Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Microarray reality checks in the context of a complex disease

Nature Biotechnology volume 22, pages 615–621 (2004)Cite this article

Abstract

A problem in analyzing microarray-based gene expression data is the separation of genes causally involved in a disease from innocent bystander genes, whose expression levels have been secondarily altered by primary changes elsewhere. To investigate this issue systematically in the context of a class of complex human diseases, we have compared microarray-based gene expression data with non-microarray-based clinical and biological data about the schizophrenias to ask whether these two approaches prioritize the same genes. We find that genes whose expression changes are deemed to be of importance from microarrays are rarely those classified as of importance from clinical, in situ, molecular, single-nucleotide polymorphism (SNP) association, knockout and drug perturbation data. This disparity is not limited to the schizophrenias but characterizes other human disease data sets. It also extends to biological validation of microarray data in model organisms, in which genome-wide phenotypic data have been systematically compared with microarray data. In addition, different bioinformatic protocols applied to the same microarray data yield quite different gene sets and thus make clinical decisions less straightforward. We discuss how progress may be improved in the clinical area by the assignment of high-quality phenotypic values to each member of a microarray-assigned gene set.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Venn diagram illustrating the overlap between genes prioritized by different methodologies.

Similar content being viewed by others

References

  1. The Chipping Forecast II. Nature Genet. 32 (Suppl.), 465–552 (2002).

  2. Taussig, M.J. & Landegren, U. Progress in antibody arrays. Targets 2, 169–176 (2003).

    Article  CAS  Google Scholar 

  3. Ziauddin, J. & Sabatini, D.M. Microarrays of cells expressing defined cDNAs. Nature 411, 107–110 (2001).

    Article  CAS  PubMed  Google Scholar 

  4. Garber, K. Gene expression tests foretell breast cancer's future. Science 303, 1754–1755 (2004).

    Article  PubMed  Google Scholar 

  5. Holzman, T. & Kolker, E. Statistical analysis of global gene expression data: some practical considerations. Curr. Opin. Biotechnol. 15, 52–57 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Wigle, D., Tsao, M. & Jurisica, I. Making sense of lung-cancer gene-expression profiles. Genome Biol. 5, 309 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Andreasen, N.C., Arndt, S., Alliger, R., Miller, D. & Flaum, M. Symptoms of schizophrenia. Methods, meanings and mechanisms. Arch. Gen. Psychiatry 52, 341–351 (1995).

    Article  CAS  PubMed  Google Scholar 

  8. Andreasen, N.C. A unitary model of schizophrenia. Bleuler's “fragmented Phrene” as schizencephaly. Arch. Gen. Psychiatry 56, 781–787 (1999).

    Article  CAS  PubMed  Google Scholar 

  9. Sedvall, G. & Farde, L. Chemical brain anatomy in schizophrenia. Lancet 346, 743–749 (1995).

    Article  CAS  PubMed  Google Scholar 

  10. Harrison, P.J. The neuropathology of schizophrenia. A critical review of the data and their interpretation. Brain 122, 593–624 (1999).

    Article  PubMed  Google Scholar 

  11. McCarley, R.W. et al. MRI anatomy of schizophrenia. Biol. Psychiatry 45, 1099–1119 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Andreasen, N.C. Schizophrenia: the fundamental questions. Brain Res. Rev 31, 106–112 (2000).

    Article  CAS  PubMed  Google Scholar 

  13. Yolken, R.H., Karlsson, H., Yee, F., Johnston-Wilson, N.L. & Torrey, E.F. Endogenous retroviruses and schizophrenia. Brain Res. Rev. 31, 193–199 (2000).

    Article  CAS  PubMed  Google Scholar 

  14. Rothermundt, M., Arolt, V. & Bayer, T.A. Review of immunological and immunopathological findings in schizophrenia. Brain Behav. Immun. 15, 319–339 (2001).

    Article  CAS  PubMed  Google Scholar 

  15. Karlsson, H. et al. Retroviral RNA identified in the cerebrospinal fluids and brains of individuals with schizophrenia. Proc. Natl. Acad. Sci. USA 98, 4634–4639 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Freedman, R. Schizophrenia. N. Engl. J. Med. 349, 1738–1749 (2003).

