Transcriptome Sequencing of Different Avocado Ecotypes: de novo Transcriptome Assembly, Annotation, Identification and Validation of EST-SSR Markers

Avocado (Persea americana Mill.) could be considered as an important tropical and subtropical woody oil crop with high economic and nutritional value. Despite the importance of this species, genomic information is currently unavailable for avocado and closely related congeners. In this study, we generated more than 216 million clean reads from different avocado ecotypes using Illumina HiSeq high-throughput sequencing technology. The high-quality reads were assembled into 154,310 unigenes with an average length of 922 bp. A total of 55,558 simple sequence repeat (SSR) loci detected among the 43,270 SSR-containing unigene sequences were used to develop 74,580 expressed sequence tag (EST)-SSR markers. From these markers, a subset of 100 EST-SSR markers was randomly chosen to identify polymorphic EST-SSR markers in 28 avocado accessions. Sixteen EST-SSR markers with moderate to high polymorphism levels were detected, with polymorphism information contents ranging from 0.33 to 0.84 and averaging 0.63. These 16 polymorphic EST-SSRs could clearly and effectively distinguish the 28 avocado accessions. In summary, our study is the first presentation of transcriptome data of different avocado ecotypes and comprehensive study on the development and analysis of a set of EST-SSR markers in avocado. The application of next-generation sequencing techniques for SSR development is a potentially powerful tool for genetic studies.

Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields

In soybean, Sclerotinia sclerotiorum apothecia are the sources of primary inoculum (ascospores) critical for Sclerotinia stem rot (SSR) development. We recently developed logistic regression models to predict the presence of apothecia in irrigated and nonirrigated soybean fields. In 2017, small-plot trials were established to validate two weather-based models (one for irrigated fields and one for nonirrigated fields) to predict SSR development. Additionally, apothecial scouting and disease monitoring were conducted in 60 commercial fields in three states between 2016 and 2017 to evaluate model accuracy across the growing region. Site-specific air temperature, relative humidity, and wind speed data were obtained through the Integrated Pest Information Platform for Extension and Education (iPiPE) and Dark Sky weather networks. Across all locations, iPiPE-driven model predictions during the soybean flowering period (R1 to R4 growth stages) explained end-of-season disease observations with an accuracy of 81.8% using a probability action threshold of 35%. Dark Sky data, incorporating bias corrections for weather variables, explained end-of-season disease observations with 87.9% accuracy (in 2017 commercial locations in Wisconsin) using a 40% probability threshold. Overall, these validations indicate that these two weather-based apothecial models, using either weather data source, provide disease risk predictions that both reduce unnecessary chemical application and accurately advise applications at critical times.

EST-SSR development for Digitaria exilis and its relatives D. iburua and D. longiflora from transcriptome sequences

Digitaria exilis is an important indigenous cereal in West Africa. The first fonio reference transcriptome was released and became a key tool for developing new molecular markers contributing to a better understanding of its genetic diversity. A total of 126 new putative primer pairs were successfully designed in 37,327 unigenes from the D. exilis transcriptome. Thirty-seven primer pairs were randomly selected and tested for their ability to cross-amplify to related species. Clear amplification patterns were observed on 24 primer pairs. Of these, 71, 74 and 35% showed polymorphism in three species: D. exilis, D. longiflora and D. iburua. The transferability from D. exilis was 96% to D. longiflora and 71% to D. iburua. The new SSR markers confirmed the close genetic proximity of D. exilis with D. longiflora and its stronger genetic difference of D. exilis from D. iburua. These markers will be valuable for completing future knowledge on Digitaria evolutionary history, and for testing gene flows between related species.

Development of Microsatellite Markers for the Nipa Palm Hispid Beetle, Octodonta nipae (Maulik)

The nipa palm hispid beetle, Octodonta nipae (Maulik) (Coleoptera: Chrysomelidae), is an important invasive pest on palm plants in southern China. Based on existing transcriptome data, polymorphism simple sequence repeat (SSR) loci were identified. In total, 1274 SSR loci were identified from 49919 unigenes. The majority of them contained mononucleotide, dinucleotide, and trinucleotide motifs (43.56%, 26.14%, and 28.18%), in which A/T (41.21%) and AT/TA (15.86%) were the most abundant motifs. 104 pairs of the SSR primers produced amplification bands of expected sizes in O. nipae, 80 pairs of SSR primers were tested randomly for polymorphism, 9 loci of them were validated to be polymorphic markers, and the number of alleles ranged from 2 to 3, with an average of 2.56 per locus. The population of Zhangzhou and Fuzhou was analyzed by the 9 loci (On1–On9). These SSR transcriptome data can provide invaluable resource for SSR development, population genetics research, invasion and expansion mechanism, paternity testing, and other research on O. nipae and its related species.