BackgroundCommon bean (Phaseolus vulgaris) is the most important food legume in the world. Although this crop is very important to both the developed and developing world as a means of dietary protein supply, resources available in common bean are limited. Global transcriptome analysis is important to better understand gene expression, genetic variation, and gene structure annotation in addition to other important features. However, the number and description of common bean sequences are very limited, which greatly inhibits genome and transcriptome research. Here we used 454 pyrosequencing to obtain a substantial transcriptome dataset for common bean.ResultsWe obtained 1,692,972 reads with an average read length of 207 nucleotides (nt). These reads were assembled into 59,295 unigenes including 39,572 contigs and 19,723 singletons, in addition to 35,328 singletons less than 100 bp. Comparing the unigenes to common bean ESTs deposited in GenBank, we found that 53.40% or 31,664 of these unigenes had no matches to this dataset and can be considered as new common bean transcripts. Functional annotation of the unigenes carried out by Gene Ontology assignments from hits to Arabidopsis and soybean indicated coverage of a broad range of GO categories. The common bean unigenes were also compared to the bean bacterial artificial chromosome (BAC) end sequences, and a total of 21% of the unigenes (12,724) including 9,199 contigs and 3,256 singletons match to the 8,823 BAC-end sequences. In addition, a large number of simple sequence repeats (SSRs) and transcription factors were also identified in this study.ConclusionsThis work provides the first large scale identification of the common bean transcriptome derived by 454 pyrosequencing. This research has resulted in a 150% increase in the number of Phaseolus vulgaris ESTs. The dataset obtained through this analysis will provide a platform for functional genomics in common bean and related legumes and will aid in the development of molecular markers that can be used for tagging genes of interest. Additionally, these sequences will provide a means for better annotation of the on-going common bean whole genome sequencing.
Peanut (Arachis hypogaea L.) is one of the major oil crops and is the fifth largest source of plant oils in the world. Numerous genes participate in regulating the biosynthesis and accumulation of the storage lipids in seeds or other reservoir organs, among which several transcription factors, such as LEAFY COTYLEDON1 (AtLEC1), LEC2, and WRINKLED1 (WRI1), involved in embryo development also control the lipid reservoir in seeds. In this study, the AtLEC1 gene was transferred into the peanut genome and expressed in a seed-specific manner driven by the NapinA full-length promoter or its truncated 230-bp promoter. Four homozygous transgenic lines, two lines with the longer promoter and the other two with the truncated one, were selected for further analysis. The AtLEC1 mRNA level and the corresponding protein accumulation in different transgenic overexpression lines were altered, and the transgenic plants grew and developed normally without any detrimental effects on major agronomic traits. In the developing seeds of transgenic peanuts, the mRNA levels of a series of genes were upregulated. These genes are associated with fatty acid (FA) biosynthesis and lipid accumulation. The former set of genes included the homomeric ACCase A (AhACC II), the BC subunit of heteromeric ACCase (AhBC4), ketoacyl-ACP synthetase (AhKAS II), and stearoyl-ACP desaturase (AhSAD), while the latter ones were the diacylglycerol acyltransferases and oleosins (AhDGAT1, AhDGAT2, AhOle1, AhOle2, and AhOle3). The oil content and seed weight increased by 4.42–15.89% and 11.1–22.2%, respectively, and the levels of major FA components including stearic acid, oleic acid, and linoleic acid changed significantly in all different lines.
BackgroundCommon bean (Phaseolus vulgaris L.) is one of the most important legumes in the world. Several diseases severely reduce bean production and quality; therefore, it is very important to better understand disease resistance in common bean in order to prevent these losses. More than 70 resistance (R) genes which confer resistance against various pathogens have been cloned from diverse plant species. Most R genes share highly conserved domains which facilitates the identification of new candidate R genes from the same species or other species. The goals of this study were to isolate expressed R gene-like sequences (RGLs) from 454-derived transcriptomic sequences and expressed sequence tags (ESTs) of common bean, and to develop RGL-tagged molecular markers.ResultsA data-mining approach was used to identify tentative P. vulgaris R gene-like sequences from approximately 1.69 million 454-derived sequences and 116,716 ESTs deposited in GenBank. A total of 365 non-redundant sequences were identified and named as common bean (P. vulgaris = Pv) resistance gene-like sequences (PvRGLs). Among the identified PvRGLs, about 60% (218 PvRGLs) were from 454-derived sequences. Reverse transcriptase-polymerase chain reaction (RT-PCR) analysis confirmed that PvRGLs were actually expressed in the leaves of common bean. Upon comparison to P. vulgaris genomic sequences, 105 (28.77%) of the 365 tentative PvRGLs could be integrated into the existing common bean physical map. Based on the syntenic blocks between common bean and soybean, 237 (64.93%) PvRGLs were anchored on the P. vulgaris genetic map and will need to be mapped to determine order. In addition, 11 sequence-tagged-site (STS) and 19 cleaved amplified polymorphic sequence (CAPS) molecular markers were developed for 25 unique PvRGLs.ConclusionsIn total, 365 PvRGLs were successfully identified from 454-derived transcriptomic sequences and ESTs available in GenBank and about 65% of PvRGLs were integrated into the common bean genetic map. A total of 30 RGL-tagged markers were developed for 25 unique PvRGLs, including 11 STS and 19 CAPS markers. The expressed PvRGLs identified in this study provide a large sequence resource for development of RGL-tagged markers that could be used further for genetic mapping of disease resistant candidate genes and quantitative trait locus/loci (QTLs). This work also represents an additional method for identifying expressed RGLs from next generation sequencing data.
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