    Article  CAS  PubMed  Google Scholar 

  17. Honey, G.D. et al. The functional neuroanatomy of schizophrenic subsyndromes. Psychol. Med. 33, 1007–1018 (2003).

    Article  CAS  PubMed  Google Scholar 

  18. Hakak, Y. et al. Genome-wide expression analysis reveals dysregulation of myelination-related genes in chronic schizophrenia. Proc. Natl. Acad. Sci. USA 98, 4746–4751 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mirnics, K., Middleton, F.A., Marquez, A., Lewis, D.A. & Levitt, P. Molecular characterization of schizophrenia viewed by microarray analysis of gene expression in prefrontal cortex. Neuron 28, 53–67 (2000).

    Article  CAS  PubMed  Google Scholar 

  20. Vawter, M.P. et al. Application of cDNA microarrays to examine gene expression differences in schizophrenia. Brain Res. Bull. 55, 641–650 (2001).

    Article  CAS  PubMed  Google Scholar 

  21. Mimmack, M.L. et al. Gene expression analysis in schizophrenia: reproducible up-regulation of several members of the apolipoprotein L family located in a high-susceptibility locus for schizophrenia on chromosome 22. Proc. Natl. Acad. Sci. USA 99, 4680–4685 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Middleton, F.A., Mirnics, K., Pierri, J.N., Lewis, D.A. & Levitt, P. Gene expression profiling reveals alterations of specific metabolic pathways in schizophrenia. J. Neurosci. 22, 2718–2729 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pongrac, J., Middleton, F.A., Lewis, D.A., Levitt, P. & Mirnics, K. Gene expression profiling with DNA microarrays: advancing our understanding of psychiatric disorders. Neurochem. Res. 27, 1049–1063 (2002).

    Article  CAS  PubMed  Google Scholar 

  24. Guidotti, A. et al. Decrease in reelin and glutamic acid decarboxylase 67 (GAD67) expression in schizophrenia and bipolar disorder. Arch. Gen. Psychiatry 57, 1061–1069 (2000).

    Article  CAS  PubMed  Google Scholar 

  25. Popken, G.J., Bunney, W.E., Potkin, S.G. & Jones, E.G. Subnucleus-specific loss of neurons in medial thalamus of schizophrenics. Proc. Natl. Acad. Sci. USA 97, 9276–9280 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Thompson, P.M. et al. Mapping adolescent brain change reveals dynamic wave of accelerated grey matter loss in very early-onset schizophrenia. Proc. Natl. Acad. Sci. USA 98, 11650–11655 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rajkowska, G. & Goldman-Rakic, P.S. Cytoarchitectonic definition of prefrontal areas in the normal human cortex: II. Variability in locations of areas 9 and 46 and relationship to the Talairach coordinate system. Cereb. Cortex 5, 323–337 (1995).

    Article  CAS  PubMed  Google Scholar 

  28. Browning, M.D., Dudek, E.M., Rapier, J.L., Leonard, S. & Freedman, R. Significant reductions in synapsin but not synaptophysin specific activity in the brains of some schizophrenics. Biol. Psychiatry 34, 529–535 (1993).

    Article  CAS  PubMed  Google Scholar 

  29. McGinnis, R.E. et al. Failure to confirm NOTCH4 association with schizophrenia in a large population-based sample from Scotland. Nature Genet. 28, 128–129 (2001).

    Article  CAS  PubMed  Google Scholar 

  30. Volk, D.W., Austin, M.C., Pierri, J.N., Sampson, A.R. & Lewis, D.A. Decreased glutamic acid decarboxylase67 messenger RNA expression in a subset of prefrontal cortical γ-aminobutyric acid neurons in subjects with schizophrenia. Arch. Gen. Psychiatry 57, 237–245 (2000).

    Article  CAS  PubMed  Google Scholar 

  31. Bernstein, H.-G.C.A. et al. Hippocampal expression of the calcium sensor protein visinin-like protein-1 in schizophrenia. Neuroreport 13, 393–396 (2002).

