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Metabolomics

, 15:6 | Cite as

Tissue-specific differences in metabolites and transcripts contribute to the heterogeneity of ricinoleic acid accumulation in Ricinus communis L. (castor) seeds

  • Drew Sturtevant
  • Trevor B. Romsdahl
  • Xiao-Hong Yu
  • David J. Burks
  • Rajeev K. Azad
  • John Shanklin
  • Kent D. ChapmanEmail author
Original Article
Part of the following topical collections:
  1. Plant metabolomics and lipidomics

Abstract

Introduction

Castor (Ricinus communis L.) seeds are valued for their production of oils which can comprise up to 90% hydroxy-fatty acids (ricinoleic acid). Castor oil contains mono-, di- and tri- ricinoleic acid containing triacylglycerols (TAGs). Although the enzymatic synthesis of ricinoleic acid is well described, the differential compartmentalization of these TAG molecular species has remained undefined.

Objectives

To examine the distribution of hydroxy fatty acid accumulation within the endosperm and embryo tissues of castor seeds.

Methods

Matrix assisted laser desorption/ionization mass spectrometry imaging was used to map the distribution of triacylglycerols in tissue sections of castor seeds. In addition, the endosperm and embryo (cotyledons and embryonic axis) tissues were dissected and extracted for quantitative lipidomics analysis and Illumina-based RNA deep sequencing.

Results

This study revealed an unexpected heterogeneous tissue distribution of mono-, di- and tri- hydroxy-triacylglycerols in the embryo and endosperm tissues of castor seeds. Pathway analysis based on transcript abundance suggested that distinct embryo- and endosperm-specific mechanisms may exist for the shuttling of ricinoleic acid away from phosphatidylcholine (PC) and into hydroxy TAG production. The embryo-biased mechanism appears to favor removal of ricinoleic acid from PC through phophatidylcholine: diacylglycerol acyltransferase while the endosperm pathway appears to remove ricinoleic acid from the PC pool by preferences of phospholipase A (PLA2α) and/or phosphatidylcholine: diacylglycerol cholinephosphotransferase.

Conclusions

Collectively, a combination of lipidomics and transcriptomics analyses revealed previously undefined spatial aspects of hydroxy fatty acid metabolism in castor seeds. These studies underscore a need for tissue-specific studies as a means to better understand the regulation of triacylglycerol accumulation in oilseeds.

Keywords

MALDI RNA-Seq Castor Ricinoleic acid Lipidomics Hydroxy fatty acids 

Notes

Acknowledgements

This work was supported in part by the U.S Department of Energy, Office of Science, BES-Physical Biosciences program (DE-SC0016536 to KDC; DE-SC0012704 to JS), by the National Science Foundation (Grant DBI 1117680, X-H Y, JS), and by a UNT-dissertation summer stipend to Drew Sturtevant. The UNT Orbitrap XL mass spectrometer and cryostat facilities were supported in part by a grant from the Hoblitzelle Foundation. The transcriptomics data were collected and reads processed for analysis by the UNT- BioDiscovery Genomics Core Facility.

Author contributions

The MALDI-MSI imaging and transcriptome sequencing and analysis were completed by DS. TR completed the MALDI-MS and ESI-MS analysis of lipid extracts. Bioinformatics support was contributed by DB, supervised by RA. XY grew and collected plant material and performed the real-time, quantitative-PCR (RT-qPCR) analyses on developing seeds. KC and JS supervised experiments at UNT and Brookhaven National Laboratory, respectively. DS, TR, and KC drafted the manuscript. All authors contributed to editing of the manuscript.

Funding

This research was funded in part by research contracts from the United States Department of Energy, Office of Science, Basic Energy Sciences (DE-SC0016536 to KDC; DE-SC0012704 to JS), a grant award from the National Science Foundation (DBI 1117680, X-H Y, JS), and by a UNT-dissertation summer stipend to Drew Sturtevant.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest.

