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Wang BY, Song K, Wang HT, Wang SS, Wang WJ, Li ZW, Du WY, Xue FZ, Zhao L, Cao WC. Comorbidity increases the risk of pulmonary tuberculosis: a nested case-control study using multi-source big data. BMC Pulm Med 2024; 24:29. [PMID: 38212743 PMCID: PMC10782630 DOI: 10.1186/s12890-023-02817-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 12/14/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Some medical conditions may increase the risk of developing pulmonary tuberculosis (PTB); however, no systematic study on PTB-associated comorbidities and comorbidity clusters has been undertaken. METHODS A nested case-control study was conducted from 2013 to 2017 using multi-source big data. We defined cases as patients with incident PTB, and we matched each case with four event-free controls using propensity score matching (PSM). Comorbidities diagnosed prior to PTB were defined with the International Classification of Diseases-10 (ICD-10). The longitudinal relationships between multimorbidity burden and PTB were analyzed using a generalized estimating equation. The associations between PTB and 30 comorbidities were examined using conditional logistic regression, and the comorbidity clusters were identified using network analysis. RESULTS A total of 4265 cases and 17,060 controls were enrolled during the study period. A total of 849 (19.91%) cases and 1141 (6.69%) controls were multimorbid before the index date. Having 1, 2, and ≥ 3 comorbidities was associated with an increased risk of PTB (aOR 2.85-5.16). Fourteen out of thirty comorbidities were significantly associated with PTB (aOR 1.28-7.27), and the associations differed by sex and age. Network analysis identified three major clusters, mainly in the respiratory, circulatory, and endocrine/metabolic systems, in PTB cases. CONCLUSIONS Certain comorbidities involving multiple systems may significantly increase the risk of PTB. Enhanced awareness and surveillance of comorbidity are warranted to ensure early prevention and timely control of PTB.
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Affiliation(s)
- Bao-Yu Wang
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Ke Song
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Hai-Tao Wang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Shan-Shan Wang
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Wen-Jing Wang
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Zhen-Wei Li
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Wan-Yu Du
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Fu-Zhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 250012, Jinan, China
- Institute for Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250002, China
| | - Lin Zhao
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
| | - Wu-Chun Cao
- Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 20 Dongda Street, Fengtai District, Beijing, 100071, China.
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Chen X, Xue B, Wahab S, Sultan A, Khalid M, Yang S. Structure-based molecular docking and molecular dynamics simulations study for the identification of dipeptidyl peptidase 4 inhibitors in type 2 diabetes. J Biomol Struct Dyn 2023:1-14. [PMID: 38100564 DOI: 10.1080/07391102.2023.2291831] [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: 03/24/2023] [Accepted: 11/23/2023] [Indexed: 12/17/2023]
Abstract
Inhibition of dipeptidyl peptidase-4 (DPP4) activity has emerged as a promising therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM). Bioinformatics-driven approaches have emerged as crucial tools in drug discovery. Molecular docking and molecular dynamics (MD) simulations are effective tools in drug discovery, as they reduce the time and cost associated with experimental screening. In this study, we employed structure-assisted in-silico methods, including molecular docking and MD simulations, to identify SRT2183, a small molecule that may potentially inhibit the activity of DPP4 enzyme. The interaction between the small molecule "SRT2183" and DPP4 exhibited a binding affinity of -9.9 Kcal/Mol, leading to the formation of hydrogen bonds with the amino acid residues MET348, SER376, and THR351 of DPP4. The MD simulations over a period of 100 ns indicated stable protein-ligand interactions, with no significant conformational rearrangements observed within the simulated timeframe. In conclusion, our results suggest that the small molecule SRT2183 may have the potential to inhibit the DPP4 enzyme and pave the way for the therapeutics of T2DM.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Xi Chen
- School of Management, Guangzhou College of Technology and Business, Guangzhou, China
| | - Bin Xue
- School of Engineering, Guangzhou College of Technology and Business, Guangzhou, China
| | - Shadma Wahab
- Department of Pharmacognosy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia
| | - Armiya Sultan
- Department of Biotechnology, Jamia Millia Islamia, New Delhi, India
| | - Mohammad Khalid
- Department of Pharmacognosy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Song Yang
- Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln, New Zealand
