Cancer Institute A national cancer institute
designated cancer center

Maximilian Diehn, MD, PhD

Publication Details

  • Large-scale identification of secreted and membrane-associated gene products using DNA microarrays NATURE GENETICS Diehn, M., Eisen, M. B., Botstein, D., Brown, P. O. 2000; 25 (1): 58-62

    Abstract:

    Membrane-associated and secreted proteins are an important class of proteins and include receptors, transporters, adhesion molecules, hormones and cytokines. Although algorithms have been developed to recognize potential amino-terminal membrane-targeting signals or transmembrane domains in protein sequences, their accuracy is limited and they require knowledge of the entire coding sequence, including the N terminus, which is not currently available for most of the genes in most organisms, including human. Several experimental approaches for identifying secreted and membrane proteins have been described, but none have taken a comprehensive genomic approach. Furthermore, none of these methods allow easy classification of clones from arrayed cDNA libraries, for which large-scale gene-expression data are now becoming available through the use of DNA microarrays. We describe here a rapid and efficient method for identifying genes that encode secreted or membrane proteins. mRNA species bound to membrane-associated polysomes were separated from other mRNAs by sedimentation equilibrium or sedimentation velocity. The distribution of individual transcripts in the 'membrane-bound' and 'cytosolic' fractions was quantitated for thousands of genes by hybridization to DNA microarrays. Transcripts known to encode secreted or membrane proteins were enriched in the membrane-bound fractions, whereas those known to encode cytoplasmic proteins were enriched in the fractions containing mRNAs associated with free and cytoplasmic ribosomes. On this basis, we identified over 275 human genes and 285 yeast genes that are likely to encode previously unrecognized secreted or membrane proteins.

    View details for Web of Science ID 000086884000017

    View details for PubMedID 10802657

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