetin (-9.0), and Coumestrol (-8.9) kcal/mol showed higher binding affinity in the molecular docking approach than 19 drug compounds. We have also found that the selected four compounds displayed favorable ADMET properties compared to the native ligand. Conclusion Our in-silico research findings identified a shared molecular etiology between PCOS and metabolic diseases that may suggest new therapeutic targets and warrants future experimental validation of the key targets.
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Affiliation(s)
- Md. Arju Hossain
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1092, Bangladesh
| | - Sheikh Abdullah Al Ashik
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1092, Bangladesh
| | - Moshiur Rahman Mahin
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1092, Bangladesh
| | - Md. Al Amin
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1092, Bangladesh
| | - Md Habibur Rahman
- Department of Computer Science and Engineering, Islamic University, Kushtia, 7003, Bangladesh
| | - Md. Arif Khan
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1092, Bangladesh
- Department of Biotechnology and Genetic Engineering, University of Development Alternative, 4/4B, Block A, Lalmatia, Dhaka, 1209, Bangladesh
| | - Abdullah Al Emran
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1092, Bangladesh
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Overexpression of Tfap2a in Mouse Oocytes Impaired Spindle and Chromosome Organization. Int J Mol Sci 2022; 23:ijms232214376. [PMID: 36430853 PMCID: PMC9699359 DOI: 10.3390/ijms232214376] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022] Open
Abstract
Transcription factor AP-2-alpha (Tfap2a) is an important sequence-specific DNA-binding protein that can regulate the transcription of multiple genes by collaborating with inducible viral and cellular enhancer elements. In this experiment, the expression, localization, and functions of Tfap2a were investigated in mouse oocytes during maturation. Overexpression via microinjection of Myc-Tfap2a mRNA into the ooplasm, immunofluorescence, and immunoblotting were used to study the role of Tfap2a in mouse oocyte meiosis. According to our results, Tfap2a plays a vital role in mouse oocyte maturation. Levels of Tfap2a in GV oocytes of mice suffering from type 2 diabetes increased considerably. Tfap2a was distributed in both the ooplasm and nucleoplasm, and its level gradually increased as meiosis resumption progressed. The overexpression of Tfap2a loosened the chromatin, accelerated germinal vesicle breakdown (GVBD), and blocked the first polar body extrusion 14 h after maturation in vitro. The width of the metaphase plate at metaphase I stage increased, and the spindle and chromosome organization at metaphase II stage were disrupted in the oocytes by overexpressed Tfap2a. Furthermore, Tfap2a overexpression dramatically boosted the expression of p300 in mouse GV oocytes. Additionally, the levels of pan histone lysine acetylation (Pan Kac), histone H4 lysine 12 acetylation (H4K12ac), and H4 lysine 16 acetylation (H4K16ac), as well as pan histone lysine lactylation (Pan Kla), histone H3 lysine18 lactylation (H3K18la), and H4 lysine12 lactylation (H4K12la), were all increased in GV oocytes after Tfap2a overexpression. Collectively, Tfap2a overexpression upregulated p300, increased the levels of histone acetylation and lactylation, impeded spindle assembly and chromosome alignment, and ultimately hindered mouse oocyte meiosis.
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MOLECULAR MIMICRY OF SARS-COV-2 SPIKE PROTEIN IN THE NERVOUS SYSTEM: A BIOINFORMATICS APPROACH. Comput Struct Biotechnol J 2022; 20:6041-6054. [PMID: 36317085 PMCID: PMC9605789 DOI: 10.1016/j.csbj.2022.10.022] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/15/2022] [Accepted: 10/15/2022] [Indexed: 11/18/2022] Open
Abstract
Introduction The development of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in record time to cope with the ongoing coronavirus disease 2019 (COVID-19) pandemic has led to uncertainty about their use and the appearance of adverse neurological reactions. The SARS-CoV-2 spike protein (SP) is used to produce neutralizing antibodies and stimulate innate immunity. However, considering the alterations in the nervous system (NS) caused by COVID- 19, cross-reactions are plausible. Objective To identify peptides in Homo sapiens SP-like proteins involved in myelin and axon homeostasis that may be affected due to molecular mimicry by antibodies and T cells induced by interaction with SP. Materials and methods A bioinformatics approach was used. To select the H. sapiens proteins to be studied, related biological processes categorized based on gene ontology were extracted through the construction of a protein–protein interaction network. Peripheral myelin protein 22, a major component of myelin in the peripheral nervous system, was used as the query protein. The extracellular domains and regions susceptible to recognition by antibodies were extracted from UniProt. In the study of T cells, linear sequence similarity between H. sapiens proteins and SP was assessed using BLASTp. This study considered the similarity in terms of biochemical groups per residue and affinity to the human major histocompatibility complex (human leukocyte antigen I), which were evaluated using Needle and NetMHCpan 4.1, respectively. Results A large number of shared pentapeptides between SP and H. sapiens proteins were identified. However, only a small group of 39 proteins was linked to axon and myelin homeostasis. In particular, some proteins, such as phosphacan, attractin, and teneurin-4, were susceptible targets of B and T cells. Other proteins closely related to myelin components in the NS, such as myelin-associated glycoprotein, were found to share at least one pentamer with SP in extracellular domains. Conclusion Proteins involved in the maintenance of nerve conduction in the central and peripheral NS were identified in H. sapiens. Based on these findings, re-evaluation of the vaccine composition is recommended to prevent possible neurological side effects.
