Gene structure-based splice variant deconvolution using a microarray platform

MOTIVATION: Alternative splicing allows a single gene to generate multiple mRNAs, which can be translated into functionally and structurally diverse proteins. One gene can have multiple variants coexisting at different concentrations. Estimating the relative abundance of each variant is important for the study of underlying biological function. Microarrays are standard tools that measure gene expression. But most design and analysis has not accounted for splice variants. Thus splice variant-specific chip designs and analysis algorithms are needed for accurate gene expression profiling. RESULTS: Inspired by Li and Wong (2001), we developed a gene structure-based algorithm to determine the relative abundance of known splice variants. Probe intensities are modeled across multiple experiments using gene structures as constraints. Model parameters are obtained through a maximum likelihood estimation (MLE) process/framework. The algorithm produces the relative concentration of each variant, as well as an affinity term associated with each probe. Validation of the algorithm is performed by a set of controlled spike experiments as well as endogenous tissue samples using a human splice variant array.     

Genome-wide detection of tissue-specific alternative splicing in the human transcriptome

We have developed an automated method for discovering tissue-specific regulation of alternative splicing through a genome-wide analysis of expressed sequence tags (ESTs). Using this approach, we have identified 667 tissue-specific alternative splice forms of human genes. We validated our muscle-specific and brain-specific splice forms for known genes. A high fraction (8/10) were reported to have a matching tissue specificity by independent studies in the published literature. The number of tissue-specific alternative splice forms is highest in brain, while eye-retina, muscle, skin, testis and lymph have the greatest enrichment of tissue-specific splicing. Overall, 10-30% of human alternatively spliced genes in our data show evidence of tissue-specific splice forms. Seventy-eight percent of our tissue-specific alternative splices appear to be novel discoveries. We present bioinformatics analysis of several tissue-specific splice forms, including automated protein isoform sequence and domain prediction, showing how our data can provide valuable insights into gene function in different tissues. For example, we have discovered a novel kidney-specific alternative splice form of the WNK1 gene, which appears to specifically disrupt its N-terminal kinase domain and may play a role in PHAII hypertension. Our database greatly expands knowledge of tissue-specific alternative splicing and provides a comprehensive dataset for investigating its functional roles and regulation in different human tissues.     

Clustered coding variants in the glutamate receptor complexes of individuals with schizophrenia and bipolar disorder

Current models of schizophrenia and bipolar disorder implicate multiple genes, however their biological relationships remain elusive. To test the genetic role of glutamate receptors and their interacting scaffold proteins, the exons of ten glutamatergic 'hub' genes in 1304 individuals were re-sequenced in case and control samples. No significant difference in the overall number of non-synonymous single nucleotide polymorphisms (nsSNPs) was observed between cases and controls. However, cluster analysis of nsSNPs identified two exons encoding the cysteine-rich domain and first transmembrane helix of GRM1 as a risk locus with five mutations highly enriched within these domains. A new splice variant lacking the transmembrane GPCR domain of GRM1 was discovered in the human brain and the GRM1 mutation cluster could perturb the regulation of this variant. The predicted effect on individuals harbouring multiple mutations distributed in their ten hub genes was also examined. Diseased individuals possessed an increased load of deleteriousness from multiple concurrent rare and common coding variants. Together, these data suggest a disease model in which the interplay of compound genetic coding variants, distributed among glutamate receptors and their interacting proteins, contribute to the pathogenesis of schizophrenia and bipolar disorders.     

Comparison of protein expression lists from mass spectrometry of human blood fluids using exact peptide sequences versus BLAST

The proteins in blood were all first expressed as mRNAs from genes within cells. There are databases of human proteins that are known to be expressed as mRNA in human cells and tissues. Proteins identified from human blood by the correlation of mass spectra that fail to match human mRNA expression products may not be correct. We compared the proteins identified in human blood by mass spectrometry by 10 different groups by correlation to human and nonhuman nucleic acid sequences. We determined whether the peptides or proteins identified by the different groups mapped to the human known proteins of the Reference Sequence (RefSeq) database. We used Structured Query Language data base searches of the peptide sequences correlated to tandem mass spectrometry spectra and basic local alignment search tool analysis of the identified full length proteins to control for correlation to the wrong peptide sequence or the existence of the same or very similar peptide sequence shared by more than one protein. Mass spectra were correlated against large protein data bases that contain many sequences that may not be expressed in human beings yet the search returned a very high percentage of peptides or proteins that are known to be found in humans. Only about 5% of proteins mapped to hypothetical sequences, which is in agreement with the reported false-positive rate of searching algorithms conditions. The results were highly enriched in secreted and soluble proteins and diminished in insoluble or membrane proteins. Most of the proteins identified were relatively short and showed a similar size distribution compared to the RefSeq database. At least three groups agree on a nonredundant set of 1671 types of proteins and a nonredundant set of 3151 proteins were identified by at least three peptides.