Genomes of the cosmopolitan fruit pest Bactrocera dorsalis (Diptera: Tephritidae) reveal its global invasion history and thermal adaptation.

INTRODUCTION: The oriental fruit fly Bactrocera dorsalis is one of the most destructive agricultural pests worldwide, with highly debated species delimitation, origin, and global spread routes. OBJECTIVES: Our study intended to (i) resolve the taxonomic uncertainties between B. dorsalis and B. carambolae, (ii) reveal the population structure and global invasion routes of B. dorsalis across Asia, Africa, and Oceania, and (iii) identify genomic regions that are responsible for the thermal adaptation of B. dorsalis. METHODS: Based on a high-quality chromosome-level reference genome assembly, we explored the population relationship using a genome-scale single nucleotide polymorphism dataset generated from the resequencing data of 487 B. dorsalis genomes and 25 B. carambolae genomes. Genome-wide association studies and silencing using RNA interference were used to identify and verify the candidate genes associated with extreme thermal stress. RESULTS: We showed that B. dorsalis originates from the Southern India region with three independent invasion and spread routes worldwide: (i) from Northern India to Northern Southeast Asia, then to Southern Southeast Asia; (ii) from Northern India to Northern Southeast Asian, then to China and Hawaii; and (iii) from Southern India toward the African mainland, then to Madagascar, which is mainly facilitated by human activities including trade and immigration. Twenty-seven genes were identified by a genome-wide association study to be associated with 11 temperature bioclimatic variables. The Cyp6a9 gene may enhance the thermal adaptation of B. dorsalis and thus boost its invasion, which tended to be upregulated at a hardening temperature of 38 °C. Functional verification using RNA interference silencing against Cyp6a9, led to the specific decrease in Cyp6a9 expression, reducing the survival rate of dsRNA-feeding larvae exposed to extreme thermal stress of 45 °C after heat hardening treatments in B. dorsalis. CONCLUSION: This study provides insights into the evolutionary history and genetic basis of temperature adaptation in B. dorsalis.

Dataset of volatile compounds from flowers and secondary metabolites from the skin pulp, green beans, and peaberry green beans of robusta coffee.

We obtained data regarding the metabolites from flowers, the skin pulp, green beans and peaberry green beans of the robusta coffee plant (Coffea canephora). The beans were processed using a wet-hulled method. The volatile compounds from the flowers were extracted using a solid-phase microextraction. Secondary metabolites from the skin pulp, green beans, and peaberry green beans were extracted by a maceration method using methanol as a solvent. The separation and identification of metabolites were conducted using gas chromatography-mass spectrometry. The flower's volatile compounds were identified by matching the generated spectra with the NIST14 library as a reference, whereas the metabolites in the skin pulp, green beans, and peaberry green beans were identified using the WILLEY09TH library as a reference. The identified volatile compounds in flowers have been listed in Table 1, and the identified skin pulp, green bean, and peaberry green bean metabolite compounds have been listed in Table 2.

Data set on volatile compound of coffee flowers at different annual rainfall.

This data informs about the profile of volatile compound of coffee flower (Coffee arabica) from different locations with different annual rainfall by using gas chromatography - mass spectrometry (GC-MS). The volatile compounds were captured by solid phase micro extraction (SPME) methods. The extract then subjected to GC-MS for separation and identification of compounds. The profile of volatile compound was provided in, Table 1, Table 2, Table 3 and Table 4.

Dataset on antixenosis and antibiosis of chili fruit by fruit fly (Bactrocera dorsalis) infestation.

This article contains the data on chili antixenosis and antibiosis to fruit fly (Bactrocera dorsalis) infestations. The data was collected from the experiment. Fifty chili varieties (Capsicum spp.) were planted in the screen house and subjected to antixenosis and antibiosis tests. The antixenosis test was evaluated using choice and no-choice methods. The data observed was the number of oviposition punctures by fruit fly on the chili fruits. The antibiosis test was conducted on chili fruits using the Fitness Index method. The data observed were the percentage of pupa (%), the weight of pupa (mg), duration of larva-pupa (day), and duration of pupa-imago (day).

Dataset on the reproductive period of three local species in a tropical sub-mountainous forest.

This data article presents the reproductive period of three local species in a tropical sub-mountainous forest (1000-1300 m above sea level). The tree species were Castanopsis argentea, Saurauia microphylla and Schima wallichii. The reproductive periods were determined by the duration of flowering, flowering-fruiting, and fruiting of the tree species. Observation on the duration of the reproductive period was conducted by counting the number of flowering (flo), flowering-fruiting (flo-fru), and fruiting (fru) trees every month for 24 months successively from July 2015 to June 2017. Analyzed data is provided in Table 1. Primary data is presented in Supplementary Tables 1-3.

Dataset on volatile compounds in susceptible and resistant chili variety to fruit fly infestation.

This data article describes the analysis of volatile compounds in susceptible and resistant chili variety via gas chromatography/mass spectrometry (GC-MS). The volatile compounds from two chili varieties which have different level of resistance to fruit fly infestation were captured by a solid-phase micro extraction method. The obtained extracts were then subjected to GC-MS for separation and identification of the compounds in three developmental stages (buds, flowers and fruits). The retention times and the detected compounds were identified as well as their relative peak areas. The data were provided in Supplementary 1 (Table 1) and Supplementary 2 (Table 2).