Much of our current knowledge about genes and their expression is based on the approximately 7 million expressed sequence tag (EST) sequences in the UniGene database. Sumio Sugano (University of Tokyo, Japan) introduced the next-generation DNA sequencers, such as those marketed by Illumina, ABI, and Roche, which promise to determine more than 10 million sequences from just one experiment. With this remarkable technology, genes, promoters and transcription start sites will in future be able to be mapped in single cell types with unprecedented precision. Sugano showed the preliminary results of such an unbiased approach, where an Illumina sequencer had been used to map transcription start sites and transcripts. He concluded that some start sites are indeed cell-type specific and that the huge number of tags generated permits fine-grained analysis of gene expression. But in every million cDNAs captured and sequenced by these techniques, gene expression turns out to range from a few transcripts to thousands of transcripts from the same gene. Sugano pointed out that with the depth of data achievable with next-generation sequencing, sparse transcription cannot be distinguished from what could be termed 'transcriptional noise'. There are no clear cutoffs, which complicates the detection of rarely expressed genes, and especially of intergenic and antisense transcription.
Caroline Shamu (Harvard Medical School, Boston, USA) discussed the many challenges associated with currently fashionable genome-scale screening by RNA interference (RNAi) using small interfering RNAs (siRNAs). She reported on projects where high-throughput transfection methods such as reverse transfection are combined with a conventional plate-and-assay design and high-content read-out to conduct more than 20 large-scale primary screens in different human and mouse cell lines. In her talk she concentrated on technical issues of RNAi screening in her central facility, stressing the importance of spending enough effort to make the assay robust, and to work on plate designs in order to circumvent edge and plate effects as these hamper data analysis. Once these issues are addressed, RNAi seems to be rather robust, as she screened for phenotypes in cancer, infectious diseases, neurobiology, and stem-cell biology, utilizing a number of different cell lines in combination with diverse transfection reagents and siRNA concentrations. While initial RNAi screens had mostly been done with plate readers, data acquisition is increasingly shifting towards high-content screening microscopy. Dorit Arlt (German Cancer Research Center, Heidelberg, Germany) reported that RNAi is also ideal for identifying functional interaction networks of genes. She presented data where knock-down of a single network component did not have a phenotype itself, yet the parallel perturbation of two or more genes did, thus revealing their functional interactions with the network. First she established a literature network of cell-cycle regulation consisting of the ErbB receptor family, AKT1 and MEK1 signaling intermediates, estrogen receptor alpha and Myc transcription factors, and cyclins D1 and E1 as well as cyclin-dependent kinases Cdk1, 4 and 6 as effector molecules. The input was epidermal growth factor (EGF), and the phosphorylation state of the retinoblatoma (Rb) protein was measured in response to siRNA treatments. She systematically perturbed the network components alone and in combinations to identify critical components in the regulation of that network. Indeed, she found novel edges in that network, most of which indicated feedback regulations, for example from cyclin D1 to AKT1 and MEK1. There was a common feeling that such screens will unravel the molecular mechanisms of cellular processes and potentially define major targets for interventions to cure human diseases. But it also became clear that such experiments take months rather than days, which needs to be improved.
The complementation of functional gene-interaction experiments with information on physical protein-protein interactions is a logical next step in the generation of protein networks. Using tandem affinity purification (TAP), Anne-Claude Gavin (EMBL, Heidelberg, Germany) and her collaborators have found that at least 80% of the proteins in yeast exert their function in complexes with other proteins. She stressed the point that protein complexes are, in general, highly dynamic structures, and often the same proteins are components of several protein complexes. To fully understand the modularity of the proteome in all its dynamics and stoichiometry will thus be a true challenge for the coming years.
Two array-based platforms were discussed as tools for qualitative and quantitative proteomics. The nucleic acid programmable protein array (NAPPA) presented by Joshua LaBaer (Harvard Institute of Proteomics, Boston, USA) enables the in situ production of large numbers of different protein probes with a success rate of greater than 90%. For use in this system, comprehensive collections of expression plasmids harboring the protein-coding regions of genes are being established at Harvard (in the Flexgene project) and by an international project (the ORFeome Collaboration). LaBaer described how NAPPA arrays have been used to generate protein-protein interaction maps, to test for serum-responsive proteins in the Pseudomonas proteome, and to detect tumor-associated antigens as a way of monitoring responses to cancer therapy. On the quantitative side, protein microarrays consisting of spotted protein lysates or antibodies tagged with Odyssey IRDye 680 or IRDye 800 were introduced by Ulrike Korf (DKFZ, Heidelberg, Germany). Detection of signals in the near infrared led to low background, low variability between samples and a high dynamic range. The highly parallel setup of these arrays enabled the dynamics of activation of the kinase ERK after stimulation with erythropoietin to be quantified in cell lines, for example. A problem in applying this method on the genome scale is the availability of high-quality antibodies that must be highly specific for their respective targets.