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High-throughput SuperSAGE for digital gene expression analysis of multiple samples using Next Generation Sequencing

ABSTRACT: We established a protocol of the SuperSAGE technology combined with next-generation sequencing, coined “High-Throughput (HT-) SuperSAGE”. SuperSAGE is a method of digital gene expression profiling that allows isolation of 26-bp tag fragments from expressed transcripts. In the present protocol, index (barcode) sequences are employed to discriminate tags from different samples. Such barcodes permit to enable researchers to analyze digital tags from many transcriptomes of many samples in a single sequencing run by simply pooling the libraries. Here, we demonstrated that HT-SuperSAGE provided highly sensitive, reproducible and accurate digital gene expression data. By increasing throughput for analysis in HT-SuperSAGE, various applications were expected and several examples of its applications were introduced in the present study, including analyses of laser-microdissected cells, biological replicates or tag extraction using different anchoring enzymes.

ORGANISM(S): Arabidopsis thaliana Oryza sativa Danio rerio

PROVIDER: GSE20682 | GEO | 2010/10/01

SECONDARY ACCESSION(S): PRJNA124943

REPOSITORIES: GEO

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Project description:Background Drought is the major constraint to increase yield in chickpea (Cicer arietinum). Improving drought tolerance is therefore of outmost importance for breeding. However, the complexity of the trait allowed only marginal progress. A solution to the current stagnation is expected from innovative molecular tools such as transcriptome analyses providing insight into stress-related gene activity and, combined with molecular markers and expression (e)QTL mapping, may accelerate knowledge-based breeding. SuperSAGE, an improved version of the serial analysis of gene expression (SAGE) technique, currently is the most advanced tool for transcriptome analysis. SuperSAGE generates genome-wide, high-quality transcription profiles from any eukaryote. The method produces 26bp long fragments (SuperTags26bp tags) from defined positions in cDNAs, providing sufficient sequence information to unambiguously characterize the mRNAs. Further, SuperSAGE Tags may be immediately used to produce so called SuperTag microarrays and probes for real-time-PCR thereby overcoming the lack of genomic tools in non-model organisms. Results We applied SuperSAGE to the analysis of gene expression in chickpea roots in response to drought. To this end, we sequenced 80,238 26bp SuperTtags representing 17,493 unique transcripts (UniTags) from drought-stressed and non-stressed control roots. A total of 7,532 (43%) UniTags were more than 2.7-fold differentially expressed , and 880 (5.0%) were regulated more than 8-fold upon stress. Their large size enabled the unambiguous annotation of 2,798 (16.3%) UniTags to genes or proteins in public data bases and thus to stress-response processes. We designed a microSuperTag array carrying 3,000 of these SuperTags26bp tags. The chip data confirmed the SuperSAGE results in 79 % of cases whereas RT-PCR confirmed the SuperSAGE data in all cases. Conclusions This study represents the most comprehensive analysis of the drought-response transcriptome of chickpea available to date. It demonstrates that - inter alias - signal transduction, transcription regulation, osmolyte accumulation, chromosome organization, and ROS scavenging undergo strong transcriptional remodelling in chickpea roots already 6h after drought stress. Certain transcript isoforms characterizing these processes are potential targets for breeding for drought tolerance. We demonstrate that these can be easily accessed by micro-arrays and RT-PCR assays readily produced downstream of SuperSAGE. Our study proves that SuperSAGE has a good potential is best suited for molecular breeding also in non-model crops.

Project description:Summary: Multi-toxins Bt-crops carrying insecticidal toxins with similar host spectrum and different mode of action e.g. Cry and Vip, are expected to improve resistance management in target pests. Control failure has been informed for Cry toxins but not for Vip3A, of which no mechanism of resistance has yet been identified. Here we applied HT-SuperSAGE to analyze the transcriptome profiling in the midgut tissue of a tobacco budworm Heliothis virescens (F.) strain laboratory-selected for Vip3A resistance. A total of 1324252 26-bp tags were sequenced representing 17751 unique transcripts (UniTags) from genetically similar Vip3A-resistant (Vip-Sel) strain and susceptible control (Vip-Unsel) strain. Differential expression was found significant (≥ 2.5-fold or ≤ 0.4) for 1845 unigenes that constitute 10.4% of the total number of UniTags, where 277 represented overexpressed (OE) and 1568 underexpressed (UE) genes in Vip-Sel compared to Vip-Unsel. BLASTN searches mapped 1141 of these UniTags to H. virescens EST sequences, of which, 816 (143 OE and 673 UE) were unambiguously annotated to proteins in NCBI non-redundant protein databases. Gene ontology revealed Vip3A adaptation induced major constitutive transcriptional differences in serine-proteases (SP)-mediated proteolysis, ribosome biogenesis and metabolic processes. Several unigenes homologous to a particular member of the REsponse to PAThogen (REPAT) family were found to be predominantly OE. Since UniTags related to SP and ribosomal proteins (RP) were the most represented in the libraries, they were further analyzed in details. Interestingly, UniTags related to the putative Vip3A-binding protein RpS2 were underexpressed, while, the tumorigenesis suppressor RpL37 accounted for the 35% of total overexpressed RP. A subset of unigenes was chosen to confirm the HT-SuperSAGE data by qRT-PCR. The present study is the first providing a lepidopteran gut transcriptome associated with Vip3A resistance and a foundation for future attempts to elucidate the resistance mechanism. Methods: Midgut transcriptional profiles of third-instar larvae from genetically similar Vip3A-resistant (Vip-Sel) and susceptible control (Vip-Unsel) strains were generated by HT-SuperSAGE. RNA pools of Vip-Sel and Vip-Unsel samples (five RNA preparations each) were prepared and used for the construction of SuperSAGE libraries HvR_GCCT and HvS_GCAC, respectively, according to the procedure described by [Matsumura et al., 2010]. Purified PCR products were mixed and applied to Illumina Genome Analyzer II sequencing with GEX (DpnII) primer in the sequencing reactions as recommended by the manufacturer. Sorting of sequence reads based on index sequences and the subsequent extraction of sequence tags from reads was conducted using a script written in Perl. Fold-change for each tag was calculated as in [Gilardoni et al., 2010]. qRT–PCR validation was performed using TaqMan and SYBR Green assays.