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Roy D, Modi A, Ghosh R, Ghosh R, Benito-León J. Visceral Adipose Tissue Molecular Networks and Regulatory microRNA in Pediatric Obesity: An In Silico Approach. Int J Mol Sci 2022; 23:11036. [PMID: 36232337 PMCID: PMC9569899 DOI: 10.3390/ijms231911036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/05/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
Childhood obesity carries an increased risk of metabolic complications, sleep disturbances, and cancer. Visceral adiposity is independently associated with inflammation and insulin resistance in obese children. However, the underlying pathogenic mechanisms are still unclear. We aimed to detect the gene expression pattern and its regulatory network in the visceral adipose tissue of obese pediatric individuals. Using differentially-expressed genes (DEGs) identified from two publicly available datasets, GSE9624 and GSE88837, we performed functional enrichment, protein-protein interaction, and network analyses to identify pathways, targeting transcription factors (TFs), microRNA (miRNA), and regulatory networks. There were 184 overlapping DEGs with six significant clusters and 19 candidate hub genes. Furthermore, 24 TFs targeted these hub genes. The genes were regulated by miR-16-5p, miR-124-3p, miR-103a-3p, and miR-107, the top miRNA, according to a maximum number of miRNA-mRNA interaction pairs. The miRNA were significantly enriched in several pathways, including lipid metabolism, immune response, vascular inflammation, and brain development, and were associated with prediabetes, diabetic nephropathy, depression, solid tumors, and multiple sclerosis. The genes and miRNA detected in this study involve pathways and diseases related to obesity and obesity-associated complications. The results emphasize the importance of the TGF-β signaling pathway and its regulatory molecules, the immune system, and the adipocytic apoptotic pathway in pediatric obesity. The networks associated with this condition and the molecular mechanisms through which the potential regulators contribute to pathogenesis are open to investigation.
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Affiliation(s)
- Dipayan Roy
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Jodhpur 342005, Rajasthan, India
- Indian Institute of Technology (IIT), Madras 600036, Tamil Nadu, India
- School of Humanities, Indira Gandhi National Open University (IGNOU), New Delhi 110044, Delhi, India
| | - Anupama Modi
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Jodhpur 342005, Rajasthan, India
| | - Ritwik Ghosh
- Department of General Medicine, Burdwan Medical College & Hospital, Burdwan 713104, West Bengal, India
| | - Raghumoy Ghosh
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Jodhpur 342005, Rajasthan, India
- Lee Kong Chian School of Medicine, Nanyang Technological University (NTU), Singapore 636921, Singapore
| | - Julián Benito-León
- Department of Neurology, University Hospital “12 de Octubre”, Av. De Córdoba, s/n, 28041 Madrid, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Av. De Córdoba, s/n, 28041 Madrid, Spain
- Department of Medicine, Universidad Complutense, Pl. de Ramón y Cajal, s/n, 28040 Madrid, Spain
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Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease. Cardiovasc Ther 2022; 2022:9034996. [PMID: 36035865 PMCID: PMC9381297 DOI: 10.1155/2022/9034996] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/17/2022] [Accepted: 07/23/2022] [Indexed: 11/17/2022] Open
Abstract
Background Cardiovascular disease (CVD) is the combination of coronary heart disease, myocardial infarction, rheumatic heart disease, and peripheral vascular disease of the heart and blood vessels. It is one of the leading deadly diseases that causes one-third of the deaths yearly in the globe. Additionally, the risk factors associated with it make the situation more complex for cardiovascular patients, which lead them towards mortality, but the genetic association between CVD and its risk factors is not clearly explored in the global literature. We addressed this issue and explored the linkage between CVD and its risk factors. Methods We developed an analytical approach to reveal the risk factors and their linkages with CVD. We used GEO microarray datasets for the CVD and other risk factors in this study. We performed several analyses including gene expression analysis, diseasome analysis, protein-protein interaction (PPI) analysis, and pathway analysis for discovering the relationship between CVD and its risk factors. We also examined the validation of our study using gold benchmark databases OMIM, dbGAP, and DisGeNET. Results We observed that the number of 32, 17, 53, 70, and 89 differentially expressed genes (DEGs) is overlapped between CVD and its risk factors of hypertension (HTN), type 2 diabetes (T2D), hypercholesterolemia (HCL), obesity, and aging, respectively. We identified 10 major hub proteins (FPR2, TNF, CXCL8, CXCL1, IL1B, VEGFA, CYBB, PTGS2, ITGAX, and CCR5), 12 significant functional pathways, and 11 gene ontological pathways that are associated with CVD. We also found the connection of CVD with its risk factors in the gold benchmark databases. Our experimental outcomes indicate a strong association of CVD with its risk factors of HTN, T2D, HCL, obesity, and aging. Conclusions Our computational approach explored the genetic association of CVD with its risk factors by identifying the significant DEGs, hub proteins, and signaling and ontological pathways. The outcomes of this study may be further used in the lab-based analysis for developing the effective treatment strategies of CVD.
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Ripon Rouf ASM, Amin MA, Islam MK, Haque F, Ahmed KR, Rahman MA, Islam MZ, Kim B. Statistical Bioinformatics to Uncover the Underlying Biological Mechanisms That Linked Smoking with Type 2 Diabetes Patients Using Transcritpomic and GWAS Analysis. Molecules 2022; 27:molecules27144390. [PMID: 35889263 PMCID: PMC9323276 DOI: 10.3390/molecules27144390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/30/2022] [Accepted: 07/04/2022] [Indexed: 12/14/2022] Open
Abstract
Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30–40 percent higher risk of developing T2D in active smokers. Moreover, T2D patients with active smoking may gradually develop many complications. However, there is still no significant research conducted to solve the issue. Hence, we have proposed a highthroughput network-based quantitative pipeline employing statistical methods. Transcriptomic and GWAS data were analysed and obtained from type 2 diabetes patients and active smokers. Differentially Expressed Genes (DEGs) resulted by comparing T2D patients’ and smokers’ tissue samples to those of healthy controls of gene expression transcriptomic datasets. We have found 55 dysregulated genes shared in people with type 2 diabetes and those who smoked, 27 of which were upregulated and 28 of which were downregulated. These identified DEGs were functionally annotated to reveal the involvement of cell-associated molecular pathways and GO terms. Moreover, protein–protein interaction analysis was conducted to discover hub proteins in the pathways. We have also identified transcriptional and post-transcriptional regulators associated with T2D and smoking. Moreover, we have analysed GWAS data and found 57 common biomarker genes between T2D and smokers. Then, Transcriptomic and GWAS analyses are compared for more robust outcomes and identified 1 significant common gene, 19 shared significant pathways and 12 shared significant GOs. Finally, we have discovered protein–drug interactions for our identified biomarkers.
