Genetic effects of welding fumes on the development of respiratory system diseases

Rana, Humayan Kabir; Akhtar, Mst. Rashida; Islam, M. Babul; Lió, Píetro; Quinn, Julian M.W.; Huq, Fazlul; Moni, Mohammad Ali

doi:10.1016/j.compbiomed.2019.04.004

Cited by 21 publications

(9 citation statements)

References 43 publications

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“…TNF is also known to be a major regulator of the production of proinflammatory cytokines and has been considered as a therapeutic target for the treatment of some inflammatory diseases such as rheumatoid arthritis and inflammatory disease ( Liu, 2005 ; Parameswaran and Patial, 2010 ). The TNF signaling pathway plays an important role in the control of inflammation, immunity, and cell survival ( Rana et al, 2019 ). It has been shown that one of the strategies for bacterial survival and immunosuppression by Mycoplasma bovis is to inhibit TNF-α and interferon-gamma production ( Mulongo et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach

et al. 2021

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Background: Bovine respiratory disease (BRD) is the most common disease in the beef and dairy cattle industry. BRD is a multifactorial disease resulting from the interaction between environmental stressors and infectious agents. However, the molecular mechanisms underlying BRD are not fully understood yet. Therefore, this study aimed to use a systems biology approach to systematically evaluate this disorder to better understand the molecular mechanisms responsible for BRD.Methods: Previously published RNA-seq data from whole blood of 18 healthy and 25 BRD samples were downloaded from the Gene Expression Omnibus (GEO) and then analyzed. Next, two distinct methods of weighted gene coexpression network analysis (WGCNA), i.e., module–trait relationships (MTRs) and module preservation (MP) analysis were used to identify significant highly correlated modules with clinical traits of BRD and non-preserved modules between healthy and BRD samples, respectively. After identifying respective modules by the two mentioned methods of WGCNA, functional enrichment analysis was performed to extract the modules that are biologically related to BRD. Gene coexpression networks based on the hub genes from the candidate modules were then integrated with protein–protein interaction (PPI) networks to identify hub–hub genes and potential transcription factors (TFs).Results: Four significant highly correlated modules with clinical traits of BRD as well as 29 non-preserved modules were identified by MTRs and MP methods, respectively. Among them, two significant highly correlated modules (identified by MTRs) and six nonpreserved modules (identified by MP) were biologically associated with immune response, pulmonary inflammation, and pathogenesis of BRD. After aggregation of gene coexpression networks based on the hub genes with PPI networks, a total of 307 hub–hub genes were identified in the eight candidate modules. Interestingly, most of these hub–hub genes were reported to play an important role in the immune response and BRD pathogenesis. Among the eight candidate modules, the turquoise (identified by MTRs) and purple (identified by MP) modules were highly biologically enriched in BRD. Moreover, STAT1, STAT2, STAT3, IRF7, and IRF9 TFs were suggested to play an important role in the immune system during BRD by regulating the coexpressed genes of these modules. Additionally, a gene set containing several hub–hub genes was identified in the eight candidate modules, such as TLR2, TLR4, IL10, SOCS3, GZMB, ANXA1, ANXA5, PTEN, SGK1, IFI6, ISG15, MX1, MX2, OAS2, IFIH1, DDX58, DHX58, RSAD2, IFI44, IFI44L, EIF2AK2, ISG20, IFIT5, IFITM3, OAS1Y, HERC5, and PRF1, which are potentially critical during infection with agents of bovine respiratory disease complex (BRDC).Conclusion: This study not only helps us to better understand the molecular mechanisms responsible for BRD but also suggested eight candidate modules along with several promising hub–hub genes as diagnosis biomarkers and therapeutic targets for BRD.