Research involving human and animal participants

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

11306_2018_1464_MOESM1_ESM.png (113 kb)
Supplementary material 1 ESI-MS spectrum of extracted castor oil and overlapping dehydrated m/z peaks. Mass spectrum of directly infused extracted castor oil from m/z 875 to 960 indicating peaks of protonated dehydrated hydroxy-TAG and their respective corresponding ammoniated parent ions. (PNG 112 KB)
11306_2018_1464_MOESM2_ESM.png (115 kb)
Supplementary material 2 Principal components analysis (PCA) of variant genes between castor endosperm and embryo tissues. PCA of endosperm (blue) and embryo (red) tissues at first (47.82% variance) and second (14.08% variance) principal components. (PNG 115 KB)
11306_2018_1464_MOESM3_ESM.png (528 kb)
Supplementary material 3 Absolute gene transcript levels from select genes of Kennedy and Lands pathways. Box and whisker plots of normalized counts for transcripts of genes in the Kennedy or Lands pathways significantly greater (A) or lower (B) in endosperm tissues (gray) relative to embryo tissues (white). (n = 6, *q < 0.05, **q < 0.01) (PNG 528 KB)
11306_2018_1464_MOESM4_ESM.png (727 kb)
Supplementary material 4 Absolute gene transcript levels from select genes of lipid droplet packaging proteins. Box and whisker plots of normalized counts for transcripts significantly greater (A) or lower (B, Seipin2) in endosperm tissues (gray) relative to embryo tissues (white). (n = 6, *q < 0.05, **q < 0.01) (PNG 727 KB)
11306_2018_1464_MOESM5_ESM.png (31 kb)
Supplementary material 5 Relative transcript levels of select genes in the endosperm and embryos of 30 DAF seeds determined by RT-qPCR. Expression levels of FAH, GPAT9 and PDAT1 genes in endosperm showed no significant difference from their expression in the embryo (p > 0.05), whereas PDAT2 showed a significant difference between the embryo and endosperm (p < 0.0001). RT-qPCR values are presented relative to the expression of each gene in the embryo. All data are means SD (n = 3 independent biological replicates), and p values were calculated using mean crossing point deviation analysis computed by the relative expression (REST) software algorithm (Pfaffl et al. 2002). (PNG 31 KB)
11306_2018_1464_MOESM6_ESM.docx (11 kb)
Supplementary material 6 (DOCX 11 KB)
11306_2018_1464_MOESM7_ESM.docx (11 kb)
Supplementary material 7 (DOCX 11 KB)

