Neuropeptides and peptide hormones identified in codling moth, Cydia pomonella (Lepidoptera: Tortricidae).

The codling moth, Cydia pomonella, is a worldwide pest of pome fruits. Neuropeptides regulate most physiological functions in insects and represent new targets for the development of control agents. The only neuropeptides reported from the codling moth to date are the allatostatin A family peptides. To identify other neuropeptides and peptide hormones from codling moth, we analyzed head transcriptomes, identified 50 transcripts, and predicted 120 prepropeptides for the codling moth neuropeptides and peptide hormones. All transcripts were amplified, and these sequences were verified. One of the notable findings in this study is that diapause hormones (DHs) reported from Tortricid moths, including the codling moth, do not have the WFGPRL sequence in C-terminal ends in the pban genes. The C-terminal motif is critical to characterize insect DH peptides, and always conserved in pban/dh genes in Lepidoptera and many insect orders. Interestingly, the WFGPRL sequence was produced only from the capa gene in the codling moth. The allatostatin A-family encoding transcript predicted nine peptides, seven of which, as expected, are identical to those previously isolated from the moth. We also identified new codling moth orthologs of insect neuropeptides including CCHamides, allatostatin CC, RYamides, and natalisins. The information provided in this study will benefit future codling moth investigations using peptidoproteomics to determine peptide presence and functions.

CisSERS: Customizable In Silico Sequence Evaluation for Restriction Sites.

High-throughput sequencing continues to produce an immense volume of information that is processed and assembled into mature sequence data. Data analysis tools are urgently needed that leverage the embedded DNA sequence polymorphisms and consequent changes to restriction sites or sequence motifs in a high-throughput manner to enable biological experimentation. CisSERS was developed as a standalone open source tool to analyze sequence datasets and provide biologists with individual or comparative genome organization information in terms of presence and frequency of patterns or motifs such as restriction enzymes. Predicted agarose gel visualization of the custom analyses results was also integrated to enhance the usefulness of the software. CisSERS offers several novel functionalities, such as handling of large and multiple datasets in parallel, multiple restriction enzyme site detection and custom motif detection features, which are seamlessly integrated with real time agarose gel visualization. Using a simple fasta-formatted file as input, CisSERS utilizes the REBASE enzyme database. Results from CisSERS enable the user to make decisions for designing genotyping by sequencing experiments, reduced representation sequencing, 3'UTR sequencing, and cleaved amplified polymorphic sequence (CAPS) molecular markers for large sample sets. CisSERS is a java based graphical user interface built around a perl backbone. Several of the applications of CisSERS including CAPS molecular marker development were successfully validated using wet-lab experimentation. Here, we present the tool CisSERS and results from in-silico and corresponding wet-lab analyses demonstrating that CisSERS is a technology platform solution that facilitates efficient data utilization in genomics and genetics studies.

Comparative analysis of predicted plastid-targeted proteomes of sequenced higher plant genomes.

Plastids are actively involved in numerous plant processes critical to growth, development and adaptation. They play a primary role in photosynthesis, pigment and monoterpene synthesis, gravity sensing, starch and fatty acid synthesis, as well as oil, and protein storage. We applied two complementary methods to analyze the recently published apple genome (Malus × domestica) to identify putative plastid-targeted proteins, the first using TargetP and the second using a custom workflow utilizing a set of predictive programs. Apple shares roughly 40% of its 10,492 putative plastid-targeted proteins with that of the Arabidopsis (Arabidopsis thaliana) plastid-targeted proteome as identified by the Chloroplast 2010 project and ∼57% of its entire proteome with Arabidopsis. This suggests that the plastid-targeted proteomes between apple and Arabidopsis are different, and interestingly alludes to the presence of differential targeting of homologs between the two species. Co-expression analysis of 2,224 genes encoding putative plastid-targeted apple proteins suggests that they play a role in plant developmental and intermediary metabolism. Further, an inter-specific comparison of Arabidopsis, Prunus persica (Peach), Malus × domestica (Apple), Populus trichocarpa (Black cottonwood), Fragaria vesca (Woodland Strawberry), Solanum lycopersicum (Tomato) and Vitis vinifera (Grapevine) also identified a large number of novel species-specific plastid-targeted proteins. This analysis also revealed the presence of alternatively targeted homologs across species. Two separate analyses revealed that a small subset of proteins, one representing 289 protein clusters and the other 737 unique protein sequences, are conserved between seven plastid-targeted angiosperm proteomes. Majority of the novel proteins were annotated to play roles in stress response, transport, catabolic processes, and cellular component organization. Our results suggest that the current state of knowledge regarding plastid biology, preferentially based on model systems is deficient. New plant genomes are expected to enable the identification of potentially new plastid-targeted proteins that will aid in studying novel roles of plastids.

Comparative analysis of virus-specific small RNA profiles of three biologically distinct strains of Potato virus Y in infected potato (Solanum tuberosum) cv. Russet Burbank.

