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Symbiotic nitrogen fixation (SNF) in root nodules of grain legumes such as chickpea is a highly complex process that drastically affects the gene expression patterns of both the prokaryotic as well as eukaryotic interacting cells. A successfully established symbiotic relationship requires mutual signaling mechanisms and a continuous adaptation of the metabolism of the involved cells to varying environmental conditions. Although some of these processes are well understood today many of the molecular mechanisms underlying SNF, especially in chickpea, remain unclear. Here, we reannotated our previously published transcriptome data generated by deepSuperSAGE (Serial Analysis of Gene Expression) to the recently published draft genome of chickpea to assess the root- and nodule-specific transcriptomes of the eukaryotic host cells. The identified gene expression patterns comprise up to 71 significantly differentially expressed genes and the expression of twenty of these was validated by quantitative real-time PCR with the tissues from five independent biological replicates. Many of the differentially expressed transcripts were found to encode proteins implicated in sugar metabolism, antioxidant defense as well as biotic and abiotic stress responses of the host cells, and some of them were already known to contribute to SNF in other legumes. The differentially expressed genes identified in this study represent candidates that can be used for further characterization of the complex molecular mechanisms underlying SNF in chickpea.
Drought and salt stress are the major constraint to increase yield in chickpea (Cicer arietinum). Improving drought and high-salinity tolerance is therefore of outmost importance for breeding. However, the complexity of these traits 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, which 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, generating genome-wide, high-quality transcription profiles from any eukaryote, has been employed in the present study. The method produces 26bp long fragments (26bp tags) from defined positions in cDNAs, providing sufficient sequence information to unambiguously characterize the mRNAs. Further, SuperSAGE tags may be immediately used to produce microarrays and probes for real-time-PCR, thereby overcoming the lack of genomic tools in non-model organisms.