Deep analysis of cellular transcriptomes - LongSAGE versus classic MPSS

Genome-wide survey and expression analysis of the amino acid transporter superfamily in potato (Solanum tuberosum L.)

Amino acid transporters (AATs) are integral membrane proteins responsible for the transmembrane transport of amino acids and play important roles in various physiological processes of plants. However, there has not yet been a genome-wide overview of the StAAT gene family to date and only StAAP1 has been previously studied in potato. In this paper, a total of 72 StAATs were identified using a series of bioinformatics searches and classified into 12 subfamilies based on their phylogenetic relationship with known Arabidopsis and rice AATs. Chromosomal localization revealed their distribution on all 12 chromosomes. Nearly one-third of StAAT genes (23 of 72) were derived from gene duplication, among which tandem duplication made the greatest contribution to the expansion of the StAAT family. Motif analysis showed that the same subfamily had similar conserved motifs in both numbers and varieties. Moreover, high-throughput sequencing data was used to analyze the expression patterns of StAAT genes and was verified by quantitative real-time RT-PCR. The expression of StAAT genes exhibited both abundant and tissue-specific expression patterns, which might be connected to their functional roles in long- and short-distance transport. This study provided a comprehensive survey of the StAAT gene family, and could serve as a theoretical foundation for the further functional identification and utilization of family members.

Digital gene expression approach over multiple RNA-Seq data sets to detect neoblast transcriptional changes in Schmidtea mediterranea

BACKGROUND

The freshwater planarian Schmidtea mediterranea is recognised as a valuable model for research into adult stem cells and regeneration. With the advent of the high-throughput sequencing technologies, it has become feasible to undertake detailed transcriptional analysis of its unique stem cell population, the neoblasts. Nonetheless, a reliable reference for this type of studies is still lacking.

RESULTS

Taking advantage of digital gene expression (DGE) sequencing technology we compare all the available transcriptomes for S. mediterranea and improve their annotation. These results are accessible via web for the community of researchers. Using the quantitative nature of DGE, we describe the transcriptional profile of neoblasts and present 42 new neoblast genes, including several cancer-related genes and transcription factors. Furthermore, we describe in detail the Smed-meis-like gene and the three Nuclear Factor Y subunits Smed-nf-YA, Smed-nf-YB-2 and Smed-nf-YC.

CONCLUSIONS

DGE is a valuable tool for gene discovery, quantification and annotation. The application of DGE in S. mediterranea confirms the planarian stem cells or neoblasts as a complex population of pluripotent and multipotent cells regulated by a mixture of transcription factors and cancer-related genes.

A hemocyte gene expression signature correlated with predictive capacity of oysters to survive Vibrio infections

Background

The complex balance between environmental and host factors is an important determinant of susceptibility to infection. Disturbances of this equilibrium may result in multifactorial diseases as illustrated by the summer mortality syndrome, a worldwide and complex phenomenon that affects the oysters, Crassostrea gigas. The summer mortality syndrome reveals a physiological intolerance making this oyster species susceptible to diseases. Exploration of genetic basis governing the oyster resistance or susceptibility to infections is thus a major goal for understanding field mortality events. In this context, we used high-throughput genomic approaches to identify genetic traits that may characterize inherent survival capacities in C. gigas.

Results

Using digital gene expression (DGE), we analyzed the transcriptomes of hemocytes (immunocompetent cells) of oysters able or not able to survive infections by Vibrio species shown to be involved in summer mortalities. Hemocytes were nonlethally collected from oysters before Vibrio experimental infection, and two DGE libraries were generated from individuals that survived or did not survive. Exploration of DGE data and microfluidic qPCR analyses at individual level showed an extraordinary polymorphism in gene expressions, but also a set of hemocyte-expressed genes whose basal mRNA levels discriminate oyster capacity to survive infections by the pathogenic V. splendidus LGP32. Finally, we identified a signature of 14 genes that predicted oyster survival capacity. Their expressions are likely driven by distinct transcriptional regulation processes associated or not associated to gene copy number variation (CNV).

Conclusions

We provide here for the first time in oyster a gene expression survival signature that represents a useful tool for understanding mortality events and for assessing genetic traits of interest for disease resistance selection programs.