References

  1. Arroyo-Caro, J. M., Chileh, T., Kazachkov, M., Zou, J., Alonso, D. L., & Garcia-Maroto, F. (2013). The multigene family of lysophosphatidate acyltransferase (LPAT)-related enzymes in Ricinus communis: Cloning and molecular characterization of two LPAT genes that are expressed in castor seeds. Plant Science, 199–200, 29–40.  https://doi.org/10.1016/j.plantsci.2012.09.015.CrossRefPubMedGoogle Scholar
  2. Asadauskas, S., Perez, J. M., & Duda, J. L. (1997). Lubrication properties of castor oil potential basestock for biodegradable lubricants. Lubrication Engineering, 53(12), 35–41.Google Scholar
  3. Bates, P. D. (2016). Understanding the control of acyl flux through the lipid metabolic network of plant oil biosynthesis. Biochimica Et Biophysica Acta-Molecular and Cell Biology of Lipids, 1861(9), 1214–1225.  https://doi.org/10.1016/j.bbalip.2016.03.021.CrossRefGoogle Scholar
  4. Bates, P. D., & Browse, J. (2012). The significance of different diacylgycerol synthesis pathways on plant oil composition and bioengineering. Frontiers in Plant Science. 3, 147.  https://doi.org/10.3389/fpls.2012.00147.CrossRefPubMedPubMedCentralGoogle Scholar
  5. Bayon, S., Chen, G., Weselake, R. J., & Browse, J. (2015). A small phospholipase A2-alpha from castor catalyzes the removal of hydroxy fatty acids from phosphatidylcholine in transgenic Arabidopsis seeds. Plant Physiology, 167(4), 1259–1270.  https://doi.org/10.1104/pp.114.253641.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114–2120.  https://doi.org/10.1093/bioinformatics/btu170.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Broun, P., & Somerville, C. (1997). Accumulation of ricinoleic, lesquerolic, and densipolic acids in seeds of transgenic Arabidopsis plants that express a fatty acyl hydroxylase cDNA from castor bean. Plant Physiology, 113(3), 933–942.  https://doi.org/10.1104/pp.113.3.933.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Burgal, J., Shockey, J., Lu, C., Dyer, J., Larson, T., Graham, I., et al. (2008). Metabolic engineering of hydroxy fatty acid production in plants: RcDGAT2 drives dramatic increases in ricinoleate levels in seed oil. Plant Biotechnology Journal, 6(8), 819–831.  https://doi.org/10.1111/j.1467-7652.2008.00361.x.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Cai, Y., Goodman, J. M., Pyc, M., Mullen, R. T., Dyer, J. M., & Chapman, K. D. (2015). Arabidopsis SEIPIN proteins modulate triacylglycerol accumulation and influence lipid droplet proliferation. The Plant Cell, 27(9), 2616–2636.  https://doi.org/10.1105/tpc.15.00588.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Cesar, A. D., & Batalha, M. O. (2010). Biodiesel production from castor oil in Brazil: A difficult reality. Energy Policy, 38(8), 4031–4039.  https://doi.org/10.1016/j.enpol.2010.03.027.CrossRefGoogle Scholar
  11. Chapman, K. D., & Moore, T. S. Jr. (1993). N-acylphosphatidylethanolamine synthesis in plants: Occurrence, molecular composition, and phospholipid origin. Archives of Biochemistry and Biophysics, 301(1), 21–33.  https://doi.org/10.1006/abbi.1993.1110.CrossRefPubMedGoogle Scholar
  12. Chen, G. Q., van Erp, H., Martin-Moreno, J., Johnson, K., Morales, E., Browse, J., et al. (2016). Expression of castor LPAT2 enhances ricinoleic acid content at the sn-2 position of triacylglycerols in lesquerella seed. International Journal of Molecular Sciences. 17(4), 507,  https://doi.org/10.3390/ijms17040507.CrossRefPubMedPubMedCentralGoogle Scholar
  13. da Silva Ramos, L. C., Tango, J. S., Savi, A., & Leal, N. R. (1984). Variability for oil and fatty acid composition in castorbean varieties. Journal of the American Oil Chemists’ Society, 61(12), 1841–1843.CrossRefGoogle Scholar
