The human salivary RNA transcriptome revealed by massively parallel sequencing.

BACKGROUND: Evaluation of the salivary transcriptome is an emerging diagnostic technology with discriminatory power for disease detection. This study explored massively parallel sequencing for providing nucleotide-level sequence information for each RNA in saliva. METHODS: Transcriptome profiling with the SOLiD™ system was applied to RNA isolated from unstimulated cell-free saliva (CFS) and whole saliva (WS) from healthy human volunteers. Sequenced reads were aligned to human genome build 18 and the Human Oral Microbiome Database (HOMD). RESULTS: Massively parallel sequencing enabled the acquisition of complete sequence information for each nucleotide position of the human salivary transcriptome through alignment to multiple sequence databases. Approximately 20%-25% of the sequenced reads from CFS aligned to the human genome, and approximately 30% of the sequenced reads aligned to the HOMD. We detected the expression of >4000 coding and noncoding genes in CFS and WS. Gene expression at different genome loci showed that the structural integrity of the transcripts for the annotated genes was preserved in saliva. CONCLUSIONS: A single measurement provided RNA sequence information of gene transcript abundance for both coding and noncoding RNAs and identified sequences from >400 different microbial species within a single sample. Contrary to previous data suggesting that salivary RNA is highly fragmented, in our study the structural integrity of RNA was preserved. The high degree of sequence alignment to annotated exons and introns for each of the respective reference genomes, with sequence coverage spanning the full length of the genes, provides strong evidence that the salivary transcriptome is a complex RNA network.

Microarray analysis reveals differences in gene expression of circulating CD8(+) T cells in melanoma patients and healthy donors.

Circulating T cells from many cancer patients are known to be dysfunctional and undergo spontaneous apoptosis. We used microarray technology to determine whether gene expression differences exist in T cells from melanoma patients versus healthy subjects, which may underlie these abnormalities. To maximize the resolution of our data, we sort purified CD8(+) subsets and amplified the extracted RNA for microarray analysis. These analyses show subtle but statistically significant expression differences for 10 genes in T cells from melanoma patients versus healthy controls, which were additionally confirmed by quantitative real-time PCR analysis. Whereas none of these genes are members of the classical apoptosis pathways, several may be linked to apoptosis. To additionally investigate the significance of these 10 genes, we combined them into a classifier and found that they provide a much better discrimination between melanoma and healthy T cells as compared with a classifier built uniquely with classical apoptosis-related genes. These results suggest the possible engagement of an alternative apoptosis pathway in circulating T cells from cancer patients.