Gene-expression profiling using suppression-subtractive hybridization and cDNA microarray in rat mononuclear cells in response to welding-fume exposure

Department of Molecular and Life Science, Hanyang University, Sŏul, Seoul, South Korea
Toxicology and Industrial Health (Impact Factor: 1.86). 07/2004; 20(1-5):77-88. DOI: 10.1191/0748233704th200oa
Source: PubMed


Welders with radiographic pneumoconiosis abnormalities have shown a gradual clearing of the X-ray identified effects following removal from exposure. In some cases, the pulmonary fibrosis associated with welding fumes appears in a more severe form in welders. Accordingly, for the early detection of welding-fume-exposure-induced pulmonary fibrosis, the gene expression profiles of peripheral mononuclear cells from rats exposed to welding fumes were studied using suppression-subtractive hybridization (SSH) and a cDNA microarray. As such, Sprague-Dawley rats were exposed to a stainless steel arc welding fume for 2 h/day in an inhalation chamber with a 1107.5 +/- 2.6 mg/m3 concentration of total suspended particulate (TSP) for 30 days. Thereafter, the total RNA was extracted from the peripheral blood mononuclear cells, the cDNA synthesized from the total RNA using the SMART PCR cDNA method, and SSH performed to select the welding-fume-exposure-regulated genes. The cDNAs identified by the SSH were then cloned into a plasmid miniprep, sequenced and the sequences analysed using the NCBI BLAST programme. In the SSH cloned cDNA microarray analysis, five genes were found to increase their expression by 1.9-fold or more, including Rgs 14, which plays an important function in cellular signal transduction pathways; meanwhile 36 genes remained the same and 30 genes decreased their expression by more than 59%, including genes associated with the immune response, transcription factors and tyrosine kinases. Among the 5200 genes analysed, 256 genes (5.1%) were found to increase their gene expression, while 742 genes (15%) decreased their gene expression in response to the welding-fume exposure when tested using a commercial 5.0k DNA microarray. Therefore, unlike exposure to other toxic substances, prolonged welding-fume exposure was found to substantially downregulate many genes.