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Affiliation(s)
| | - Md. Al Amin
- Department of Computer Science & Engineering, Prime University, Dhaka 1216, Bangladesh;
| | - Md. Khairul Islam
- Department of Information & Communication Technology, Islamic University, Kushtia 7003, Bangladesh;
| | - Farzana Haque
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Kushtia 7003, Bangladesh;
| | - Kazi Rejvee Ahmed
- Department of Pathology, College of Korean Medicine, Kyung Hee University, Hoegidong Dongdaemungu, Seoul 02447, Korea;
| | - Md. Ataur Rahman
- Department of Pathology, College of Korean Medicine, Kyung Hee University, Hoegidong Dongdaemungu, Seoul 02447, Korea;
- Korean Medicine-Based Drug Repositioning Cancer Research Center, College of Korean Medicine, Kyung Hee University, Seoul 02447, Korea
- Correspondence: (M.A.R.); (M.Z.I.); (B.K.)
| | - Md. Zahidul Islam
- Department of Information & Communication Technology, Islamic University, Kushtia 7003, Bangladesh;
- Correspondence: (M.A.R.); (M.Z.I.); (B.K.)
| | - Bonglee Kim
- Department of Pathology, College of Korean Medicine, Kyung Hee University, Hoegidong Dongdaemungu, Seoul 02447, Korea;
- Korean Medicine-Based Drug Repositioning Cancer Research Center, College of Korean Medicine, Kyung Hee University, Seoul 02447, Korea
- Correspondence: (M.A.R.); (M.Z.I.); (B.K.)
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Al-Mustanjid M, Mahmud SMH, Akter F, Rahman MS, Hossen MS, Rahman MH, Moni MA. Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases. INFORMATICS IN MEDICINE UNLOCKED 2022; 32:101003. [PMID: 35818398 PMCID: PMC9259025 DOI: 10.1016/j.imu.2022.101003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 06/25/2022] [Accepted: 06/25/2022] [Indexed: 11/20/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been circulating since 2019, and its global dominance is rising. Evidences suggest the respiratory illness SARS-CoV-2 has a sensitive affect on causing organ damage and other complications to the patients with autoimmune diseases (AD), posing a significant risk factor. The genetic interrelationships and molecular appearances between SARS-CoV-2 and AD are yet unknown. We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on AD progression. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and AD affected tissues. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene disease (diseasesome) association network and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and AD. We found a total of 77, 21, 9, 54 common DEGs for SARS-CoV-2 and inflammatory bowel disorder (IBD), SARS-CoV-2 and rheumatoid arthritis (RA), SARS-CoV-2 and systemic lupus erythematosus (SLE) and SARS-CoV-2 and type 1 diabetes (T1D). The enclosure of these common DEGs with bimolecular networks revealed 10 hub proteins (FYN, VEGFA, CTNNB1, KDR, STAT1, B2M, CD3G, ITGAV, TGFB3). Drugs such as amlodipine besylate, vorinostat, methylprednisolone, and disulfiram have been identified as a common ground between SARS-CoV-2 and AD from drug repurposing investigation which will stimulate the optimal selection of medications in the battle against this ongoing pandemic triggered by COVID-19.
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Affiliation(s)
- Md Al-Mustanjid
- Department of Software Engineering, Faculty of Science and Information Technology, Daffodil International University, Dhaka-1207, Bangladesh
| | - S M Hasan Mahmud
- Department of Computer Science, American International University-Bangladesh, Dhaka, 1229, Bangladesh
| | - Farzana Akter
- Department of Software Engineering, Faculty of Science and Information Technology, Daffodil International University, Dhaka-1207, Bangladesh
| | - Md Shazzadur Rahman
- Department of Computer Science & Engineering, Faculty of Science and Information Technology, Daffodil International University, Dhaka-1207, Bangladesh
| | - Md Sajid Hossen
- Department of Software Engineering, Faculty of Science and Information Technology, Daffodil International University, Dhaka-1207, Bangladesh
| | - Md Habibur Rahman
- Department of Computer Science and Engineering, Islamic University, Kushtia-7003, Bangladesh
| | - Mohammad Ali Moni
- Department of Computer Science and Engineering, Pabna Science & Technology University, Pabna, 6600, Bangladesh
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Lu S, Wang J, Kakongoma N, Hua W, Xu J, Wang Y, He S, Gu H, Shi J, Hu W. DNA methylation and expression profiles of placenta and umbilical cord blood reveal the characteristics of gestational diabetes mellitus patients and offspring. Clin Epigenetics 2022; 14:69. [PMID: 35606885 PMCID: PMC9126248 DOI: 10.1186/s13148-022-01289-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 05/13/2022] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is a common pregnancy-specific disease and is growing at an alarming rate worldwide, which can negatively affect the health of pregnant women and fetuses. However, most studies are limited to one tissue, placenta or umbilical cord blood, usually with one omics assay. It is thus difficult to systematically reveal the molecular mechanism of GDM and the key influencing factors on pregnant women and offspring. RESULTS We recruited a group of 21 pregnant women with GDM and 20 controls without GDM. For each pregnant woman, reduced representation bisulfite sequencing and RNA-seq were performed using the placenta and paired neonatal umbilical cord blood specimens. Differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were identified with body mass index as a covariate. Through the comparison of GDM and control samples, 2779 and 141 DMRs, 1442 and 488 DEGs were identified from placenta and umbilical cord blood, respectively. Functional enrichment analysis showed that the placenta methylation and expression profiles of GDM women mirrored the molecular characteristics of "type II diabetes" and "insulin resistance." Methylation-altered genes in umbilical cord blood were associated with pathways "type II diabetes" and "cholesterol metabolism." Remarkably, both DMRs and DEGs illustrated significant overlaps among placenta and umbilical cord blood samples. The overlapping DMRs were associated with "cholesterol metabolism." The top-ranking pathways enriched in the shared DEGs include "growth hormone synthesis, secretion and action" and "type II diabetes mellitus." CONCLUSIONS Our research demonstrated the epigenetic and transcriptomic alternations of GDM women and offspring. Our findings emphasized the importance of epigenetic modifications in the communication between pregnant women with GDM and offspring, and provided a reference for the prevention, control, treatment, and intervention of perinatal deleterious events of GDM and neonatal complications.
