In PLoS One this week, investigators at McGill University report their use of exon-level transcriptome profiling in murine breast cancer, which revealed "splicing changes specific to tumors with different metastatic abilities." Specifically, in their statistical analysis the team found "significant expression changes in four percent of exons and one percent of intronic regions, affecting 2,263 out of 16,654 genes. The team's subsequent pathway analysis showed that "1,224 of genes expressing alternative isoforms were involved in cell growth, cell interactions, cell proliferation, cell migration, and cell death and have been previously linked to cancers and genetic disorders." Further identification of metastasis-specific isoforms could contribute to improved breast cancer stage identification, the authors suggest.
Also in PLoS One this week, an international research team presents a protocol for combining SuperSAGE digital gene expression technology with next-generation sequencing. Hideo Matsumura of the Iwate Biotechnology Research Center in Kitakami, Japan, and colleagues use barcode sequences to discriminate tags from different samples, allowing them "to analyze digital tags from transcriptomes of many samples in a single sequencing run by simply pooling the libraries." Matsumura et al. suggest that HT-SuperSAGE could be applied to the analysis of laser-microdissected cells, or could be useful for "biological replicates and tag extraction using different anchoring enzymes."
A multidisciplinary team led by investigators at Harvard University reports in PLoS Genetics this week that their use of RNAi screening methods "implicates a SKN-1–dependent transcriptional response in stress resistance and longevity deriving from translation inhibition." In a C. elegans model, the authors identified "41 genes for which knockdown leads to activation of a SKN-1 target gene (gcs-1) through skn-1-dependent or other mechanisms," and suggest that the SKN-1 detoxification gene network could monitor various metabolic and regulatory processes.
In PLoS Computational Biology, Masaki Tsuda and Masakado Kawata at Tohuku University in Sendai, Japan, describe the "evolution of gene regulatory networks by fluctuating selection and intrinsic constraints." Using an individual-based model, the duo "examined the effect of fluctuating environmental selection and some intrinsic constraining factors on GRN evolution," and found that the "evolution of complex GRNs is remarkably promoted by fixation of beneficial gene duplications under unpredictably fluctuating environmental conditions." Tsuda and Kawata suggest that certain properties of GRNs could evolve as a non-adaptive process — owing to the process of the organism — as well as in response to unpredictable environmental changes.