Volume 34 Issue 11
Nov.  2021
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CAI Ying, LIU Yan Ping, ZHENG Kai, LEI Yuan Di, WANG Ye, XU Xin Yun, ZHANG Zhao Hui. Proteomics Study on the Differentially Expressed Proteins in 16HBE Cells Exposed to Beryllium Sulfate[J]. Biomedical and Environmental Sciences, 2021, 34(11): 926-930. doi: 10.3967/bes2021.128
Citation: CAI Ying, LIU Yan Ping, ZHENG Kai, LEI Yuan Di, WANG Ye, XU Xin Yun, ZHANG Zhao Hui. Proteomics Study on the Differentially Expressed Proteins in 16HBE Cells Exposed to Beryllium Sulfate[J]. Biomedical and Environmental Sciences, 2021, 34(11): 926-930. doi: 10.3967/bes2021.128

Proteomics Study on the Differentially Expressed Proteins in 16HBE Cells Exposed to Beryllium Sulfate

doi: 10.3967/bes2021.128
Funds:  This study was supported by the National Natural Science Foundation of China [81573193] and the Natural Science Foundation of Hunan Province [2020JJ4082]
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  • Author Bio:

    CAI Ying, female, born in 1995, MPH, majoring in environmental toxicology

    LIU Yan Ping, female, born in 1995, majoring in environmental toxicology

  • Corresponding author: ZHANG Zhao Hui, Tel: 86-734-8281375, E-mail: nhzzh@usc.edu.cn; Prof. XU Xin Yun, Tel: 86-755-25609527, E-mail: xyxu2008@163.com
  • &These authors contributed equally to this work.
  • Received Date: 2021-01-25
  • Accepted Date: 2021-03-17
  • &These authors contributed equally to this work.
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  • [1] Drobyshev E, Kybarskaya L, Dagaev S, et al. New insight in beryllium toxicity excluding exposure to beryllium-containing dust: accumulation patterns, target organs, and elimination. Arch Toxicol, 2019; 93, 859−69. doi:  10.1007/s00204-019-02432-7
    [2] Verma DK, Ritchie AC, Shaw ML. Measurement of beryllium in lung tissue of a chronic beryllium disease case and cases with sarcoidosis. Occup Med, 2003; 53, 223−7. doi:  10.1093/occmed/kqg042
    [3] Taylor TP, Ding M, Ehler DS, et al. Beryllium in the environment: a review. J Environ Sci Health Part A, 2003; 38, 439−69. doi:  10.1081/ESE-120016906
    [4] Devoy J, Remy AM, La Rocca B, et al. Occupational exposure to beryllium in French industries. J Occup Environ Hyg, 2019; 16, 229−41. doi:  10.1080/15459624.2018.1559926
    [5] Liu ZH, Wang K, Yan Q, et al. Beryllium inhibits apoptosis via mitochondria in beryllium-induced lung disease in the rat. Exp Lung Res, 2019; 45, 92−100. doi:  10.1080/01902148.2019.1621409
    [6] Wang Y, Fan KT, Li JM, et al. The regulation and activity of interleukin-12. Front Biosci, 2012; 4, 888−99.
    [7] Ďuračková Z. Some current insights into oxidative stress. Physiol Res, 2010; 59, 459−69.
    [8] Kirkham PA, Barnes PJ. Oxidative stress in COPD. Chest, 2013; 144, 266−73. doi:  10.1378/chest.12-2664
    [9] Sinha N, Dabla PK. Oxidative stress and antioxidants in hypertension-a current review. Curr Hypertens Rev, 2015; 11, 132−42. doi:  10.2174/1573402111666150529130922
    [10] Sawyer RT, Dobis DR, Goldstein M, et al. Beryllium-stimulated reactive oxygen species and macrophage apoptosis. Free Radic Biol Med, 2005; 38, 928−37. doi:  10.1016/j.freeradbiomed.2004.12.014
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Proteomics Study on the Differentially Expressed Proteins in 16HBE Cells Exposed to Beryllium Sulfate

doi: 10.3967/bes2021.128
Funds:  This study was supported by the National Natural Science Foundation of China [81573193] and the Natural Science Foundation of Hunan Province [2020JJ4082]
  • Author Bio:

