Genome-wide analysis of immune activation in human T and B cells reveals distinct classes of alternatively spliced genes.

Alternative splicing of pre-mRNA is a mechanism that increases the protein diversity of a single gene by differential exon inclusion/exclusion during post-transcriptional processing. While alternative splicing is established to occur during lymphocyte activation, little is known about the role it plays during the immune response. Our study is among the first reports of a systematic genome-wide analysis of activated human T and B lymphocytes using whole exon DNA microarrays integrating alternative splicing and differential gene expression. Purified human CD2(+) T or CD19(+) B cells were activated using protocols to model the early events in post-transplant allograft immunity and sampled as a function of time during the process of immune activation. Here we show that 3 distinct classes of alternatively spliced and/or differentially expressed genes change in an ordered manner as a function of immune activation. We mapped our results to function-based canonical pathways and demonstrated that some are populated by only one class of genes, like integrin signaling, while other pathways, such as purine metabolism and T cell receptor signaling, are populated by all three classes of genes. Our studies augment the current view of T and B cell activation in immunity that has been based exclusively upon differential gene expression by providing evidence for a large number of molecular networks populated as a function of time and activation by alternatively spliced genes, many of which are constitutively expressed.

A fast coarse filtering method for peptide identification by mass spectrometry.

MOTIVATION: We reformulate the problem of comparing mass-spectra by mapping spectra to a vector space model. Our search method leverages a metric space indexing algorithm to produce an initial candidate set, which can be followed by any fine ranking scheme. RESULTS: We consider three distance measures integrated into a multi-vantage point index structure. Of these, a semi-metric fuzzy-cosine distance using peptide precursor mass constraints performs the best. The index acts as a coarse, lossless filter with respect to the SEQUEST and ProFound scoring schemes, reducing the number of distance computations and returned candidates for fine filtering to about 0.5% and 0.02% of the database respectively. The fuzzy cosine distance term improves specificity over a peptide precursor mass filter, reducing the number of returned candidates by an order of magnitude. Run time measurements suggest proportional speedups in overall search times. Using an implementation of ProFound's Bayesian score as an example of a fine filter on a test set of Escherichia coli protein fragmentation spectra, the top results of our sample system are consistent with that of SEQUEST.