Abstract
Background: The insect predator, Arma chinensis, is capable of effectively controlling many pests, such as Colorado potato beetle, cotton bollworm, and mirid bugs. Our previous study demonstrated several life history parameters were diminished for A. chinensis reared on an artificial diet compared to a natural food source like the Chinese oak silk moth pupae. The molecular mechanisms underlying the nutritive impact of the artificial diet on A. chinensis health are unclear. So we utilized transcriptome information to better understand the impact of the artificial diet on A. chinensis at the molecular level. Methodology/principal findings: Illumina HiSeq2000 was used to sequence 4.79 and 4.70 Gb of the transcriptome from pupae-fed and artificial diet-fed A. chinensis libraries, respectively, and a de novo transcriptome assembly was performed (Trinity short read assembler). This resulted in 112,029 and 98,724 contigs, clustered into 54,083 and 54,169 unigenes for pupae-fed and diet-fed A. chinensis, respectively. Unigenes from each sample's assembly underwent sequence splicing and redundancy removal to acquire non-redundant unigenes. We obtained 55,189 unigenes of A. chinensis, including 12,046 distinct clusters and 43,143 distinct singletons. Unigene sequences were aligned by BLASTx to nr, Swiss-Prot, KEGG and COG (E-value <10(-5)), and further aligned by BLASTn to nt (E-value <10(-5)), retrieving proteins of highest sequence similarity with the given unigenes along with their protein functional annotations. Totally, 22,964, 7,898, 18,069, 15,416, 8,066 and 5,341 unigenes were annotated in nr, nt, Swiss-Prot, KEGG, COG and GO, respectively. We compared gene expression variations and found thousands of genes were differentially expressed between pupae-fed and diet-fed A. chinensis. Conclusions/significance: Our study provides abundant genomic data and offers comprehensive sequence information for studying A. chinensis. Additionally, the physiological roles of the differentially expressed genes enable us to predict effects of some dietary ingredients and subsequently propose formulation improvements to artificial diets.