  • Corresponding author: ZHANG Zhao Hui, Tel: 86-734-8281375, E-mail: nhzzh@usc.edu.cn Prof. XU Xin Yun, Tel: 86-755-25609527, E-mail: xyxu2008@163.com
  • &These authors contributed equally to this work.
&These authors contributed equally to this work.
CAI Ying, LIU Yan Ping, ZHENG Kai, LEI Yuan Di, WANG Ye, XU Xin Yun, ZHANG Zhao Hui. Proteomics Study on the Differentially Expressed Proteins in 16HBE Cells Exposed to Beryllium Sulfate[J]. Biomedical and Environmental Sciences, 2021, 34(11): 926-930. doi: 10.3967/bes2021.128
Citation: CAI Ying, LIU Yan Ping, ZHENG Kai, LEI Yuan Di, WANG Ye, XU Xin Yun, ZHANG Zhao Hui. Proteomics Study on the Differentially Expressed Proteins in 16HBE Cells Exposed to Beryllium Sulfate[J]. Biomedical and Environmental Sciences, 2021, 34(11): 926-930. doi: 10.3967/bes2021.128
  • Beryllium (Be) is a non-radioactive element with carcinogenic properties, and presents serious occupational and environmental hazards from its resulting toxicants, which could significantly impact the health of the occupational population[1-3]. Beryllium and its compounds can cause diseases such as acute chemical pneumonia, lung cancer, and chronic beryllium disease, which predominantly occurs alongside either lung granuloma or pulmonary fibrosis[4,5]. Furthermore, the mechanism by which beryllium is toxic has not been fully elucidated; therefore, studies on its health hazards as well of its compounds are important. An in-depth study of the mechanism of beryllium toxicity is especially essential to prevent and control its associated health hazards. Tandem mass tag (TMT) technology is a reliable technology in quantitative proteomics, which can be implemented to perform relative quantification and identification analysis of proteins, peptides, nucleic acids, and other biological macromolecules.

    In this study, TMT-labeled quantitative proteomics technology and bioinformatics analyses were used to evaluate the differential protein expression profiles in 16HBE cells treated with BeSO4. In addition, we screened selected key proteins that have important reference values for potential follow-up research, thereby providing a new direction for exploring the mechanism of BeSO4-treated 16HBE cytotoxicity and screening for potential biomarkers.

    The human bronchial epithelial (16HBE) cells were cultured in DMEM containing 10% FBS at 37 °C in a 5% CO2 incubator and treated with BeSO4 at 150 μmol/mL for 48 h. Protein quantification was performed according to the instructions of the BCA Protein Quantification Kit. One hundred and fifty μg of total protein was used for each sample in an ultrafiltration tube to which 10 mmol/L DTT (DL-dithiothreitol) was added to 500 μL at 4 °C, then the protein sample was centrifuged at 10,464 ×g for 15 min, and incubated at room temperature for 1 h. Subsequently, 400 μL of 20 mmol/L IAA (Indole-3-acetic acid) was added at 4 °C, centrifuged at 14,243 ×g for 15 min, after which the filtrate was discarded in the collection tube. Later 400 μL of 100 mmol/L triethylamine borane (TEAB) was added and the mixture was centrifuged at 14,243 ×g for 15 min at 4 °C, and the filtrate was discarded. TMT labeling was performed according to instructions by Thermo ScientificTM TMTTM, and the TMT-labeled samples were combined and transferred into a 1.5 mL tube. The evaporated samples were dissolved in 0.1% formic acid (FA). The samples were divided into groups using Ultimate 3000 and APC-3000 instruments (Thermo, USA). Each component was evaporated to dryness using a PVC 2–18 centrifugal concentrator, and the components of the spin-dried samples were dissolved in 20 μL of 0.1% formic acid (FA) (mixed by vortexing), and tested on a Q Exactive mass spectrometer (Thermo, USA). The original data obtained by searching the UniProt human database were exported to Microsoft Excel software and then normalized using Persue software for subsequent analyses.

