Transcriptomic analysis of growth heterosis in larval Pacific oysters (Crassostrea gigas).

Compared with understanding of biological shape and form, knowledge is sparse regarding what regulates growth and body size of a species. For example, the genetic and physiological causes of heterosis (hybrid vigor) have remained elusive for nearly a century. Here, we investigate gene-expression patterns underlying growth heterosis in the Pacific oyster (Crassostrea gigas) in two partially inbred (f = 0.375) and two hybrid larval populations produced by a reciprocal cross between the two inbred families. We cloned cDNA and generated 4.5 M sequence tags with massively parallel signature sequencing. The sequences contain 23,274 distinct signatures that are expressed at statistically nonzero levels and show a highly positively skewed distribution with median and modal counts of 9.25 million and 3 transcripts per million, respectively. For nearly half of these signatures, expression level depends on genotype and is predominantly nonadditive (hybrids deviate from the inbred average). Statistical contrasts suggest approximately 350 candidate genes for growth heterosis that exhibit concordant nonadditive expression in reciprocal hybrids; this represents only approximately 1.5% of the >20,000 transcripts. Patterns of gene expression, which include dominance for low expression and even underdominance of expression, are more complex than predicted from classical dominant or overdominant explanations of heterosis. Preliminary identification of ribosomal proteins among candidate genes supports the suggestion from previous studies that efficiency of protein metabolism plays a role in growth heterosis.

Massively parallel signature sequencing.

Massively parallel signature sequencing is an ultra-high throughput sequencing technology. It can simultaneously sequence millions of sequence tags, and, therefore, is ideal for whole genome analysis. When applied to expression profiling, it reveals almost every transcript in the sample and provides its accurate expression level. This chapter describes the technology and its application in establishing stem cell transcriptome databases.

The use of MPSS for whole-genome transcriptional analysis in Arabidopsis.

We have generated 36,991,173 17-base sequence "signatures" representing transcripts from the model plant Arabidopsis. These data were derived by massively parallel signature sequencing (MPSS) from 14 libraries and comprised 268,132 distinct sequences. Comparable data were also obtained with 20-base signatures. We developed a method for handling these data and for comparing these signatures to the annotated Arabidopsis genome. As part of this procedure, 858,019 potential or "genomic" signatures were extracted from the Arabidopsis genome and classified based on the position and orientation of the signatures relative to annotated genes. A comparison of genomic and expressed signatures matched 67,735 signatures predicted to be derived from distinct transcripts and expressed at significant levels. Expressed signatures were derived from the sense strand of at least 19,088 of 29,084 annotated genes. A comparison of the genomic and expression signatures demonstrated that approximately 7.7% of genomic signatures were underrepresented in the expression data. These genomic signatures contained one of 20 four-base words that were consistently associated with reduced MPSS abundances. More than 89% of the sum of the expressed signature abundances matched the Arabidopsis genome, and many of the unmatched signatures found in high abundances were predicted to match to previously uncharacterized transcripts.