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Zhou P, Shen J, Ge X, Ding F, Zhang H, Huang X, Zhao C, Li M, Li Z. Classification and characterisation of extracellular vesicles-related tuberculosis subgroups and immune cell profiles. J Cell Mol Med 2023; 27:2482-2494. [PMID: 37409682 PMCID: PMC10468662 DOI: 10.1111/jcmm.17836] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/23/2023] [Accepted: 06/24/2023] [Indexed: 07/07/2023] Open
Abstract
Around the world, tuberculosis (TB) remains one of the most common causes of morbidity and mortality. The molecular mechanism of Mycobacterium tuberculosis (Mtb) infection is still unclear. Extracellular vesicles (EVs) play a key role in the onset and progression of many disease states and can serve as effective biomarkers or therapeutic targets for the identification and treatment of TB patients. We analysed the expression profile to better clarify the EVs characteristics of TB and explored potential diagnostic markers to distinguish TB from healthy control (HC). Twenty EVs-related differentially expressed genes (DEGs) were identified, and 17 EVs-related DEGs were up-regulated and three DEGs were down-regulated in TB samples, which were related to immune cells. Using machine learning, a nine EVs-related gene signature was identified and two EVs-related subclusters were defined. The single-cell RNA sequence (scRNA-seq) analysis further confirmed that these hub genes might play important roles in TB pathogenesis. The nine EVs-related hub genes had excellent diagnostic values and accurately estimated TB progression. TB's high-risk group had significantly enriched immune-related pathways, and there were substantial variations in immunity across different groups. Furthermore, five potential drugs were predicted for TB using CMap database. Based on the EVs-related gene signature, the TB risk model was established through a comprehensive analysis of different EV patterns, which can accurately predict TB. These genes could be used as novel biomarkers to distinguish TB from HC. These findings lay the foundation for further research and design of new therapeutic interventions aimed at treating this deadly infectious disease.
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Affiliation(s)
- Peipei Zhou
- School of Medical LaboratoryWeifang Medical UniversityWeifangChina
| | - Jie Shen
- School of Medical LaboratoryWeifang Medical UniversityWeifangChina
| | - Xiao Ge
- School of Medical LaboratoryWeifang Medical UniversityWeifangChina
| | - Fang Ding
- Respiratory MedicineAffiliated Hospital of Weifang Medical UniversityWeifangChina
| | - Hong Zhang
- School of Public HealthWeifang Medical UniversityWeifangChina
| | - Xinlin Huang
- School of Medical LaboratoryWeifang Medical UniversityWeifangChina
| | - Chao Zhao
- Office of Academic AffairsWeifang Medical UniversityWeifangChina
| | - Meng Li
- School of Medical LaboratoryWeifang Medical UniversityWeifangChina
| | - Zhenpeng Li
- School of Medical LaboratoryWeifang Medical UniversityWeifangChina
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Ali R, Sultan A, Ishrat R, Haque S, Khan NJ, Prieto MA. Identification of New Key Genes and Their Association with Breast Cancer Occurrence and Poor Survival Using In Silico and In Vitro Methods. Biomedicines 2023; 11:biomedicines11051271. [PMID: 37238942 DOI: 10.3390/biomedicines11051271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/20/2023] [Accepted: 03/07/2023] [Indexed: 05/28/2023] Open
Abstract
Breast cancer is one of the most prevalent types of cancer diagnosed globally and continues to have a significant impact on the global number of cancer deaths. Despite all efforts of epidemiological and experimental research, therapeutic concepts in cancer are still unsatisfactory. Gene expression datasets are widely used to discover the new biomarkers and molecular therapeutic targets in diseases. In the present study, we analyzed four datasets using R packages with accession number GSE29044, GSE42568, GSE89116, and GSE109169 retrieved from NCBI-GEO and differential expressed genes (DEGs) were identified. Protein-protein interaction (PPI) network was constructed to screen the key genes. Subsequently, the GO function and KEGG pathways were analyzed to determine the biological function of key genes. Expression profile of key genes was validated in MCF-7 and MDA-MB-231 human breast cancer cell lines using qRT-PCR. Overall expression level and stage wise expression pattern of key genes was determined by GEPIA. The bc-GenExMiner was used to compare expression level of genes among groups of patients with respect to age factor. OncoLnc was used to analyze the effect of expression levels of LAMA2, TIMP4, and TMTC1 on the survival of breast cancer patients. We identified nine key genes, of which COL11A1, MMP11, and COL10A1 were found up-regulated and PCOLCE2, LAMA2, TMTC1, ADAMTS5, TIMP4, and RSPO3 were found down-regulated. Similar expression pattern of seven among nine genes (except ADAMTS5 and RSPO3) was observed in MCF-7 and MDA-MB-231 cells. Further, we found that LAMA2, TMTC1, and TIMP4 were significantly expressed among different age groups of patients. LAMA2 and TIMP4 were found significantly associated and TMTC1 was found less correlated with breast cancer occurrence. We found that the expression level of LAMA2, TIMP4, and TMTC1 was abnormal in all TCGA tumors and significantly associated with poor survival.