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Affiliation(s)
- Sha Lu
- Department of Obstetrics and Gynecology, Hangzhou Women's Hospital (Hangzhou Maternity and Child Health Care Hospital), Hangzhou, Zhejiang, People's Republic of China
- The Affiliated Hangzhou Women's Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, People's Republic of China
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Jiahao Wang
- State Key Laboratory of Molecular Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Nisile Kakongoma
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Wen Hua
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Jiahui Xu
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China
| | - Yunfei Wang
- Hangzhou ShengTing Biotech Co. Ltd, Hangzhou, Zhejiang, People's Republic of China
| | - Shutao He
- State Key Laboratory of Molecular Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Hongcang Gu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, People's Republic of China
| | - Jiantao Shi
- State Key Laboratory of Molecular Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Wensheng Hu
- Department of Obstetrics and Gynecology, Hangzhou Women's Hospital (Hangzhou Maternity and Child Health Care Hospital), Hangzhou, Zhejiang, People's Republic of China.
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China.
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Hossain MA, Al Amin M, Hasan MI, Sohel M, Ahammed MA, Mahmud SH, Rahman MR, Rahman MH. Bioinformatics and system biology approaches to identify molecular pathogenesis of polycystic ovarian syndrome, type 2 diabetes, obesity, and cardiovascular disease that are linked to the progression of female infertility. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Integrated Bioinformatics Analysis and Verification of Gene Targets for Myocardial Ischemia-Reperfusion Injury. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:2056630. [PMID: 35463067 PMCID: PMC9033367 DOI: 10.1155/2022/2056630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 03/11/2022] [Accepted: 03/28/2022] [Indexed: 11/18/2022]
Abstract
Background Myocardial ischemia-reperfusion injury (MIRI) has become a thorny and unsolved clinical problem. The pathological mechanisms of MIRI are intricate and unclear, so it is of great significance to explore potential hub genes and search for some natural products that exhibit potential therapeutic efficacy on MIRI via targeting the hub genes. Methods First, the differential expression genes (DEGs) from GSE58486, GSE108940, and GSE115568 were screened and integrated via a robust rank aggregation algorithm. Then, the hub genes were identified and verified by the functional experiment of the MIRI mice. Finally, natural products with protective effects against MIRI were retrieved, and molecular docking simulations between hub genes and natural products were performed. Results 230 integrated DEGs and 9 hub genes were identified. After verification, Emr1, Tyrobp, Itgb2, Fcgr2b, Cybb, and Fcer1g might be the most significant genes during MIRI. A total of 75 natural products were discovered. Most of them (especially araloside C, glycyrrhizic acid, ophiopogonin D, polyphyllin I, and punicalagin) showed good ability to bind the hub genes. Conclusions Emr1, Tyrobp, Itgb2, Fcgr2b, Cybb, and Fcer1g might be critical in the pathological process of MIRI, and the natural products (araloside C, glycyrrhizic acid, ophiopogonin D, polyphyllin I, and punicalagin) targeting these hub genes exhibited potential therapeutic efficacy on MIRI. Our findings provided new insights to explore the mechanism and treatments for MIRI and revealed new therapeutic targets for natural products with protective properties against MIRI.
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UBE2D1 and COX7C as Potential Biomarkers of Diabetes-Related Sepsis. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9463717. [PMID: 35445133 PMCID: PMC9015863 DOI: 10.1155/2022/9463717] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/05/2021] [Accepted: 03/23/2022] [Indexed: 11/23/2022]
Abstract
Patients with diabetes are physiologically frail and more likely to suffer from infections and even life-threatening sepsis. This study aimed to identify and verify potential biomarkers of diabetes-related sepsis (DRS). Datasets GSE7014, GSE57065, and GSE95233 from the Gene Expression Omnibus were used to explore diabetes- and sepsis-related differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) and functional analyses were performed to explore potential functions and pathways associated with sepsis and diabetes. Weighted gene co-expression network analysis (WGCNA) was performed to identify diabetes- and sepsis-related modules. Functional enrichment analysis was performed to determine the characteristics and pathways of key modules. Intersecting DEGs that were also present in key modules were considered as common DEGs. Protein-protein interaction (PPI) network and key genes were analyzed to screen hub genes involved in DRS development. A mouse C57 BL/6J-DRS model and a neural network prediction model were constructed to verify the relationship between hub genes and DRS. In total, 7457 diabetes-related DEGs and 2606 sepsis-related DEGs were identified. GSEA indicated that gene datasets associated with diabetes and sepsis were mainly enriched in metabolic processes linked to inflammatory responses and reactive oxygen species, respectively. WGCNA indicated that grey60 and brown modules were diabetes- and sepsis-related key modules, respectively. Functional analysis showed that grey60 module genes were mainly enriched in cell morphogenesis, heart development, and the PI3K-Akt signaling pathway, whereas genes from the brown module were mainly enriched in organelle inner membrane, mitochondrion organization, and oxidative phosphorylation. UBE2D1, IDH1, DLD, ATP5C1, COX6C, and COX7C were identified as hub genes in the PPI network. Animal DRS and neural network prediction models indicated that the expression levels of UBE2D1 and COX7C in DRS models and samples were higher than control mice. UBE2D1 and COX7C were identified as potential biomarkers of DRS. These findings may help develop treatment strategies for DRS.