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Section: Discussionmentioning

confidence: 99%

Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach

et al. 2021

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“…Tumor necrosis factor (TNF) is of the cytokine family that coordinates the mammalian immune response and secrete by macrophages in response to endotoxins. TNF signaling pathway is a classical immune system pathway, which has a central role in the control of inflammation, immunity and cell survival (Galvão et al, 2012;Rana et al, 2019). Hence, it is suggested that a potential mechanism to block the development of inflammation, by the effective medicines that used for treating mastitis, is inhibiting TNF signaling pathway through reducing the secretion of TNF (Wu et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Identification of Gene Modules and Hub Genes Involved in Mastitis Development Using a Systems Biology Approach

et al. 2020

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Objective: Mastitis is defined as the inflammation of the mammary gland, which impact directly on the production performance and welfare of dairy cattle. Since, mastitis is a multifactorial complex disease and the molecular pathways underlying this disorder have not been clearly understood yet, a system biology approach was used in this study to a better understanding of the molecular mechanisms behind mastitis. Methods: Publicly available RNA-Seq data containing samples from milk of five infected and five healthy Holstein cows at five time points were retrieved. Gene Co-expression network analysis (WGCNA) approach and functional enrichment analysis were then applied with the aim to find the non-preserved module of genes that their connectivity were altered under infected condition. Hub genes were identified in the non-preserved modules and were subjected to protein-protein interactions (PPI) network construction. Results: Among the 25 modules identified, eight modules were non-preserved and were also biologically associated with inflammation, immune response and mastitis development. Interestingly most of the hub genes in the eight modules were also densely connected in the PPI network. Of the hub genes, 250 genes were hubs in both coexpression and PPI networks and most of them were reported to play important roles in immune response or inflammatory pathways. The blue module was highly enriched in inflammatory responses and STAT1 was suggested to play an important role in mastitis development by regulating the immune related genes in this module. Moreover, a set of highly connected genes were identified such as BIRC3,

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“…Welding fumes are caused by melting and evaporation of metal wire electrodes or dust during joining or coating of metals. A variety of metallic and non-metallic elements and compounds are present in the fume composition (Sowards et al, 2010), including, metallic oxides, silicates and fluorides, as well as complex mixtures of heavy metal contaminants such as cadmium, aluminium, chromium, copper and lead (Rana et al, 2019). Some of these particles vent into the atmosphere and some of them hang in the air for a while and then accumulate on the ground as a result of condensation, air movement, gravity or atomic interactions.…”

Section: Introductionmentioning

confidence: 99%

Análisis de la microestructura de humos de soldadura de aceros de bajo y medio contenido de carbon

Güney

2021

revmetal

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En este estudio, la muestra de humo de soldadura se obtuvo a partir de aceros de bajo y medio carbono y los electrodos utilizados en la soldadura. Las microestructuras de las partículas se analizaron mediante microscopía electrónica de barrido (SEM), espectrómetro de dispersión de energía (EDS), difractómetro de rayos X (XRD) y espectrómetro de infrarrojos por transformada de Fourier (FTIR). En los experimentos se encontró que los elementos Be, O, F, Fe, Si, Cl, K, Ca, Ti, V, Cr, Mn tenían contenidos atómicos superiores al 1%. Con base en este hallazgo, se revela que la estructura está constituida principalmente por óxidos tipos Fe2O3, Fe3O4, MnO2, TiO2, SiO2, Fe3Mn3O8, FeMn2O4, BeO, CrO. También se encontró mediante análisis XRD que los elementos con contenidos inferiores al 1% atómico se encontraban también asociados a fases en forma de óxidos. Debido a que las estructuras oxidadas amenazan el medio ambiente y la salud humana, se ha descubierto experimentalmente que los metales emitidos por los humos de soldadura siguen contaminando y amenazando el medio ambiente.

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Genetic effects of welding fumes on the development of respiratory system diseases

Cited by 21 publications

References 43 publications

Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach

Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach

Identification of Gene Modules and Hub Genes Involved in Mastitis Development Using a Systems Biology Approach

Análisis de la microestructura de humos de soldadura de aceros de bajo y medio contenido de carbon

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