    Based on the R language “clusterProfiler” and “org.Hs.eg.db” software packages, Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DEPs were carried out, whose biological effect is analyzed from three aspects: biological function, cell composition and molecular function. Simultaneously, the related signaling pathways involved in the differential proteins of 16HBE cells treated with BeSO4 were analyzed. The protein-protein interaction network (PPI) is a visual network diagram of the interaction between proteins, highlighting the key proteins from the interconnected nodes and the number. After importing DEPs into the STRINIG online database (https://string-db.org/), they were combined with Cytoscape software to visually analyze the TSV files from STRINIG. The MCODE plug-in in Cytoscape, based on the calculation method of node information in the network graph. This was done to further determine the more closely connected protein modules. R language was used to perform GO enrichment analysis on the protein modules selected in PPI and to screen key proteins.

    A total of 4,054 proteins were obtained, and the subsequent analysis illustrated that a total of 883 proteins were (significantly) differentially expressed between the groups (P < 0.05). The volcano and heat maps more intuitively demonstrate the distribution of differential protein expression between the experimental and control groups. Criteria of FC ≥ 1.2, FC ≤ 0.83, and P < 0.05, were established to identify the initial 15 proteins exhibiting significant differential expression (Table 1).

    Protein accession numberProtein nameGeneFCUp/DownP value
    P68431Histone H3.1H3C11.33up0.05
    P04259Keratin, type II cytoskeletal 6BKRT6B1.29up0.01
    Q9BVS4Serine/threonine-protein kinase RIO2RIOK21.21up< 0.01
    Q8WXF1Paraspeckle component 1PSPC11.20up< 0.01
    P08263Glutathione S-transferase A1GSTA10.71down0.02
    O60499Syntaxin-10STX100.71down0.05
    Q9P1F3Costars family protein ABRACLABRACL0.72down0.03
    Q96FK6WD repeat-containing protein 89WDR890.73down0.05
    P02795Metallothionein-2MT2A0.74down< 0.01
    P17066Heat shock 70 kD protein 6HSPA60.77down0.02
    Q9Y4G6Talin-2TLN20.78down0.03
    P54868Hydroxymethylglutaryl-CoA synthase, mitochondrialHMGCS20.80down< 0.01
    Q92604Acyl-CoA:lysophosphatidylglycerol acyltransferase 1LPGAT10.81down< 0.01
    Q15651High mobility group nucleosome-binding domain-containing protein 3HMGN30.81down0.02
    P07477Trypsin-1PRSS10.82down0.01

    Table 1.  Top 15 DEPs screened in the 16HBE cells treated with beryllium sulfate

    GO analysis showed that in terms of biological processes, the identified DEPs were mainly involved in ribonucleoprotein complex biogenesis, ribosome biogenesis, and RNA catabolism and processing. Cell component analysis showed that differentially expressed proteins are involved in cell-substrate junctions, focal adhesions, cell-substrate adherens junctions, etc. According to molecular function, the most differentially expressed proteins are related to cadherin binding, cell adhesion molecule binding, and structural constituents of ribosomes (Figure 1). KEGG analysis was used to explore the signaling pathways involved in the regulation of differentially expressed proteins. It was found that these differential proteins are mainly involved in signaling pathways involving the spliceosome, Huntington’s disease, and ribosomes (Figure 2).

    Figure 1.  GO enrichment analysis of DEPs

    Figure 2.  KEGG enrichment analysis of DEPs.