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Bioinformatics and Network-based Approaches for Determining Pathways, Signature Molecules, and Drug Substances connected to Genetic Basis of Schizophrenia etiology. Brain Res 2022; 1785:147889. [PMID: 35339428 DOI: 10.1016/j.brainres.2022.147889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/28/2022] [Accepted: 03/21/2022] [Indexed: 12/12/2022]
Abstract
Knowledge of heterogeneous etiology and pathophysiology of schizophrenia (SZP) is reasonably inadequate and non-deterministic due to its inherent complexity and underlying vast dynamics related to genetic mechanisms. The evolution of large-scale transcriptome-wide datasets and subsequent development of relevant, robust technologies for their analyses show promises toward elucidating the genetic basis of disease pathogenesis, its early risk prediction, and predicting drug molecule targets for therapeutic intervention. In this research, we have scrutinized the genetic basis of SZP through functional annotation and network-based system biology approaches. We have determined 96 overlapping differentially expressed genes (DEGs) from 2 microarray datasets and subsequently identified their interconnecting networks to reveal transcriptome signatures like hub proteins (FYN, RAD51, SOCS3, XIAP, AKAP13, PIK3C2A, CBX5, GATA3, EIF3K, and CDKN2B), transcription factors and miRNAs. In addition, we have employed gene set enrichment to highlight significant gene ontology (e.g., positive regulation of microglial cell activation) and relevant pathways (such as axon guidance and focal adhesion) interconnected to the genes associated with SZP. Finally, we have suggested candidate drug substances like Luteolin HL60 UP as a possible therapeutic target based on these key molecular signatures.
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Identification of Genetic Risk Factors of Severe COVID-19 Using Extensive Phenotypic Data: A Proof-of-Concept Study in a Cohort of Russian Patients. Genes (Basel) 2022; 13:genes13030534. [PMID: 35328087 PMCID: PMC8949130 DOI: 10.3390/genes13030534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/13/2022] [Accepted: 03/14/2022] [Indexed: 11/17/2022] Open
Abstract
The COVID-19 pandemic has drawn the attention of many researchers to the interaction between pathogen and host genomes. Over the last two years, numerous studies have been conducted to identify the genetic risk factors that predict COVID-19 severity and outcome. However, such an analysis might be complicated in cohorts of limited size and/or in case of limited breadth of genome coverage. In this work, we tried to circumvent these challenges by searching for candidate genes and genetic variants associated with a variety of quantitative and binary traits in a cohort of 840 COVID-19 patients from Russia. While we found no gene- or pathway-level associations with the disease severity and outcome, we discovered eleven independent candidate loci associated with quantitative traits in COVID-19 patients. Out of these, the most significant associations correspond to rs1651553 in MYH14p = 1.4 × 10-7), rs11243705 in SETX (p = 8.2 × 10-6), and rs16885 in ATXN1 (p = 1.3 × 10-5). One of the identified variants, rs33985936 in SCN11A, was successfully replicated in an independent study, and three of the variants were found to be associated with blood-related quantitative traits according to the UK Biobank data (rs33985936 in SCN11A, rs16885 in ATXN1, and rs4747194 in CDH23). Moreover, we show that a risk score based on these variants can predict the severity and outcome of hospitalization in our cohort of patients. Given these findings, we believe that our work may serve as proof-of-concept study demonstrating the utility of quantitative traits and extensive phenotyping for identification of genetic risk factors of severe COVID-19.
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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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Hasan MM, Khan Z, Chowdhury MS, Khan MA, Moni MA, Rahman MH. In silico molecular docking and ADME/T analysis of Quercetin compound with its evaluation of broad-spectrum therapeutic potential against particular diseases. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100894] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
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Hasan I, Hossain A, Bhuiyan P, Miah S, Rahman H. A system biology approach to determine therapeutic targets by identifying molecular mechanisms and key pathways for type 2 diabetes that are linked to the development of tuberculosis and rheumatoid arthritis. Life Sci 2022; 297:120483. [DOI: 10.1016/j.lfs.2022.120483] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/07/2022] [Accepted: 03/09/2022] [Indexed: 12/17/2022]
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Mahbub NI, Hasan MI, Rahman MH, Naznin F, Islam MZ, Moni MA. Identifying molecular signatures and pathways shared between Alzheimer's and Huntington's disorders: A bioinformatics and systems biology approach. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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Shu J, Wei W, Zhang L. Identification of Molecular Signatures and Candidate Drugs in Vascular Dementia by Bioinformatics Analyses. Front Mol Neurosci 2022; 15:751044. [PMID: 35221911 PMCID: PMC8873373 DOI: 10.3389/fnmol.2022.751044] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/17/2022] [Indexed: 01/30/2023] Open
Abstract
Vascular dementia (VaD) is considered to be the second most common form of dementia after Alzheimer’s disease, and no specific drugs have been approved for VaD treatment. We aimed to identify shared transcriptomic signatures between the frontal cortex and temporal cortex in VaD by bioinformatics analyses. Gene ontology and pathway enrichment analyses, protein–protein interaction (PPI) and hub gene identification, hub gene–transcription factor interaction, hub gene–microRNA interaction, and hub gene–drug interaction analyses were performed. We identified 159 overlapping differentially expressed genes (DEGs) between the frontal cortex and temporal cortex that were enriched mainly in inflammation and innate immunity, synapse pruning, regeneration, positive regulation of angiogenesis, response to nutrient levels, and positive regulation of the digestive system process. We identified 10 hub genes in the PPI network (GNG13, CD163, C1QA, TLR2, SST, C1QB, ITGB2, CCR5, CRH, and TAC1), four central regulatory transcription factors (FOXC1, CREB1, GATA2, and HINFP), and four microRNAs (miR-27a-3p, miR-146a-5p, miR-335-5p, and miR-129-2-3p). Hub gene–drug interaction analysis found four drugs (maraviroc, cenicriviroc, PF-04634817, and efalizumab) that could be potential drugs for VaD treatment. Together, our results may contribute to understanding the underlying mechanisms in VaD and provide potential targets and drugs for therapeutic intervention.