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Available from: Kyung Taek Rim, May 07, 2015
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    • "Animal studies provide additional evidence of systemic immune responses. In a study by Rim et al. (2004), gene expression profiling of peripheral blood mononuclear cells collected from rats after 30 days of inhalation to a relatively high concentration of stainless steel welding fume indicated that ~3-times more genes, many of which are associated with immune function and response, were down-regulated as opposed to being increased. Moreover, microarray technology and subsequent pathway analysis indicated a strong immunologic transcriptional response to welding fume in a study comparing lung tumor susceptible and resistant mouse strains (Zeidler-Erdely et al., 2010). "
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    ABSTRACT: Welding fume inhalation affects the immune system of exposed workers. Manganese (Mn) in welding fume may induce immunosuppressive effects. The goal was to determine if Mn in welding fume alters immunity by reducing the number of circulating total leukocytes and specific leukocyte sub-populations. Sprague-Dawley rats were treated by intratracheal instillation (ITI) with either a single dose (2.00 mg/rat) or repeated doses (0.125 or 2.00 mg/rat for 7 weeks) with welding fumes that contained different levels of Mn. Additional rats were treated by ITI once a week for 7 weeks with the two doses of manganese chloride (MnCl₂). Bronchoalveolar lavage was performed to assess lung inflammation. Also, whole blood was recovered, and the number of circulating total leukocytes, as well as specific lymphocyte subsets, was determined by flow cytometry. The welding fume highest in Mn content significantly increased lung inflammation, injury, and production of inflammatory cytokines and chemokines compared to all other treatment groups. In addition, the same group expressed significant decreases in the number of circulating CD4⁺ and CD8⁺ T-lymphocytes after a single exposure, and significant reductions in the number of circulating total lymphocytes, primarily CD4⁺ and CD8⁺ T-lymphocytes, after repeated exposures (compared to control values). Repeated MnCl₂ exposure led to a trend of a reduction (but not statistically significant) in circulating total lymphocytes, attributable to the changes in the CD4⁺ T-lymphocyte population levels. The welding fume with the lower concentration of Mn had no significant effect on the numbers of blood lymphocytes and lymphocyte subsets compared to control values. Evidence from this study indicates that pulmonary exposure to certain welding fumes cause decrements in systemic immune cell populations, specifically circulating T-lymphocytes, and these alterations in immune cell number are not dependent exclusively on Mn, but likely a combination of other metals present in welding fume.
    Journal of Immunotoxicology 02/2012; 9(2):184-92. DOI:10.3109/1547691X.2011.650733 · 2.05 Impact Factor
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    • "Moreover, phenotype-anchored gene expression profiles suggest that various toxicological endpoints or diseases can be classified or predicted by gene expression patterns (Alizadeh et al. 2000; Bittner et al. 2000). Gene expression analysis has been used to investigate peripheral blood mononuclear cells in which pneumoconiosis symptoms were caused in a rat model after a 30-day exposure to welding fumes (Rim et al. 2004). In a previous study, we also investigated gene expression profiling in lung injury in Sprague–Dawley rats after welding fume exposure and recovery (Oh et al. 2007). "
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    ABSTRACT: Many in the welding industry suffer from bronchitis, lung function changes, metal fume fever, and diseases related to respiratory damage. These phenomena are associated with welding fumes; however, the mechanism behind these findings remains to be elucidated. In this study, the lungs of cynomolgus monkeys were exposed to MMA-SS welding fumes for 229 days and allowed to recover for 153 days. After the exposure and recovery period, gene expression profiles were investigated using the Affymetrix GeneChip® Human U133 plus 2.0. In total, it was confirmed that 1,116 genes were up-or down-regulated (over 2-fold changes, P < 0.01) for the T1 (31.4 ± 2.8 mg/m3) and T2 (62.5 ± 2.7 mg/m3) dose groups. Differentially expressed genes in the exposure and recovery groups were analyzed, based on hierarchical clustering, and were imported into Ingenuity Pathways Analysis to analyze the biological and toxicological functions. Functional analysis identified genes involved in immunological disease in both groups. Additionally, differentially expressed genes in common between monkeys and rats following welding fume exposure were compared using microarray data, and the gene expression of selected genes was verified by real-time PCR. Genes such as CHI3L1, RARRES1, and CTSB were up-regulated and genes such as CYP26B1, ID4, and NRGN were down-regulated in both monkeys and rats following welding fume exposure. This is the first comprehensive gene expression profiling conducted for welding fume exposure in monkeys, and these expressed genes are expected to be useful in helping to understand transcriptional changes in monkey lungs after welding fume exposure. Electronic supplementary material The online version of this article (doi:10.1007/s00204-009-0486-z) contains supplementary material, which is available to authorized users.
    Archives of Toxicology 11/2009; 84(3):191-203. DOI:10.1007/s00204-009-0486-z · 5.98 Impact Factor
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    • "Nevertheless, this library construction strategy served our downstream purpose of making cDNA microarrays with the isolated cDNA clones even though we cannot identify which cDNA or groups of cDNAs responded to which compound and at which exposure time point using the raw EST data. The combination of SSH-PCR and cDNA microarray analysis has been a widely used approach for identifying differentially expressed genes [9,10] and characterizing mechanisms of action of known and suspected toxicants [11,12], especially when there is no or little genomic information available for the test organism. Our microarray studies have generated data enabling us to further identify differentially expressed transcripts and to elucidate sublethal toxicological mechanisms in E. fetida exposed to TNT alone [13] or a mixture of TNT and RDX [14]. "
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    ABSTRACT: Eisenia fetida, commonly known as red wiggler or compost worm, belongs to the Lumbricidae family of the Annelida phylum. Little is known about its genome sequence although it has been extensively used as a test organism in terrestrial ecotoxicology. In order to understand its gene expression response to environmental contaminants, we cloned 4032 cDNAs or expressed sequence tags (ESTs) from two E. fetida libraries enriched with genes responsive to ten ordnance related compounds using suppressive subtractive hybridization-PCR. A total of 3144 good quality ESTs (GenBank dbEST accession number EH669363-EH672369 and EL515444-EL515580) were obtained from the raw clone sequences after cleaning. Clustering analysis yielded 2231 unique sequences including 448 contigs (from 1361 ESTs) and 1783 singletons. Comparative genomic analysis showed that 743 or 33% of the unique sequences shared high similarity with existing genes in the GenBank nr database. Provisional function annotation assigned 830 Gene Ontology terms to 517 unique sequences based on their homology with the annotated genomes of four model organisms Drosophila melanogaster, Mus musculus, Saccharomyces cerevisiae, and Caenorhabditis elegans. Seven percent of the unique sequences were further mapped to 99 Kyoto Encyclopedia of Genes and Genomes pathways based on their matching Enzyme Commission numbers. All the information is stored and retrievable at a highly performed, web-based and user-friendly relational database called EST model database or ESTMD version 2. The ESTMD containing the sequence and annotation information of 4032 E. fetida ESTs is publicly accessible at
    BMC Bioinformatics 02/2007; 8 Suppl 7(Suppl 7):S7. DOI:10.1186/1471-2105-8-S7-S7 · 2.58 Impact Factor
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