The phytocyanin gene family in rice (Oryza sativa L.): genome-wide identification, classification and transcriptional analysis

BACKGROUND

Phytocyanins (PCs) are plant-specific blue copper proteins involved in electron transport, and a large number of known PCs are considered to be chimeric arabinogalactan proteins (AGPs). To date there has not been a genome-wide overview of the OsPC gene family. Therefore, as the first step and a useful strategy to elucidate the functions of OsPCs, there is an urgent need for a thorough genome-wide analysis of this gene family.

METHODOLOGY/PRINCIPAL FINDINGS

In this study, a total of 62 OsPC genes were identified through a comprehensive bioinformatics analysis of the rice (Oryza sativa L.) genome. Based on phylogeny and motif constitution, the family of OsPCs was classified into three subclasses: uclacyanin-like proteins (OsUCLs), stellacyanin-like proteins (OsSCLs) and early nodulin-like proteins (OsENODLs). Structure and glycosylation prediction indicated that 46 OsPCs were glycosylphosphatigylinositol-anchored proteins and 38 OsPCs were chimeric AGPs. Gene duplication analysis revealed that chromosomal segment and tandem duplications contributed almost equally to the expansion of this gene family, and duplication events were mostly happened in the OsUCL subfamily. The expression profiles of OsPC genes were analyzed at different stages of vegetative and reproductive development and under abiotic stresses. It revealed that a large number of OsPC genes were abundantly expressed in the various stages of development. Moreover, 17 genes were regulated under the treatments of abiotic stresses.

CONCLUSIONS/SIGNIFICANCE

The genome-wide identification and expression analysis of OsPC genes should facilitate research in this gene family and give new insights toward elucidating their functions in higher plants.

Linking genes to diseases: it's all in the data

Genome-wide association analyses on large patient cohorts are generating large sets of candidate disease genes. This is coupled with the availability of ever-increasing genomic databases and a rapidly expanding repository of biomedical literature. Computational approaches to disease-gene association attempt to harness these data sources to identify the most likely disease gene candidates for further empirical analysis by translational researchers, resulting in efficient identification of genes of diagnostic, prognostic and therapeutic value. Existing computational methods analyze gene structure and sequence, functional annotation of candidate genes, characteristics of known disease genes, gene regulatory networks, protein-protein interactions, data from animal models and disease phenotype. To date, a few studies have successfully applied computational analysis of clinical phenotype data for specific diseases and shown genetic associations. In the near future, computational strategies will be facilitated by improved integration of clinical and computational research, and by increased availability of clinical phenotype data in a format accessible to computational approaches.

Quantification of the yeast transcriptome by single-molecule sequencing

We present single-molecule sequencing digital gene expression (smsDGE), a high-throughput, amplification-free method for accurate quantification of the full range of cellular polyadenylated RNA transcripts using a Helicos Genetic Analysis system. smsDGE involves a reverse-transcription and polyA-tailing sample preparation procedure followed by sequencing that generates a single read per transcript. We applied smsDGE to the transcriptome of Saccharomyces cerevisiae strain DBY746, using 6 of the available 50 channels in a single sequencing run, yielding on average 12 million aligned reads per channel. Using spiked-in RNA, accurate quantitative measurements were obtained over four orders of magnitude. High correlation was demonstrated across independent flow-cell channels, instrument runs and sample preparations. Transcript counting in smsDGE is highly efficient due to the representation of each transcript molecule by a single read. This efficiency, coupled with the high throughput enabled by the single-molecule sequencing platform, provides an alternative method for expression profiling.

