Humayan Kabir Rana1, Mst Rashida Akhtar2, Mohammad Boshir Ahmed3, Pietro Liò4, Julian M W Quinn5, Fazlul Huq6, Mohammad Ali Moni7. 1. Department of Computer Science and Engineering, Green University of Bangladesh, Bangladesh. 2. Department of Computer Science and Engineering, Varendra University, Rajshahi, Bangladesh. 3. School of Civil and Environmental Engineering, University of Technology, Sydney, NSW 2007, Australia. 4. Computer Laboratory, The University of Cambridge, 15 JJ Thomson Avenue, Cambridge, UK. 5. Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia. 6. School of Biomedical Science, Faculty of Medicine and Health, The University of Sydney, Australia. 7. Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia; School of Biomedical Science, Faculty of Medicine and Health, The University of Sydney, Australia. Electronic address: mohammad.moni@sydney.edu.au.
Abstract
BACKGROUND: Welding involves exposure to fumes, gases and radiant energy that can be hazardous to human health. Welding fumes (WFs) comprise a complex mixture of metallic oxides, silicates and fluorides that may result in different health effects. Inhalation of WFs in large quantities over a long periods may pose a risk of developing neurodegenerative diseases (NDGDs), but the nature of this risk is poorly understood. To address this we performed transcriptomic analysis to identify links between WF exposure and NDGDs. METHODS: We developed quantitative frameworks to identify the gene expression relationships of WF exposure and NDGDs. We analyzed gene expression microarray data from fume-exposed tissues and NDGDs including Parkinson's disease (PD), Alzheimer's disease (AD), Lou Gehrig's disease (LGD), Epilepsy disease (ED) and multiple sclerosis disease (MSD) datasets. We constructed disease-gene relationship networks and identified dysregulated pathways, ontological pathways and protein-protein interaction sub-network using multilayer network topology and neighborhood-based benchmarking. RESULTS: We observed that WF associated genes share 18, 16, 13, 19 and 19 differentially expressed genes with PD, AD, LGD, ED and MSD respectively. Gene expression dysregulation along with relationship networks, pathways and ontologic analysis indicate that WFs may be linked to the progression of these NDGDs. CONCLUSIONS: Our developed network-based approach to analysis and investigate the genetic effects of welding fumes on PD, AD, LGD, ED and MSD neurodegenerative diseases could be helpful to understand the causal influences of WF exposure for the progression of the NDGDs.
BACKGROUND: Welding involves exposure to fumes, gases and radiant energy that can be hazardous to human health. Welding fumes (WFs) comprise a complex mixture of metallic oxides, silicates and fluorides that may result in different health effects. Inhalation of WFs in large quantities over a long periods may pose a risk of developing neurodegenerative diseases (NDGDs), but the nature of this risk is poorly understood. To address this we performed transcriptomic analysis to identify links between WF exposure and NDGDs. METHODS: We developed quantitative frameworks to identify the gene expression relationships of WF exposure and NDGDs. We analyzed gene expression microarray data from fume-exposed tissues and NDGDs including Parkinson's disease (PD), Alzheimer's disease (AD), Lou Gehrig's disease (LGD), Epilepsy disease (ED) and multiple sclerosis disease (MSD) datasets. We constructed disease-gene relationship networks and identified dysregulated pathways, ontological pathways and protein-protein interaction sub-network using multilayer network topology and neighborhood-based benchmarking. RESULTS: We observed that WF associated genes share 18, 16, 13, 19 and 19 differentially expressed genes with PD, AD, LGD, ED and MSD respectively. Gene expression dysregulation along with relationship networks, pathways and ontologic analysis indicate that WFs may be linked to the progression of these NDGDs. CONCLUSIONS: Our developed network-based approach to analysis and investigate the genetic effects of welding fumes on PD, AD, LGD, ED and MSD neurodegenerative diseases could be helpful to understand the causal influences of WF exposure for the progression of the NDGDs.
Authors: Humayan Kabir Rana; Mst Rashida Akhtar; M Babul Islam; Mohammad Boshir Ahmed; Pietro Lió; Fazlul Huq; Julian M W Quinn; Mohammad Ali Moni Journal: Sci Rep Date: 2020-02-17 Impact factor: 4.379
Authors: Md Nasim Haidar; M Babul Islam; Utpala Nanda Chowdhury; Md Rezanur Rahman; Fazlul Huq; Julian M W Quinn; Mohammad Ali Moni Journal: IET Syst Biol Date: 2020-04 Impact factor: 1.615