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Affiliation(s)
- Rafat Ali
- Department of Biosciences, Jamia Millia Islamia (A Central University), New Delhi 110025, India
| | - Armiya Sultan
- Department of Biosciences, Jamia Millia Islamia (A Central University), New Delhi 110025, India
| | - Romana Ishrat
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia (A Central University), New Delhi 110025, India
| | - Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan 45142, Saudi Arabia
- Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut P.O. Box 36, Lebanon
- Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman P.O. Box 346, United Arab Emirates
| | - Nida Jamil Khan
- Department of Biosciences, Jamia Millia Islamia (A Central University), New Delhi 110025, India
| | - Miguel Angel Prieto
- Nutrition and Bromatology Group, Department of Analytical Chemistry and Food Science, Faculty of Science, Universidade de Vigo, E32004 Ourense, Spain
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Singh P, Gurung R, Sultan A, Dohare R. Understanding the role of adipokines and adipogenesis family in hepatocellular carcinoma. EGYPTIAN JOURNAL OF MEDICAL HUMAN GENETICS 2023. [DOI: 10.1186/s43042-023-00401-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Abstract
Background
Hepatocellular carcinoma (HCC) is the most common primary liver cancer. It has the sixth most incident cases with poor prognosis. Adipokines are known to have been linked with oncogenesis and progression of HCC.
Methods
We extracted TCGA-HCC data and identified differentially expressed genes (DEGs) using R. Genes of adipokines and adipogenesis family were scrutinized from DEGs and expression of genes in normal versus tumor patients was studied. Prognostic and stage plot analyses were performed, and key genes were selected. Pathway and gene ontology (GO) enrichment analysis was conducted. Expression analysis based on nodal metastasis, tumor protein p53 (TP53) mutation and tumor grade, and mutation analysis was performed using UALCAN and cBioPortal. Tumor infiltration analysis was performed to study the correlation of gene expression with tumor-infiltrating immune cells.
Results
We found four genes apelin (APLN), aldehyde dehydrogenase, mitochondrial (ALDH2), E2F transcription factor 1 (E2F1) and phosphoenolpyruvate carboxykinase, cytosolic (PCK1) highly associated with HCC. APLN and E2F1 were upregulated and ALDH2 and PCK1 were downregulated in HCC patients. High expression of APLN and E2F1 and low expression of ALDH2 and PCK1 resulted in poor prognosis of HCC patients. In expression analysis, ALDH2 showed significant change in all three categories. PCK1 showed highest mutation of out all $$4$$
4
genes in HCC patients. T cell CD8+ is found to be positively correlated with APLN, ALDH2 and E2F1 and macrophages showed a positive correlation with APLN and E2F1.
Conclusions
ALDH2 and PCK1 are great prognostic biomarkers and play a vital role in the development of HCC. Overexpression of ALDH2 and PCK1 can be a potential treatment strategy for HCC.