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Machine learning models for classification and identification of significant attributes to detect type 2 diabetes. Health Inf Sci Syst 2022; 10:2. [PMID: 35178244 PMCID: PMC8828812 DOI: 10.1007/s13755-021-00168-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 12/15/2022] Open
Abstract
Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population.
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Shu J, Li N, Wei W, Zhang L. Detection of molecular signatures and pathways shared by Alzheimer's disease and type 2 diabetes. Gene 2022; 810:146070. [PMID: 34813915 DOI: 10.1016/j.gene.2021.146070] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 10/21/2021] [Accepted: 11/16/2021] [Indexed: 01/12/2023]
Abstract
Alzheimer's disease (AD) and type 2 diabetes (T2D) are common in the general elderly population, conferring heavy individual, social, and economic stresses on families and society. Accumulating evidence indicates T2D to be a risk factor for AD. However, the underlying mechanisms for this association are largely unknown. This study aimed to identify the shared molecular signatures between AD and T2D through integrated analysis of temporal cortex gene expression data. Gene Ontology (GO) and pathway enrichment analysis, protein over-representation analysis, protein-protein interaction, DEG-transcription factor interactions, DEG-microRNA interactions, protein-drug interactions, gene-disease association analysis, and protein subcellular localization analysis of the common DEGs were performed. We identified 16 common DEGs between the two datasets, which were mainly enriched in the biological processes of apoptosis, autophagy, inflammation, and hemostasis. We also identified five hub proteins encoded by the DEGs, five central regulatory transcription factors, and six microRNAs. Protein-drug interaction analysis showed C1QB to be associated with different drugs. Gene-disease association analysis revealed that hub genes, SFN and ITGB2, were actively engaged in other diseases. Collectively, these findings provide new insights into shared molecular mechanisms between AD and T2D and provide novel candidate targets for therapeutic intervention.
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Affiliation(s)
- Jun Shu
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, No. 221, West Yan An Road, Shanghai, China
| | - Nan Li
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, No. 221, West Yan An Road, Shanghai, China
| | - Wenshi Wei
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, No. 221, West Yan An Road, Shanghai, China.
| | - Li Zhang
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, No. 221, West Yan An Road, Shanghai, China.
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Determination of molecular signatures and pathways common to brain tissues of autism spectrum disorder: Insights from comprehensive bioinformatics approach. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100871] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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40
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Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers. Heliyon 2022; 8:e08892. [PMID: 35198765 PMCID: PMC8841363 DOI: 10.1016/j.heliyon.2022.e08892] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 08/04/2021] [Accepted: 01/29/2022] [Indexed: 01/11/2023] Open
Abstract
Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.
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Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19. INFORMATICS IN MEDICINE UNLOCKED 2022; 28:100840. [PMID: 34981034 PMCID: PMC8716147 DOI: 10.1016/j.imu.2021.100840] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 12/27/2021] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.
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El Idrissi F, Fruchart M, Belarbi K, Lamer A, Dubois-Deruy E, Lemdani M, N’Guessan AL, Guinhouya BC, Zitouni D. Exploration of the core protein network under endometriosis symptomatology using a computational approach. Front Endocrinol (Lausanne) 2022; 13:869053. [PMID: 36120440 PMCID: PMC9478376 DOI: 10.3389/fendo.2022.869053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Endometriosis is defined by implantation and invasive growth of endometrial tissue in extra-uterine locations causing heterogeneous symptoms, and a unique clinical picture for each patient. Understanding the complex biological mechanisms underlying these symptoms and the protein networks involved may be useful for early diagnosis and identification of pharmacological targets. METHODS In the present study, we combined three approaches (i) a text-mining analysis to perform a systematic search of proteins over existing literature, (ii) a functional enrichment analysis to identify the biological pathways in which proteins are most involved, and (iii) a protein-protein interaction (PPI) network to identify which proteins modulate the most strongly the symptomatology of endometriosis. RESULTS Two hundred seventy-eight proteins associated with endometriosis symptomatology in the scientific literature were extracted. Thirty-five proteins were selected according to degree and betweenness scores criteria. The most enriched biological pathways associated with these symptoms were (i) Interleukin-4 and Interleukin-13 signaling (p = 1.11 x 10-16), (ii) Signaling by Interleukins (p = 1.11 x 10-16), (iii) Cytokine signaling in Immune system (p = 1.11 x 10-16), and (iv) Interleukin-10 signaling (p = 5.66 x 10-15). CONCLUSION Our study identified some key proteins with the ability to modulate endometriosis symptomatology. Our findings indicate that both pro- and anti-inflammatory biological pathways may play important roles in the symptomatology of endometriosis. This approach represents a genuine systemic method that may complement traditional experimental studies. The current data can be used to identify promising biomarkers for early diagnosis and potential therapeutic targets.
