Background: Anastrepha fraterculus sp. 1 is considered a quarantine pest in several American countries. Since chemical control applied in an integrated pest management program is the only strategy utilized against this pest, the development of pesticide-free methods, such as the Sterile Insect Technique, is being considered. The search for genes involved in sex-determination and differentiation, and in metabolic pathways associated with communication and mating behaviour, contributes with key information to the development of genetic control strategies. The aims of this work were to perform a comprehensive analysis of A. fraterculus sp. 1 transcriptome and to obtain an initial evaluation of genes associated with main metabolic pathways by the expression analysis of specific transcripts identified in embryos and adults. Results: Sexually mature adults of both sexes and 72 h embryos were considered for transcriptome analysis. The de novo transcriptome assembly was fairly complete (62.9% complete BUSCO orthologs detected) with a total of 86,925 transcripts assembled and 28,756 GO annotated sequences. Paired-comparisons between libraries showed 319 transcripts differently expressed between embryos and females, 1242 between embryos and males, and 464 between sexes. Using this information and genes searches based on published studies from other tephritid species, we evaluated a set of transcripts involved in development, courtship and metabolic pathways. The qPCR analysis evidenced that the early genes serendipity alpha and transformer-2 displayed similar expression levels in the analyzed stages, while heat shock protein 27 is over-expressed in embryos and females in comparison to males. The expression of genes associated with courtship (takeout-like, odorant-binding protein 50a1) differed between males and females, independently of their reproductive status (virgin vs mated individuals). Genes associated with metabolic pathways (maltase 2-like, androgen-induced gene 1) showed differential expression between embryos and adults. Furthermore, 14,262 microsatellite motifs were identified, with 11,208 transcripts containing at least one simple sequence repeat, including 48% of di/trinucleotide motifs. Conclusion: Our results significantly expand the available gene space of A. fraterculus sp. 1, contributing with a fairly complete transcript database of embryos and adults. The expression analysis of the selected candidate genes, along with a set of microsatellite markers, provides a valuable resource for further genetic characterization of A. fraterculus sp. 1 and supports the development of specific genetic control strategies.

Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~40% of protein-coding genes in the model plant Arabidopsis thaliana L. are still not categorized in the Gene Ontology (GO) Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus L.), the number of BP term annotations is far fewer, ~22%. In the current study, we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust, high-quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTLs or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. All in all, we have implemented a valuable strategy to better describe genes within specific biological processes.

Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~ 40% of protein-coding genes in the model plant A. thaliana are still not categorized in the Gene Ontology Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus), the number of BP term annotation is far fewer, ~22%. In the current study we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and performed expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust high quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which, two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTL or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. In all, we have implemented a valuable strategy to better describe genes within specific biological processes.