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Comparative genomics and interactomics of polyadenylation factors for the prediction of new parasite targets: Entamoeba histolytica as a working model. Biosci Rep 2023; 43:232462. [PMID: 36651565 PMCID: PMC9912109 DOI: 10.1042/bsr20221911] [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: 09/16/2022] [Revised: 01/05/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Protein-protein interactions (PPI) play a key role in predicting the function of a target protein and drug ability to affect an entire biological system. Prediction of PPI networks greatly contributes to determine a target protein and signal pathways related to its function. Polyadenylation of mRNA 3'-end is essential for gene expression regulation and several polyadenylation factors have been shown as valuable targets for controlling protozoan parasites that affect human health. Here, by using a computational strategy based on sequence-based prediction approaches, phylogenetic analyses, and computational prediction of PPI networks, we compared interactomes of polyadenylation factors in relevant protozoan parasites and the human host, to identify key proteins and define potential targets for pathogen control. Then, we used Entamoeba histolytica as a working model to validate our computational results. RT-qPCR assays confirmed the coordinated modulation of connected proteins in the PPI network and evidenced that silencing of the bottleneck protein EhCFIm25 affects the expression of interacting proteins. In addition, molecular dynamics simulations and docking approaches allowed to characterize the relationships between EhCFIm25 and Ehnopp34, two connected bottleneck proteins. Interestingly, the experimental identification of EhCFIm25 interactome confirmed the close relationships among proteins involved in gene expression regulation and evidenced new links with moonlight proteins in E. histolytica, suggesting a connection between RNA biology and metabolism as described in other organisms. Altogether, our results strengthened the relevance of comparative genomics and interactomics of polyadenylation factors for the prediction of new targets for the control of these human pathogens.
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Siddiqui MF, Alam A, Kalmatov R, Mouna A, Villela R, Mitalipova A, Mrad YN, Rahat SAA, Magarde BK, Muhammad W, Sherbaevna SR, Tashmatova N, Islamovna UG, Abuassi MA, Parween Z. Leveraging Healthcare System with Nature-Inspired Computing Techniques: An Overview and Future Perspective. NATURE-INSPIRED INTELLIGENT COMPUTING TECHNIQUES IN BIOINFORMATICS 2023:19-42. [DOI: 10.1007/978-981-19-6379-7_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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Alam A, Yildirim O, Siddiqui F, Imam N, Bay S. Network Medicine: Methods and Applications. BIOLOGICAL NETWORKS IN HUMAN HEALTH AND DISEASE 2023:75-90. [DOI: 10.1007/978-981-99-4242-8_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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Hasankhani A, Bahrami A, Mackie S, Maghsoodi S, Alawamleh HSK, Sheybani N, Safarpoor Dehkordi F, Rajabi F, Javanmard G, Khadem H, Barkema HW, De Donato M. In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection. Front Microbiol 2022; 13:1041314. [PMID: 36532492 PMCID: PMC9748370 DOI: 10.3389/fmicb.2022.1041314] [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: 09/10/2022] [Accepted: 11/04/2022] [Indexed: 08/26/2023] Open
Abstract
OBJECTIVE Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection. METHODS RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes). RESULTS As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response. CONCLUSION The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.
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Affiliation(s)
- Aliakbar Hasankhani
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Abolfazl Bahrami
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
- Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University, Munich, Germany
| | - Shayan Mackie
- Faculty of Science, Earth Sciences Building, University of British Columbia, Vancouver, BC, Canada
| | - Sairan Maghsoodi
- Faculty of Paramedical Sciences, Kurdistan University of Medical Sciences, Kurdistan, Iran
| | - Heba Saed Kariem Alawamleh
- Department of Basic Scientific Sciences, AL-Balqa Applied University, AL-Huson University College, AL-Huson, Jordan
| | - Negin Sheybani
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran
| | - Farhad Safarpoor Dehkordi
- Halal Research Center of IRI, FDA, Tehran, Iran
- Department of Food Hygiene and Quality Control, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Fatemeh Rajabi
- Department of Agronomy and Plant Breeding, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Ghazaleh Javanmard
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Hosein Khadem
- Department of Agronomy and Plant Breeding, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Herman W. Barkema
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Marcos De Donato
- Regional Department of Bioengineering, Tecnológico de Monterrey, Monterrey, Mexico
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