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Affiliation(s)
- Fatima El Idrissi
- Univ. Lille, UFR 3S, Faculté Ingénierie et Management de la Santé, Lille, France
- Univ. Lille, UFR 3S, Faculté de Pharmacie, Lille, France
| | - Mathilde Fruchart
- Univ. Lille, UFR 3S, Faculté Ingénierie et Management de la Santé, Lille, France
- Univ. Lille, CHU Lille, ULR 2694 - METRICS, Lille, France
| | - Karim Belarbi
- Univ. Lille, UFR 3S, Faculté de Pharmacie, Lille, France
- Univ. Lille, Inserm, CHU-Lille, Lille Neuroscience & Cognition, Lille, France
| | - Antoine Lamer
- Univ. Lille, UFR 3S, Faculté Ingénierie et Management de la Santé, Lille, France
- Univ. Lille, CHU Lille, ULR 2694 - METRICS, Lille, France
| | - Emilie Dubois-Deruy
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE - Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, Lille, France
| | - Mohamed Lemdani
- Univ. Lille, UFR 3S, Faculté de Pharmacie, Lille, France
- Univ. Lille, CHU Lille, ULR 2694 - METRICS, Lille, France
| | - Assi L. N’Guessan
- Univ. Lille, UMR CNRS 8524, Laboratoire Paul Painlevé, Villeneuve d’Ascq, Cedex, France
| | - Benjamin C. Guinhouya
- Univ. Lille, UFR 3S, Faculté Ingénierie et Management de la Santé, Lille, France
- Univ. Lille, CHU Lille, ULR 2694 - METRICS, Lille, France
- *Correspondence: Benjamin C. Guinhouya,
| | - Djamel Zitouni
- Univ. Lille, UFR 3S, Faculté de Pharmacie, Lille, France
- Univ. Lille, CHU Lille, ULR 2694 - METRICS, Lille, France
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Identification of molecular signatures and pathways common to blood cells and brain tissue based RNA-Seq datasets of bipolar disorder: Insights from comprehensive bioinformatics approach. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100881] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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44
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Rahman MH, Rana HK, Peng S, Kibria MG, Islam MZ, Mahmud SMH, Moni MA. Bioinformatics and system biology approaches to identify pathophysiological impact of COVID-19 to the progression and severity of neurological diseases. Comput Biol Med 2021; 138:104859. [PMID: 34601390 PMCID: PMC8483812 DOI: 10.1016/j.compbiomed.2021.104859] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/21/2021] [Accepted: 09/06/2021] [Indexed: 02/06/2023]
Abstract
The Coronavirus Disease 2019 (COVID-19) still tends to propagate and increase the occurrence of COVID-19 across the globe. The clinical and epidemiological analyses indicate the link between COVID-19 and Neurological Diseases (NDs) that drive the progression and severity of NDs. Elucidating why some patients with COVID-19 influence the progression of NDs and patients with NDs who are diagnosed with COVID-19 are becoming increasingly sick, although others are not is unclear. In this research, we investigated how COVID-19 and ND interact and the impact of COVID-19 on the severity of NDs by performing transcriptomic analyses of COVID-19 and NDs samples by developing the pipeline of bioinformatics and network-based approaches. The transcriptomic study identified the contributing genes which are then filtered with cell signaling pathway, gene ontology, protein-protein interactions, transcription factor, and microRNA analysis. Identifying hub-proteins using protein-protein interactions leads to the identification of a therapeutic strategy. Additionally, the incorporation of comorbidity interactions score enhances the identification beyond simply detecting novel biological mechanisms involved in the pathophysiology of COVID-19 and its NDs comorbidities. By computing the semantic similarity between COVID-19 and each of the ND, we have found gene-based maximum semantic score between COVID-19 and Parkinson's disease, the minimum semantic score between COVID-19 and Multiple sclerosis. Similarly, we have found gene ontology-based maximum semantic score between COVID-19 and Huntington disease, minimum semantic score between COVID-19 and Epilepsy disease. Finally, we validated our findings using gold-standard databases and literature searches to determine which genes and pathways had previously been associated with COVID-19 and NDs.
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Affiliation(s)
- Md Habibur Rahman
- Dept. of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh
| | - Humayan Kabir Rana
- Dept. of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh
| | - Silong Peng
- Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Md Golam Kibria
- Dept. of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Canada
| | - Md Zahidul Islam
- Department of Electronics, Graduate School of Engineering, Nagoya University, Japan
| | - S M Hasan Mahmud
- Dept. of Computer Science, American International University Bangladesh, Dhaka, Bangladesh
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia.
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Rahman MH, Rana HK, Peng S, Hu X, Chen C, Quinn JMW, Moni MA. Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression. Brief Bioinform 2021; 22:6066369. [PMID: 33406529 DOI: 10.1093/bib/bbaa365] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 10/25/2020] [Accepted: 11/11/2020] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.