  14. Feenstra, A. D., Alexander, L. E., Song, Z., Korte, A. R., Yandeau-Nelson, M., Nikolau, B. J., et al. (2017). Spatial mapping and profiling of metabolite distributions during germination. Plant Physiology, 174, 2532–2548.CrossRefGoogle Scholar
  15. Greenwood, J., Gifford, D., & Bewley, J. (1984). Seed development in Ricinus communis cv. Hale (castor bean). II. Accumulation of phytic acid in the developing endosperm and embryo in relation to the deposition of lipid, protein, and phosphorus. Canadian Journal of Botany, 62(2), 255–261.CrossRefGoogle Scholar
  16. Hankin, J. A., Barkley, R. M., & Murphy, R. C. (2007). Sublimation as a method of matrix application for mass spectrometric imaging. Journal of the American Society for Mass Spectrometry, 18(9), 1646–1652.  https://doi.org/10.1016/j.jasms.2007.06.010.CrossRefPubMedPubMedCentralGoogle Scholar
  17. He, X., Chen, G. Q., Lin, J. T., & McKeon, T. A. (2004). Regulation of diacylglycerol acyltransferase in developing seeds of castor. Lipids, 39(9), 865–871.CrossRefGoogle Scholar
  18. Horn, P. J., & Chapman, K. D. (2014). Metabolite imager: Customized spatial analysis of metabolite distributions in mass spectrometry imaging. Metabolomics, 10(2), 337–348.  https://doi.org/10.1007/s11306-013-0575-0.CrossRefGoogle Scholar
  19. Horn, P. J., James, C. N., Gidda, S. K., Kilaru, A., Dyer, J. M., Mullen, R. T., et al. (2013a). Identification of a new class of lipid droplet-associated proteins in plants. Plant Physiology, 162(4), 1926–1936.  https://doi.org/10.1104/pp.113.222455.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Horn, P. J., Korte, A. R., Neogi, P. B., Love, E., Fuchs, J., Strupat, K., et al. (2012). Spatial mapping of lipids at cellular resolution in embryos of cotton. The Plant Cell, 24(2), 622–636.  https://doi.org/10.1105/tpc.111.094581.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Horn, P. J., Silva, J. E., Anderson, D., Fuchs, J., Borisjuk, L., Nazarenus, T. J., et al. (2013b). Imaging heterogeneity of membrane and storage lipids in transgenic Camelina sativa seeds with altered fatty acid profiles. Plant Journal, 76(1), 138–150.  https://doi.org/10.1111/tpj.12278.CrossRefPubMedGoogle Scholar
  22. Hu, Z., Ren, Z., & Lu, C. (2012). The phosphatidylcholine diacylglycerol cholinephosphotransferase is required for efficient hydroxy fatty acid accumulation in transgenic Arabidopsis. Plant Physiology, 158(4), 1944–1954.  https://doi.org/10.1104/pp.111.192153.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Kennedy, E. P., & Weiss, S. B. (1956). The function of cytidine coenzymes in the biosynthesis of phospholipides. Journal of Biological Chemistry, 222(1), 193–214.PubMedGoogle Scholar
  24. Kim, H. U., Lee, K. R., Go, Y. S., Jung, J. H., Suh, M. C., & Kim, J. B. (2011). Endoplasmic reticulum-located PDAT1-2 from castor bean enhances hydroxy fatty acid accumulation in transgenic plants. Plant and Cell Physiology, 52(6), 983–993.  https://doi.org/10.1093/pcp/pcr051.CrossRefPubMedGoogle Scholar
  25. Knight, B. (1979). Ricin—A potent homicidal poison. London: BMJ Publishing Group.Google Scholar
  26. Lands, W. E. (1958). Metabolism of glycerolipides; a comparison of lecithin and triglyceride synthesis. Journal of Biological Chemistry, 231(2), 883–888.PubMedGoogle Scholar
  27. Li, M. Y., Baughman, E., Roth, M. R., Han, X. L., Welti, R., & Wang, X. M. (2014). Quantitative profiling and pattern analysis of triacylglycerol species in Arabidopsis seeds by electrospray ionization mass spectrometry. The Plant Journal, 77(1), 160–172.  https://doi.org/10.1111/tpj.12365.CrossRefPubMedGoogle Scholar
  28. Lord, J. M., Roberts, L. M., & Robertus, J. D. (1994). Ricin: Structure, mode of action, and some current applications. The FASEB Journal, 8(2), 201–208.CrossRefGoogle Scholar
