Orchids display unique phenotypes, functional characteristics and ecological adaptations that are not found in model plants. In this study, we aimed to characterize the microRNA (miRNA) transcriptome and identify species- and tissue-specific miRNAs in Phalaenopsis aphrodite. After data filtering and cleanup, a total of 59,387,374 reads, representing 1,649,996 unique reads, were obtained from four P. aphrodite small RNA libraries. A systematic bioinformatics analysis pipeline was developed that can be used for miRNA and precursor mining, and target gene prediction in non-model plants. A total of 3,251 unique reads for 181 known plant miRNAs (belonging to 88 miRNA families), 23 new miRNAs and 91 precursors were identified. All the miRNA star sequences (miRNA*), the complementary strands of miRNA that from miRNA/miRNA* duplexes, of the predicted new miRNAs were detected in our small RNA libraries, providing additional evidence for their existence as new miRNAs in P. aphrodite. Furthermore, 240 potential miRNA-targets that appear to be involved in many different biological activities and molecular functions, especially transcription factors, were identified, suggesting that miRNAs can impact multiple processes in P. aphrodite. We also verified the cleavage sites for six targets using RNA ligase-mediated rapid amplification of 5 ends assay. The results provide valuable information about the composition, expression and function of miRNA in P. aphrodite, and will aid functional genomics studies of orchids.
A specialized orchid database, named Orchidstra (URL: http://orchidstra.abrc.sinica.edu.tw), has been constructed to collect, annotate and share genomic information for orchid functional genomics studies. The Orchidaceae is a large family of Angiosperms that exhibits extraordinary biodiversity in terms of both the number of species and their distribution worldwide. Orchids exhibit many unique biological features; however, investigation of these traits is currently constrained due to the limited availability of genomic information. Transcriptome information for five orchid species and one commercial hybrid has been included in the Orchidstra database. Altogether, these comprise >380,000 non-redundant orchid transcript sequences, of which >110,000 are protein-coding genes. Sequences from the transcriptome shotgun assembly (TSA) were obtained either from output reads from next-generation sequencing technologies assembled into contigs, or from conventional cDNA library approaches. An annotation pipeline using Gene Ontology, KEGG and Pfam was built to assign gene descriptions and functional annotation to protein-coding genes. Deep sequencing of small RNA was also performed for Phalaenopsis aphrodite to search for microRNAs (miRNAs), extending the information archived for this species to miRNA annotation, precursors and putative target genes. The P. aphrodite transcriptome information was further used to design probes for an oligonucleotide microarray, and expression profiling analysis was carried out. The intensities of hybridized probes derived from microarray assays of various tissues were incorporated into the database as part of the functional evidence. In the future, the content of the Orchidstra database will be expanded with transcriptome data and genomic information from more orchid species.
Previously we developed genomic resources for orchids, including transcriptomic analyses using next-generation sequencing techniques and construction of a web-based orchid genomic database. Here, we report a modified molecular model of flower development in the Orchidaceae based on functional analysis of gene expression profiles in Phalaenopsis aphrodite (a moth orchid) that revealed novel roles for the transcription factors involved in floral organ pattern formation. Phalaenopsis orchid floral organ-specific genes were identified by microarray analysis. Several critical transcription factors including AP3, PI, AP1 and AGL6, displayed distinct spatial distribution patterns. Phylogenetic analysis of orchid MADS box genes was conducted to infer the evolutionary relationship among floral organ-specific genes. The results suggest that gene duplication MADS box genes in orchid may have resulted in their gaining novel functions during evolution. Based on these analyses, a modified model of orchid flowering was proposed. Comparison of the expression profiles of flowers of a peloric mutant and wild-type Phalaenopsis orchid further identified genes associated with lip morphology and peloric effects. Large scale investigation of gene expression profiles revealed that homeotic genes from the ABCDE model of flower development classes A and B in the Phalaenopsis orchid have novel functions due to evolutionary diversification, and display differential expression patterns.
Being one of the largest families in the angiosperms, Orchidaceae display a great biodiversity resulting from adaptation to diverse habitats. Genomic information on orchids is rather limited, despite their unique and interesting biological features, thus impeding advanced molecular research. Here we report a strategy to integrate sequence outputs of the moth orchid, Phalaenopsis aphrodite, from two high-throughput sequencing platform technologies, Roche 454 and Illumina/Solexa, in order to maximize assembly efficiency. Tissues collected for cDNA library preparation included a wide range of vegetative and reproductive tissues. We also designed an effective workflow for annotation and functional analysis. After assembly and trimming processes, 233,823 unique sequences were obtained. Among them, 42,590 contigs averaging 875 bp in length were annotated to protein-coding genes, of which 7,263 coding genes were found to be nearly full length. The sequence accuracy of the assembled contigs was validated to be as high as 99.9%. Genes with tissue-specific expression were also categorized by profiling analysis with RNA-Seq. Gene products targeted to specific subcellular localizations were identified by their annotations. We concluded that, with proper assembly to combine outputs of next-generation sequencing platforms, transcriptome information can be enriched in gene discovery, functional annotation and expression profiling of a non-model organism.
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