Deep sequencing technology has enabled the analysis of small RNA profiles of virus-infected plants and could provide insights into virus-host interactions. Potato virus Y is an economically important viral pathogen of potato worldwide. In this study, we investigated the nature and relative levels of virus-derived small interfering RNAs (vsiRNAs) in potato cv. Russet Burbank infected with three biologically distinct and economically important strains of PVY, the ordinary strain (PVY-O), tobacco veinal-necrotic strain (PVY-N) and tuber necrotic strain (PVY-NTN). The analysis showed an overall abundance of vsiRNAs of 20-24nt in PVY-infected plants. Considerable differences were present in the distribution of vsiRNAs as well as total small RNAs. The 21nt class was the most prevalent in PVY-infected plants irrespective of the virus strain, whereas in healthy potato plants, the 24nt class was the most dominant. vsiRNAs were derived from every position in the PVY genome, though certain hotspots were identified for each of the PVY strains. Among the three strains used, the population of vsiRNAs of different size classes was relatively different with PVY-NTN accumulating the highest level of vsiRNAs, while PVY-N infected plants had the least population of vsiRNAs. Unique vsiRNAs mapping to PVY genome in PVY-infected plants amounted to 3.13, 1.93 and 1.70% for NTN, N and O, respectively. There was a bias in the generation of vsiRNAs from the plus strand of the genome in comparison to the negative strand. The highest number of total vsiRNAs was from the cytoplasmic inclusion protein gene (CI) in PVY-O and PVY-NTN strains, whereas from PVY-N, the NIb gene produced maximum total vsiRNAs. These findings indicate that the three PVY strains interact differently in the same host genetic background and provided insights into virus-host interactions in an important food crop.

Comparative genomics analysis in Prunoideae to identify biologically relevant polymorphisms.

Prunus is an economically important genus with a wide range of physiological and biological variability. Using the peach genome as a reference, sequencing reads from four almond accessions and one sweet cherry cultivar were used for comparative analysis of these three Prunus species. Reference mapping enabled the identification of many biological relevant polymorphisms within the individuals. Examining the depth of the polymorphisms and the overall scaffold coverage, we identified many potentially interesting regions including hundreds of small scaffolds with no coverage from any individual. Non-sense mutations account for about 70 000 of the 13 million identified single nucleotide polymorphisms (SNPs). Blast2GO analyses on these non-sense SNPs revealed several interesting results. First, non-sense SNPs were not evenly distributed across all gene ontology terms. Specifically, in comparison with peach, sweet cherry is found to have non-sense SNPs in two 1-aminocyclopropane-1-carboxylate synthase (ACS) genes and two 1-aminocyclopropane-1-carboxylate oxidase (ACO) genes. These polymorphisms may be at the root of the nonclimacteric ripening of sweet cherry. A set of candidate genes associated with bitterness in almond were identified by comparing sweet and bitter almond sequences. To the best of our knowledge, this is the first report in plants of non-sense SNP abundance in a genus being linked to specific GO terms.

Rapid gene-based SNP and haplotype marker development in non-model eukaryotes using 3'UTR sequencing.

BACKGROUND: Sweet cherry (Prunus avium L.), a non-model crop with narrow genetic diversity, is an important member of sub-family Amygdoloideae within Rosaceae. Compared to other important members like peach and apple, sweet cherry lacks in genetic and genomic information, impeding understanding of important biological processes and development of efficient breeding approaches. Availability of single nucleotide polymorphism (SNP)-based molecular markers can greatly benefit breeding efforts in such non-model species. RNA-seq approaches employing second generation sequencing platforms offer a unique avenue to rapidly identify gene-based SNPs. Additionally, haplotype markers can be rapidly generated from transcript-based SNPs since they have been found to be extremely utile in identification of genetic variants related to health, disease and response to environment as highlighted by the human HapMap project. RESULTS: RNA-seq was performed on two sweet cherry cultivars, Bing and Rainier using a 3' untranslated region (UTR) sequencing method yielding 43,396 assembled contigs. In order to test our approach of rapid identification of SNPs without any reference genome information, over 25% (10,100) of the contigs were screened for the SNPs. A total of 207 contigs from this set were identified to contain high quality SNPs. A set of 223 primer pairs were designed to amplify SNP containing regions from these contigs and high resolution melting (HRM) analysis was performed with eight important parental sweet cherry cultivars. Six of the parent cultivars were distantly related to Bing and Rainier, the cultivars used for initial SNP discovery. Further, HRM analysis was also performed on 13 seedlings derived from a cross between two of the parents. Our analysis resulted in the identification of 84 (38.7%) primer sets that demonstrated variation among the tested germplasm. Reassembly of the raw 3'UTR sequences using upgraded transcriptome assembly software yielded 34,620 contigs containing 2243 putative SNPs in 887 contigs after stringent filtering. Contigs with multiple SNPs were visually parsed to identify 685 putative haplotypes at 335 loci in 301 contigs. CONCLUSIONS: This approach, which leverages the advantages of RNA-seq approaches, enabled rapid generation of gene-linked SNP and haplotype markers. The general approach presented in this study can be easily applied to other non-model eukaryotes irrespective of the ploidy level to identify gene-linked polymorphisms that are expected to facilitate efficient Gene Assisted Breeding (GAB), genotyping and population genetics studies. The identified SNP haplotypes reveal some of the allelic differences in the two sweet cherry cultivars analyzed. The identification of these SNP and haplotype markers is expected to significantly improve the genomic resources for sweet cherry and facilitate efficient GAB in this non-model crop.