    Article  CAS  PubMed  Google Scholar 

  32. Chudin, E. et al. Assessment of the relationship between signal intensities and transcript concentration for Affymetrix GeneChip arrays. Genome Biol. 3, research0005.1z–0005.10 (2001).

    Article  Google Scholar 

  33. Churchill, G.A. Fundamentals of experimental design for cDNA microarrays. Nature Genet. 32 suppl. Suppl., 490–495 (2002).

    Article  CAS  PubMed  Google Scholar 

  34. Kothapalli, R., Yoder, S.J., Mane, S. & Loughran, T.P. Microarray results: how accurate are they? BMC Bioinformatics 3, 22 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kuo, W.P. et al. Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 18, 405–412 (2002).

    Article  CAS  PubMed  Google Scholar 

  36. Li, J., Pankratz, M. & Johnson, J.A. Differential gene expression patterns revealed by oligonucleotide versus long cDNA arrays. Toxicol. Sci. 69, 383–390 (2002).

    Article  CAS  PubMed  Google Scholar 

  37. Barczak, A. et al. Spotted long oligonucleotide arrays for human gene expression analysis. Genome Res. 13, 1775–1785 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Fan, J., Tam, P., Vande Woude, G. & Ren, Y. Normalization and analysis of cDNA microarrays using within-array replications applied to neuroblastoma cell response to a cytokine. Proc. Natl. Acad. Sci. USA 101, 1135–1140 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Mills, J.C. & Gordon, J.I. A new approach for filtering noise from high-density oligonucleotide microarray datasets. Nucleic Acids Res. 29, E72–2 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Li, C. & Wong, W.H. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Natl. Acad. Sci. USA 98, 31–36 (2001).

    Article  CAS  PubMed  Google Scholar 

  41. Huntsman, M.M., Tran, B.-V., Potkin, S.G., Bunney, W.E. & Jones, E.G. Altered ratios of alternatively spliced long and short γ2 subunit mRNAs of the γ-amino butyrate type A receptor in prefrontal cortex of schizophrenics. Proc. Natl. Acad. Sci. USA 95, 15066–15071 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tkachev, D. et al. Oligodendrocyte dysfunction in schizophrenia and bipolar disorder. Lancet 362, 798–805 (2003).

    Article  CAS  PubMed  Google Scholar 

  43. Kiiveri, H.T. A Bayesian approach to variable selection when the number of variables is very large. Institute of Mathematical Statistics, Lecture Notes, Monograph Series. 40, 127–143 (2003).

  44. Moler, E.J. et al. Analysis of molecular profile data using generative and discriminative methods. Physiol. Genomics 4, 109–126 (2000).

    Article  CAS  PubMed  Google Scholar 

  45. Staudt, L.M. & Brown, P.O. Genomic views of the immune system. Annu. Rev. Immunol. 18, 829–859 (2000).

    Article  CAS  PubMed  Google Scholar 

  46. Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001).

    Article  CAS  PubMed  Google Scholar 

  47. Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lemon, W.J., Liyanarachchi, S. & You, M. A high performance test of differential gene expression for oligonucleotide arrays. Genome Biol. 4, R67 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Somorjai, R.L., Dolenko, B. & Baumgartner, R. Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics 19, 1484–1491 (2003).

    Article  CAS  PubMed  Google Scholar 

  50. Morris, J.S., Yin, G., Baggerly, K.A., Wu, C. & Zhang, L. Pooling information across different studies and oligonucleotide microarray chip types to identify prognostic genes for lung cancer. Methods Microarray Anal. in the press. III, 1–16 (2004).

    Google Scholar 

  51. Robb, L., Stephens, R. & Coleman, J. Application of survival and multivariate methods to gene expression data combined from two sources. Methods Microarray Anal. III (2004), in the press.

  52. Jung, S.-H., Owzar, K. & George, S. Associating microarray data with a survival endpoint. Methods Microarray Anal. III (2004), in the press.

  53. Bhattacharjee, A. et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. USA 98, 13790–13795 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Garber, M.E. et al. Diversity of gene expression in adenocarcinoma of the lung. Proc. Natl. Acad. Sci. USA 98, 13784–13789 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wigle, D.A. et al. Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Res. 62, 3005–3008 (2002).