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Affiliation(s)
- Md Habibur Rahman
- Institute of Automation Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China.,Department of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh
| | - Humayan Kabir Rana
- Department of Computer Science and Engineering, Green University of Bangladesh, Bangladesh
| | - Silong Peng
- Institute of Automation Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xiyuan Hu
- Institute of Automation Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Chen Chen
- Institute of Automation Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Julian M W Quinn
- Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,The Surgical Education and Research Training Institute, Royal North Shore Hospital, Sydney, Australia
| | - Mohammad Ali Moni
- Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,WHO Collaborating Centre on eHealth, School of Public Health and Community Medicine, Faculty of Medicine, The University of New South Wales, Sydney, Australia
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Raza W, Guo J, Qadir MI, Bai B, Muhammad SA. qPCR Analysis Reveals Association of Differential Expression of SRR, NFKB1, and PDE4B Genes With Type 2 Diabetes Mellitus. Front Endocrinol (Lausanne) 2021; 12:774696. [PMID: 35046895 PMCID: PMC8761634 DOI: 10.3389/fendo.2021.774696] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/08/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is a heterogeneous, metabolic, and chronic condition affecting vast numbers of the world's population. The related variables and T2DM associations have not been fully understood due to their diverse nature. However, functional genomics can facilitate understanding of the disease. This information will be useful in drug design, advanced diagnostic, and prognostic markers. AIM To understand the genetic causes of T2DM, this study was designed to identify the differentially expressed genes (DEGs) of the disease. METHODS We investigated 20 publicly available disease-specific cDNA datasets from Gene Expression Omnibus (GEO) containing several attributes including gene symbols and clone identifiers, GenBank accession numbers, and phenotypic feature coordinates. We analyzed an integrated system-level framework involving Gene Ontology (GO), protein motifs and co-expression analysis, pathway enrichment, and transcriptional factors to reveal the biological information of genes. A co-expression network was studied to highlight the genes that showed a coordinated expression pattern across a group of samples. The DEGs were validated by quantitative PCR (qPCR) to analyze the expression levels of case and control samples (50 each) using glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as the reference gene. RESULTS From the list of 50 DEGs, we ranked three T2DM-related genes (p < 0.05): SRR, NFKB1, and PDE4B. The enriched terms revealed a significant functional role in amino acid metabolism, signal transduction, transmembrane and intracellular transport, and other vital biological functions. DMBX1, TAL1, ZFP161, NFIC (66.7%), and NR1H4 (33.3%) are transcriptional factors associated with the regulatory mechanism. We found substantial enrichment of insulin signaling and other T2DM-related pathways, such as valine, leucine and isoleucine biosynthesis, serine and threonine metabolism, adipocytokine signaling pathway, P13K/Akt pathway, and Hedgehog signaling pathway. The expression profiles of these DEGs verified by qPCR showed a substantial level of twofold change (FC) expression (2-ΔΔCT) in the genes SRR (FC ≤ 0.12), NFKB1 (FC ≤ 1.09), and PDE4B (FC ≤ 0.9) compared to controls (FC ≥ 1.6). The downregulated expression of these genes is associated with pathophysiological development and metabolic disorders. CONCLUSION This study would help to modulate the therapeutic strategies for T2DM and could speed up drug discovery outcomes.
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Affiliation(s)
- Waseem Raza
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan
| | - Jinlei Guo
- School of Medical Engineering, Sanquan College of Xinxiang Medical University, Xinxiang, China
| | - Muhammad Imran Qadir
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan
| | - Baogang Bai
- School of Information and Technology, Wenzhou Business College, Wenzhou, China
- Engineering Research Center of Intelligent Medicine, Wenzhou, China
- The 1st School of Medical, School of Information and Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- *Correspondence: Syed Aun Muhammad, ; Baogang Bai,
| | - Syed Aun Muhammad
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan
- *Correspondence: Syed Aun Muhammad, ; Baogang Bai,
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A system biological approach to investigate the genetic profiling and comorbidities of type 2 diabetes. GENE REPORTS 2020. [DOI: 10.1016/j.genrep.2020.100830] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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48
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Bi HR, Zhou CH, Zhang YZ, Cai XD, Ji MH, Yang JJ, Chen GQ, Hu YM. Neuron-specific deletion of presenilin enhancer2 causes progressive astrogliosis and age-related neurodegeneration in the cortex independent of the Notch signaling. CNS Neurosci Ther 2020; 27:174-185. [PMID: 32961023 PMCID: PMC7816208 DOI: 10.1111/cns.13454] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 08/19/2020] [Accepted: 08/24/2020] [Indexed: 12/18/2022] Open
Abstract
Introduction Presenilin enhancer2 (Pen‐2) is an essential subunit of γ‐secretase, which is a key protease responsible for the cleavage of amyloid precursor protein (APP) and Notch. Mutations on Pen‐2 cause familial Alzheimer disease (AD). However, it remains unknown whether Pen‐2 regulates neuronal survival and neuroinflammation in the adult brain. Methods Forebrain neuron‐specific Pen‐2 conditional knockout (Pen‐2 cKO) mice were generated for this study. Pen‐2 cKO mice expressing Notch1 intracellular domain (NICD) conditionally in cortical neurons were also generated. Results Loss of Pen‐2 causes astrogliosis followed by age‐dependent cortical atrophy and neuronal loss. Loss of Pen‐2 results in microgliosis and enhanced inflammatory responses in the cortex. Expression of NICD in Pen‐2 cKO cortices ameliorates neither neurodegeneration nor neuroinflammation. Conclusions Pen‐2 is required for neuronal survival in the adult cerebral cortex. The Notch signaling may not be involved in neurodegeneration caused by loss of Pen‐2.
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Affiliation(s)
- Hui-Ru Bi
- Model Animal Research Center, MOE Key Laboratory of Model Animal for Disease Study, Medical School, Nanjing University, Nanjing, China
| | - Cui-Hua Zhou
- Department of Anesthesiology, The Second Affiliated Changzhou People's Hospital of Nanjing Medical University, Changzhou, China
| | - Yi-Zhi Zhang
- Model Animal Research Center, MOE Key Laboratory of Model Animal for Disease Study, Medical School, Nanjing University, Nanjing, China
| | - Xu-Dong Cai
- Model Animal Research Center, MOE Key Laboratory of Model Animal for Disease Study, Medical School, Nanjing University, Nanjing, China
| | - Mu-Huo Ji
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Jun Yang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Gui-Quan Chen
- Model Animal Research Center, MOE Key Laboratory of Model Animal for Disease Study, Medical School, Nanjing University, Nanjing, China
| | - Yi-Min Hu
- Department of Anesthesiology, The Second Affiliated Changzhou People's Hospital of Nanjing Medical University, Changzhou, China
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Systems biology and bioinformatics approach to identify gene signatures, pathways and therapeutic targets of Alzheimer's disease. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100439] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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50
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A systems biology approach to identifying genetic factors affected by aging, lifestyle factors, and type 2 diabetes that influences Parkinson's disease progression. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100448] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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