    Cytoscape software was used to visually display the protein interaction- relationship data obtained from the STRING online database. The network graph composed of upregulated proteins consists of 421 nodes and 4,566 edges, whereas the downregulated protein network consists of 284 nodes and 1,756 edges. Thereafter, MCODE was used to filter out the key protein modules associated with the upregulation and downregulation of network graphs. The key protein modules are more intuitive. The key protein modules in the up- and down-regulated protein network graphs were 74 nodes, 1,574 connections, and 20 nodes and 47 connections, respectively.

    Further GO analysis of the selected key protein modules revealed that the upregulated modules were mainly related to biological processes such as rRNA processing, metabolism, and protein localization. These differential proteins are mainly distributed in the cytoplasm and ribosomes and are involved in molecular functions, such as rRNA binding and translation factor activity. The biological processes involved in the downregulation of key protein modules are mainly related to oxidative stress and interleukin-12. Cell component analysis highlights that the proteins in this module are mainly distributed intercellularly, in the melanosome, or in the myelin. However, they are also related to molecular functions, such as oxidoreductase activity. The identified proteins from the upregulated and downregulated key protein modules were further screened to reveal that RIOK2, CSNK1D, and HEATR1 are upregulated, all of which are involved in rRNA processing and metabolism. Nine were downregulated, including PRDX5, P4HB, GLRX3, PDIA3, PRDX2, TXN, SOD2, CTNNB1, and HMGB1, all of which are related to oxidative stress. Additionally, HMGB1, P4HB, and TXN are involved in the interleukin-12-mediated signaling pathways. After importing these 12 key proteins into the STRING database, the data illustrate an interaction relationship between them. This suggests that these 12 key proteins form a regulatory network, and may play a regulatory role in the biological processes of oxidative stress and those of interleukin-12 in 16HBE cells treated with BeSO4.

    In the present study, proteomics and bioinformatics were used to investigate DEPs before and after BeSO4 treatment in 16HBE cells, and to further explore any relevant signaling pathways. A total of 883 DEPs were identified, of which 520 were upregulated and 363 were downregulated. Additionally, GO and KEGG enrichment analyses were performed on the DEPs to explore the biological processes and signaling pathways involved. Visualization software was used to display the PPI interaction relationship of DEPs and to further identify key proteins. These analyses provide valuable mechanistic information in the study of BeSO4- treated 16HBE cell damage.

    To predict the function of the key protein modules in the PPI network, we screened three key proteins from the upregulated modules: RIOK2, CSNK1D, and HEATR1, all of which are related to rRNA processing and metabolism. Additionally, nine key downregulated proteins related to oxidative stress: PRDX5, P4HB, GLRX3, PDIA3, PRDX2, TXN, SOD2, CTNNB1, and HMGB1 were screened. Among these, HMGB1, P4HB, and TXN are also involved in biological processes related to interleukin-12. Interleukin-12 is a multifunctional cytokine that is linked to effective anti-tumor and anti-infection immunomodulatory activities both, in vivo and in vitro[6]. Oxidative stress refers to the imbalance between the production of free radicals and reactive metabolites, and the elimination of antioxidants. This imbalance can promote damage to important biological molecules and cells, potentially affecting the entire organism[7]. In addition, oxidative stress is related to the pathogenesis of many diseases, such as chronic lung disease, chronic disease including chronic obstructive pulmonary disease, hypertension, and Alzheimer's disease[8, 9]. Studies have shown that BeSO4 stimulates the formation of reactive oxygen species (ROS) in mouse macrophages and plays an important role in apoptosis[10].

    In summary, the results of this study identified three key potential differential proteins related to rRNA processing and metabolism, and nine were involved in oxidative stress signaling pathways. The DEPs demonstrate specific regulatory effects, and could thus be the basis of novel research directions and ideas for subsequent follow-up studies pertaining to the molecular mechanism of BeSO4-induced 16HBE cell damage. Our findings provide valuable references for future research on potential biomarkers and metabolic pathways of BeSO4 toxicity mechanisms.

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