  29. Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 15(12), 550,  https://doi.org/10.1186/s13059-014-0550-8.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Lu, C., Fulda, M., Wallis, J. G., & Browse, J. (2006). A high-throughput screen for genes from castor that boost hydroxy fatty acid accumulation in seed oils of transgenic Arabidopsis. The Plant Journal, 45(5), 847–856.  https://doi.org/10.1111/j.1365-313X.2005.02636.x.CrossRefPubMedGoogle Scholar
  31. Lu, C., Xin, Z., Ren, Z., Miquel, M., & Browse, J. (2009). An enzyme regulating triacylglycerol composition is encoded by the ROD1 gene of Arabidopsis. Proceedings of the National Academy of Sciences, 106(44), 18837–18842.  https://doi.org/10.1073/pnas.0908848106.CrossRefGoogle Scholar
  32. Lu, S., Sturtevant, D., Aziz, M., Jin, C., Li, Q., Chapman, K. D., et al. (2018). Spatial analysis of lipid metabolites and expressed genes reveals tissue-specific heterogeneity of lipid metabolism in high- and low-oil Brassica napus L. seeds. The Plant Journal, 94(6), 915–932.  https://doi.org/10.1111/tpj.13959.CrossRefPubMedGoogle Scholar
  33. Lunn, D., Smith, G. A., Wallis, J. G., & Browse, J. (2018b). Development defects of hydroxy-fatty acid-accumulating seeds are reduced by castor acyltransferases. Plant Physiology, 177(2), 553–564.  https://doi.org/10.1104/pp.17.01805.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Lunn, D., Wallis, J. G., & Browse, J. (2018a). Overexpression of seipin1 increases oil in hydroxy fatty acid-accumulating seeds. Plant and Cell Physiology, 59(1), 205–214.  https://doi.org/10.1093/pcp/pcx177.CrossRefPubMedGoogle Scholar
  35. Marmon, S., Sturtevant, D., Herrfurth, C., Chapman, K., Stymne, S., & Feussner, I. (2017). Two acyltransferases contribute differently to linolenic acid levels in seed oil. Plant Physiology, 173(4), 2081–2095.  https://doi.org/10.1104/pp.16.01865.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Mutlu, H., & Meier, M. A. R. (2010). Castor oil as a renewable resource for the chemical industry. European Journal of Lipid Science and Technology, 112(1), 10–30.  https://doi.org/10.1002/ejlt.200900138.CrossRefGoogle Scholar
  37. Ogunniyi, D. S. (2006). Castor oil: A vital industrial raw material. Bioresource Technology, 97(9), 1086–1091.  https://doi.org/10.1016/j.biortech.2005.03.028.CrossRefPubMedGoogle Scholar
  38. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods, 14(4), 417.  https://doi.org/10.1038/nmeth.4197.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Pfaffl, M. W., Horgan, G. W., & Dempfle, L. (2002). Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Research, 30, e36.CrossRefGoogle Scholar
  40. Pyc, M., Cai, Y., Greer, M. S., Yurchenko, O., Chapman, K. D., Dyer, J. M., et al. (2017). Turning over a new leaf in lipid droplet biology. Trends in Plant Science, 22(7), 596–609.  https://doi.org/10.1016/j.tplants.2017.03.012.CrossRefPubMedGoogle Scholar
  41. Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences, 100(16), 9440–9445.  https://doi.org/10.1073/pnas.1530509100.CrossRefGoogle Scholar
  42. Strohalm, M., Kavan, D., Novak, P., Volny, M., & Havlicek, V. (2010). mMass 3: A cross-platform software environment for precise analysis of mass spectrometric data. Analytical Chemistry, 82(11), 4648–4651.  https://doi.org/10.1021/ac100818g.CrossRefPubMedGoogle Scholar
  43. Sturtevant, D., Aziz, M., & Chapman, K. D. (2018). Visualizing the oilseed lipidome. INFORM. 29(4).Google Scholar
  44. Sturtevant, D., Duenas, M. E., Lee, Y. J., & Chapman, K. D. (2017a). Three-dimensional visualization of membrane phospholipid distributions in Arabidopsis thaliana seeds: A spatial perspective of molecular heterogeneity. Biochimica Et Biophysica Acta-Molecular and Cell Biology of Lipids, 1862(2), 268–281.  https://doi.org/10.1016/j.bbalip.2016.11.012.CrossRefPubMedGoogle Scholar