    CAS  PubMed  Google Scholar 

  56. Beer, D.G. et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nature Med. 8, 816–824 (2002).

    Article  CAS  PubMed  Google Scholar 

  57. Shedden, K. & Cooper, S. Analysis of cell cycle–specific gene expression in human cells as determined by microarrays and double-thymidine block synchronization. Proc. Natl. Acad. Sci. USA 99, 4379–4384 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Gerdes, S.Y. et al. Experimental determination and system level analysis of essential genes in Escherichia coli MG1655. J. Bacteriol. 185, 5673–5684 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Birrell, G.W. et al. Transcriptional response of Saccharomyces cerevisiae to DNA-damaging agents does not identify the genes that protect against these agents. Proc. Natl. Acad. Sci. USA 99, 8778–8783 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ooi, S.L., Shoemaker, D.D. & Boeke, J.D. DNA helicase gene interaction network define using synthetic lethality analyzed by microarray. Nature Genet. 35, 277–286 (2003).

    Article  CAS  PubMed  Google Scholar 

  61. Jeong, H., Mason, S.P., Barabasi, A.-L. & Oltvai, Z.N. Lethality and centrality in protein networks. Nature 411, 41–42 (2001).

    Article  CAS  PubMed  Google Scholar 

  62. Thatcher, J.W., Shaw, J.M. & Dickinson, W.J. Marginal contributions of nonessential genes in yeast. Proc. Natl. Acad. Sci. USA 95, 253–257 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Miklos, G.L.G. & Rubin, G.M. The role of the genome project in determining gene function: insights from model organisms. Cell 86, 521–529 (1996).

    Article  CAS  PubMed  Google Scholar 

  64. Zhang, L. et al. Gene expression profiles in normal and cancer cells. Science 276, 1268–1272 (1997).

    Article  CAS  PubMed  Google Scholar 

  65. John, B. & Miklos, G.L.G. The Eukaryote Genome in Development and Evolution (Allen & Unwin, London, 1988).

    Google Scholar 

  66. Colucci-Guyon, E. et al. Mice lacking vimentin develop and reproduce without an obvious phenotype. Cell 79, 679–694 (1994).

    Article  CAS  PubMed  Google Scholar 

  67. Erikson, H.P. A tenascin knockout with a phenotype. Nature Genet. 17, 5–8 (1997).

    Article  Google Scholar 

  68. Mills, M.A. et al. Differential expression of the actin-binding proteins, α-actinin-2 and -3 in different species: implications for the evolution of functional redundancy. Hum. Mol. Genet. 10, 1335–1346 (2001).

    Article  CAS  PubMed  Google Scholar 

  69. Watkins, S. et al. Analbumenia: three cases resulting from different point mutations in the albumin gene. Proc. Natl. Acad. Sci. USA 91, 9417–9421 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Stock, R.P. & Bialy, H. The sigmoidal curve of cancer. Nature Biotechnol. 21, 13–14 (2003).

    Article  CAS  Google Scholar 

  71. Kacser, H. & Burns, J.A. The molecular basis of dominance. Genetics 97, 639–666 (1981).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Wuensche, A. Basins of attraction in network dynamics. in Modularity in Development and Evolution (eds. Schlosser, G. & Wagner, G.P.) 1–17 (Chicago University Press, Chicago, Illinois, 2004).

    Google Scholar 

  73. DeRisi, J.L., Iyer, V.R. & Brown, P.O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997).

    Article  CAS  PubMed  Google Scholar 

  74. Arkin, A., Shen, P. & Ross, J. A test case of correlation metric construction of a reaction pathway from measurements. Science 277, 1275–1279 (1997).

    Article  CAS  Google Scholar 

  75. Dewey, T.G. From microarrays to networks: mining expression time series. Drug Discov. Today 7, S170–S175 (2002).

    Article  PubMed  Google Scholar 

  76. Klevecz, R.R., Bolen, J., Forrest, G. & Murray, D.B. A genome-wide oscillation in transcription gates DNA replication and cell cycle. Proc. Natl. Acad. Sci. USA 101, 1200–1205 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Almaas, E., Kovacs, B., Vicsek, T., Oltvai, Z.N. & Barabasi, A.-L. Global organization of metabolic fluses in the bacterium Escherichia coli. Nature 427, 839–843 (2004).