MicroRNA profiling and head and neck cancer

Head and neck/oral cancer (HNOC) is a devastating disease. Despite advances in diagnosis and treatment, mortality rates have not improved significantly over the past three decades. Improvement in patient survival requires a better understanding of the disease progression so that HNOC can be detected early in the disease process and targeted therapeutic interventions can be deployed. Accumulating evidence suggests that microRNAs play important roles in many human cancers. They are pivotal regulators of diverse cellular processes including proliferation, differentiation, apoptosis, survival, motility, and morphogenesis. MicroRNA expression patterns may become powerful biomarkers for diagnosis and prognosis of HNOC. In addition, microRNA therapy could be a novel strategy for HNOC prevention and therapeutics. Recent advances in microRNA expression profiling have led to a better understanding of the cancer pathogenesis. In this review, we will survey recent technological advances in microRNA profiling and their applications in defining microRNA markers/targets for cancer prediction, diagnostics, treatment, and prognostics. MicroRNA alterations that consistently identified in HNOC will be discussed, such as upregulation of miR-21, miR-31, miR-155, and downregulation of miR-26b, miR-107, miR-133b, miR-138, and miR-139.

Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays

Background

High-throughput cDNA synthesis and sequencing of poly(A)-enriched RNA is rapidly emerging as a technology competing to replace microarrays as a quantitative platform for measuring gene expression.

Results

Consequently, we compared full length cDNA sequencing to 2-channel gene expression microarrays in the context of measuring differential gene expression. Because of its comparable cost to a gene expression microarray, our study focused on the data obtainable from a single lane of an Illumina 1 G sequencer. We compared sequencing data to a highly replicated microarray experiment profiling two divergent strains of S. cerevisiae.

Conclusion

Using a large number of quantitative PCR (qPCR) assays, more than previous studies, we found that neither technology is decisively better at measuring differential gene expression. Further, we report sequencing results from a diploid hybrid of two strains of S. cerevisiae that indicate full length cDNA sequencing can discover heterozygosity and measure quantitative allele-specific expression simultaneously.

Applications of ultra-high-throughput sequencing

The genomics era has enabled scientists to more readily pose truly global questions regarding mutation, evolution, gene and genome structure, function, and regulation. Just as Sanger sequencing ushered in a paradigm shift that enabled the molecular basis of biological questions to be directly addressed, to an even greater degree, ultra-high-throughput DNA sequencing is poised to dramatically change the nature of biological research. New sequencing technologies have opened the door for novel questions to be addressed at the level of the entire genome in the areas of comparative genomics, systems biology, metagenomics, and genome biology. These new sequencing technologies provide a tremendous amount of DNA sequence data to be collected at an astounding pace, with reduced costs, effort, and time as compared to Sanger sequencing. Applications of ultra-high-throughput sequencing (UHTS) are essentially limited only by the imaginations of researchers, and include genome sequencing/resequencing, small RNA discovery, deep SNP discovery, chromatin immunoprecipitation (ChIP) and RNA immunoprecipitation (RIP) coupled with sequence identification, transcriptome analysis including empirical annotation, discovery and characterization of alternative splicing, and gene expression profiling. This technology will have a profound impact on plant breeding, biotechnology, and our fundamental understanding of plant evolution, development, and environmental responses. In this chapter, we provide an overview of UHTS approaches and their applications. We also describe a protocol we have developed for deep sequencing of plant transcriptomes using the Illumina/Solexa sequencing platform.

A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome

Background

"Open" transcriptome analysis methods allow to study gene expression without a priori knowledge of the transcript sequences. As of now, SAGE (Serial Analysis of Gene Expression), LongSAGE and MPSS (Massively Parallel Signature Sequencing) are the mostly used methods for "open" transcriptome analysis. Both LongSAGE and MPSS rely on the isolation of 21 pb tag sequences from each transcript. In contrast to LongSAGE, the high throughput sequencing method used in MPSS enables the rapid sequencing of very large libraries containing several millions of tags, allowing deep transcriptome analysis. However, a bias in the complexity of the transcriptome representation obtained by MPSS was recently uncovered.

Results

In order to make a deep analysis of mouse hypothalamus transcriptome avoiding the limitation introduced by MPSS, we combined LongSAGE with the Solexa sequencing technology and obtained a library of more than 11 millions of tags. We then compared it to a LongSAGE library of mouse hypothalamus sequenced with the Sanger method.

Conclusion

We found that Solexa sequencing technology combined with LongSAGE is perfectly suited for deep transcriptome analysis. In contrast to MPSS, it gives a complex representation of transcriptome as reliable as a LongSAGE library sequenced by the Sanger method.