  45. Sturtevant, D., Horn, P., Kennedy, C., Hinze, L., Percy, R., & Chapman, K. (2017b). Lipid metabolites in seeds of diverse Gossypium accessions: Molecular identification of a high oleic mutant allele. Planta, 245(3), 595–610.  https://doi.org/10.1007/s00425-016-2630-3.CrossRefPubMedGoogle Scholar
  46. Sturtevant, D., Lee, Y. J., & Chapman, K. D. (2016). Matrix assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) for direct visualization of plant metabolites in situ. Current Opinion in Biotechnology, 37, 53–60.  https://doi.org/10.1016/j.copbio.2015.10.004.CrossRefPubMedGoogle Scholar
  47. van de Loo, F. J., Broun, P., Turner, S., & Somerville, C. (1995a). An oleate 12-hydroxylase from Ricinus communis L. is a fatty acyl desaturase homolog. Proceedings of the National Academy of Sciences, 92(15), 6743–6747.CrossRefGoogle Scholar
  48. van de Loo, F. J., Turner, S., & Somerville, C. (1995b). Expressed sequence tags from developing castor seeds. Plant Physiology, 108(3), 1141–1150.CrossRefGoogle Scholar
  49. van Erp, H., Bates, P. D., Burgal, J., Shockey, J., & Browse, J. (2011). Castor phospholipid:diacylglycerol acyltransferase facilitates efficient metabolism of hydroxy fatty acids in transgenic Arabidopsis. Plant Physiology, 155(2), 683–693.  https://doi.org/10.1104/pp.110.167239.CrossRefPubMedGoogle Scholar
  50. van Erp, H., Shockey, J., Zhang, M., Adhikari, N. D., & Browse, J. (2015). Reducing isozyme competition increases target fatty acid accumulation in seed triacylglycerols of transgenic Arabidopsis. Plant Physiology, 168(1), 36–46.  https://doi.org/10.1104/pp.114.254110.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Welti, R., Li, W. Q., Li, M. Y., Sang, Y. M., Biesiada, H., Zhou, H. E., et al. (2002). Profiling membrane lipids in plant stress responses—Role of phospholipase D alpha in freezing-induced lipid changes in Arabidopsis. Journal of Biological Chemistry, 277(35), 31994–32002.  https://doi.org/10.1074/jbc.M205375200.CrossRefPubMedGoogle Scholar
  52. Woodfield, H. K., Sturtevant, D., Borisjuk, L., Munz, E., Guschina, I. A., Chapman, K., et al. (2017). Spatial and temporal mapping of key lipid species in Brassica napus seeds. Plant Physiology, 173(4), 1998–2009.  https://doi.org/10.1104/pp.16.01705.CrossRefPubMedPubMedCentralGoogle Scholar
  53. Wu, Y. R., Llewellyn, D. J., & Dennis, E. S. (2002). A quick and easy method for isolating good-quality RNA from cotton (Gossypium hirsutum L.) tissues. Plant Molecular Biology Reporter, 20(3), 213–218. doi: https://doi.org/10.1007/Bf02782456.CrossRefGoogle Scholar
  54. Yao, L. X., Hammond, E. G., Wang, T., Bhuyan, S., & Sundararajan, S. (2010). Synthesis and physical properties of potential biolubricants based on ricinoleic acid. Journal of the American Oil Chemists Society, 87(8), 937–945.  https://doi.org/10.1007/s11746-010-1574-1.CrossRefGoogle Scholar
  55. Yu, X. H., Cahoon, R. E., Horn, P. J., Shi, H., Prakash, R. R., Cai, Y., et al. (2018). Identification of bottlenecks in the accumulation of cyclic fatty acids in camelina seed oil. Plant Biotechnology Journal, 16(4), 926–938.  https://doi.org/10.1111/pbi.12839.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biological SciencesUniversity of North TexasDentonUSA
  2. 2.BioDiscovery InstituteUniversity of North TexasDentonUSA
  3. 3.Department of Biochemistry and Cell BiologyStony Brook UniversityStony BrookUSA
  4. 4.Department of MathematicsUniversity of North TexasDentonUSA
  5. 5.Biology DepartmentBrookhaven National LaboratoryUptonUSA

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