    Article  CAS  PubMed  Google Scholar 

  78. Kitano, H. Computational systems biology. Nature 420, 206–210 (2002).

    Article  CAS  PubMed  Google Scholar 

  79. Barabasi, A.-L. Linked: The New Science of Networks (Persus Publishing, Cambridge, Massachusetts, USA, 2002).

    Google Scholar 

  80. Paulsson, J. Summing up the noise in gene networks. Nature 427, 415–418 (2004).

    Article  CAS  PubMed  Google Scholar 

  81. Miklos, G.L.G. & Maleszka, R. Integrating molecular medicine with functional proteomics: realities and expectations. Proteomics 1, 30–41 (2001).

    Article  CAS  PubMed  Google Scholar 

  82. Miklos, G.L.G. & Maleszka, R. Protein functions and biological contexts. Proteomics 1, 169–178 (2001).

    Article  CAS  PubMed  Google Scholar 

  83. Strohman, R. Maneuvering in the complex path from genotype to phenotype. Science 296, 701–703 (2002).

    Article  CAS  PubMed  Google Scholar 

  84. Carney, S.L. Leroy Hood expounds the principles, practice and future of systems biology. Drug Discov. Today 8, 436–438 (2003).

    Article  Google Scholar 

  85. Palsson, B.O. In silico biotechnology. Era of reconstruction and interrogation. Curr. Opin. Biotechnol. 15, 50–51 (2004).

    Article  CAS  PubMed  Google Scholar 

  86. Miklos, G.L.G. Molecules to cognition: the latter-day lessons of levels, language and lac. Evolutionary overview of brain structure and function in some vertebrates and invertebrates. J. Neurobiol. 24, 842–890 (1993).

    Article  CAS  PubMed  Google Scholar 

  87. Famili, I., Forster, J., Nielsen, J. & Palsson, B.O. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc. Natl. Acad. Sci. USA 100, 13134–13139 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Liao, J.C. et al. Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl. Acad. Sci. USA 100, 15522–15527 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Davidson, E.H., McClay, D.R. & Hood, L. Regulatory gene networks and the properties of the developmental process. Proc. Natl. Acad. Sci. USA 100, 1475–1480 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Herrgard, M.J., Covert, M.W. & Palsson, B.O. Reconciling gene expression data with known genome-scale regulatory network structures. Genome Res. 13, 2423–2434 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Li, J.Z. et al. Systematic changes in gene expression in postmortem human brains associated with tissue pH and terminal medical conditions. Hum. Mol. Genet. 13, 609–616 (2004).

    Article  CAS  PubMed  Google Scholar 

  92. Reese, M.G. et al. Genome annotation assessment in Drosophila melanogaster. Genome Res. 10, 483–501 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Jones, L., Ng, S.-K., Ambroise, C. & McLachlan, G. Use of microarray data via model-based classification in the study and prediction of survival from lung cancer. Methods Microarray Anal. III (2004), in the press.

  94. Matsuzaki, H. et al. Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array. Genome Res. 14, 414–425 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Alberto Ferrus for very helpful discussions and Rob Dunne, David Mitchell and Glenn Stone for clarification of bioinformatic protocols related to expression microarray data deconvolution.

Author information

Authors and Affiliations

  1. Secure Genetics, 81 Bynya Road, Palm Beach, Sydney, 2108, NSW, Australia

    George L Gabor Miklos

  2. Human Genetic Signatures,

    George L Gabor Miklos

  3. Research School of Biological Sciences, Visual Sciences, Australian National University, Canberra, 0200, ACT, Australia

    Ryszard Maleszka

Authors
  1. George L Gabor Miklos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryszard Maleszka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George L Gabor Miklos.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Miklos, G., Maleszka, R. Microarray reality checks in the context of a complex disease. Nat Biotechnol 22, 615–621 (2004). https://doi.org/10.1038/nbt965

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt965

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing