Both the wheat midge (Sitodiplosis mosellana) (Géhin) (Diptera: Cecidomyiidae) and the Hessian fly (Mayetiola destructor) (Say) (Diptera: Cecidomyiidae) belong to a group of insects called gall midges (Diptera: Cecidomyiidae), and both are destructive pests of wheat. From Hessian fly larvae, a large number of genes have been identified to encode secreted salivary gland proteins (SSGPs), which are presumably critical for the insect to feed on and manipulate host plants. For comparison, we conducted an analysis on transcripts encoding SSGPs from the first instar larvae of the wheat midge. In total, 3,500 cDNA clones were sequenced, from which 1,301 high-quality sequences were obtained. Approximately 25% of the cDNAs with high-quality sequences encoded SSGPs. The SSGPs were grouped into 97 groups based on sequence homology. Among the SSGP-encoding transcripts, 206 encoded unique proteins with no sequence similarity to any known protein and 29 encoded proteins similar to known proteins including proteases, serpines, thioesterases, ankyrins, and ferritins. Most (~80%) SSGP-encoding genes appear under strong selection for mutations that generate amino acid changes within the coding region. Identification and characterization of SSGPs in wheat midge larvae provide a foundation for future work to reveal molecular mechanisms behind wheat midge–wheat interactions and the role of these putative effector proteins in insect virulence. Availability of the SSGP transcripts will also facilitate comparative analyses of insect effectors from related species. Key words: orange wheat blossom midge, transcriptome analysis, secreted salivary gland protein, Sitodiplosis mosellana, insect effector The orange wheat blossom midge (wheat midge), Sitodiplosis Very little is known regarding detailed feeding mechanisms of mosellana (Géhin) (Diptera: Cecidomyiidae), is one of the most wheat midge larvae. Like other gall midges, wheat midge larvae are destructive pests of wheat in the northern hemisphere (Berzonsky thought to inject saliva into wheat developing seeds, resulting in shrive- et al. 2003, Doane and Olfert 2008). In the United States, wheat led wheat kernels (Lamb et al., 2000). Many plant pathogens and par- midge outbreaks have been recorded on spring wheat in the north- asitic insects possess an effector-based mechanism to attack host plants ern states of Minnesota, Montana, and North Dakota. In the mid- and promote virulence via secreted effector proteins (Shorthouse and 1990s, spring wheat losses were estimated at more than $27 million Rohfritsch 1992, Miles 1999, Harris et al. 2015, Toruno et al. 2016). in North Dakota (NDSU, 2016). Wheat midge larvae can feed on In plant–insect systems that have gene-for-gene interactions (namely developing seeds of both bread and durum wheat (Ding et al. 2000, for every resistance gene in host plants, there is a corresponding avir- Harris et al. 2003). Wheat midges have four life stages: egg, larva, ulence gene in the insect), many effectors from parasitic insects have pupa, and adult. Females lay eggs on the surface of wheat heads. been identified and characterized (Harris et al. 2015, Stuart 2015). For Newly hatched larvae feed on developing kernels for 2–3 wk. The instance, in the Hessian fly, a large number of secreted salivary gland first two instars are the damaging stages. Mature third instars drop proteins (SSGPs) have been identified and many of them are likely from wheat heads after rain or heavy dew in August to move into to play effector roles once injected into plant tissues. Approximately 2–4 inches deep in soil for overwintering. In unfavorable conditions, 60% of transcripts in salivary glands of Hessian fly first instar larvae larvae can remain dormant and survive in cocoons for more than 10 encode SSGPs (Chen et al. 2008). Later, genomic sequencing revealed yr (Harris et al. 2003). more than 7% of predicted genes in the Hessian fly genome encode Published by Oxford University Press on behalf of Entomological Society of America 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. This Open Access article contains public sector information licensed under the Open Government Licence v2.0 (http://www.nationalarchives.gov.uk/doc/ open-government-licence/version/2/). Downloaded from https://academic.oup.com/jinsectscience/article-abstract/18/1/17/4883173 by Ed 'DeepDyve' Gillespie user on 16 March 2018 2 Journal of Insect Science, 2018, Vol. 18, No. 1 effector-like proteins (Zhao et al. 2015). Several avirulence effectors Technologies, Santa Clara, CA). cDNA libraries were constructed have been cloned from the Hessian fly, and all of them were SSGPs using a “SMART” library construction kit from Clontech (Palo Alto, (Aggarwal et al. 2014; Zhao et al. 2015, 2016). Many secreted proteins CA) as described by Chen et al. (2004). Briefly, cDNA inserts were have also been identified in the saliva of several aphid species (Thorpe ligated into the pPCRXL-TOPO plasmid contained in a TOPO TA et al. 2016), and many of these aphid proteins act as effectors either cloning kit (Invitrogen, Carlsbad, CA) instead of a phage vector. to suppress or trigger plant defense responses (Elzinga et al. 2014). Individual clones were picked up for plasmid DNA isolation, which Therefore, identification of SSGPs from insects provides an efficient were sequenced with the M13 forward and reverse primers follow- way to identify putative effectors of insect species. ing the Sanger DNA sequencing method via a commercial contract Whether the interaction between wheat midge and wheat fol- (GENEWIZ, South Plainfield, NJ). lows a gene-for-gene model remains to be investigated. However, a highly effective resistance gene, named Sm1, to the wheat midge was Sequence Analysis discovered in winter wheat genotype in 1996 (Barker and McKenzie Vector sequences were trimmed from raw reads after cDNA clones 1996), and wheat cultivars with Sm1 can significantly limit kernel were sequenced. Sequences from sense and antisense directions were damage and yield loss (Blake et al. 2014, Smith et al. 2014). The ex- aligned to examine if a clone was sequenced fully from both direc- istence of a major resistance gene in wheat suggests that a gene-for- tions. If no overlap was found between the sense and antisense reads, gene relationship is possible in the wheat midge–wheat interaction. new primers were synthesized for further sequencing. Two groups of SSGPs in wheat midge larvae have been reported Cluster analyses of cDNAs were conducted using BlastStation- previously (Chen et al. 2010). However, large-scale identification of Local 64 program. Open reading frames (ORF) were identified SSGP-encoding genes in the wheat midge has not been conducted. using the ORF finder. Sequence alignment and similarity analysis The objective of this study is to conduct a more extensive analysis were performed using various BLAST programs (http://www.ncbi. of SSGPs from dissected salivary glands of first instar wheat midge nlm.nih.gov/). Initial database search was conducted with BLASTN larvae via a transcriptomic approach. 3 and BLASTX. Sequence alignments with E-values greater than 10 were considered to have no meaningful sequence similarity between Materials and Methods the two sequences. Sequence alignments with E-values smaller than 10 were considered that two sequences share significant similarity. Insects and Salivary Gland Preparation 3 10 Sequence alignments with E-values between 10 and 10 were fur- The insect population used in this research was derived from a ther examined individually to determine if two sequences share colony consisting approximately 20,000 individuals collected from similarity based on the length and gaps of the alignments. Analysis Divide County in North Dakota in 2013. The colony has been main- for secretion signal peptides was carried out using the SignalP v4.1 tained in a greenhouse at North Dakota State University, Fargo, ND, (Center for Biological Sequence Analysis, Technical University of since then. Denmark; http://www.cbs.dtu.dk/services/SignalP/). Seeds of Roblin hard red spring wheat, an early maturing Canadian variety that is susceptible to wheat midge, were planted Calculation of Synonymous and Nonsynonymous in a greenhouse at North Dakota State University to rear the wheat Mutation Rates midge. Wheat plants were maintained at 20°C with a photoperiod The percentages of synonymous and nonsynonymous mutations of 18:6 (L:D) h cycles. Meanwhile, dormant pupae at 4°C have been were calculated based on sequence alignments of members within a placed at room temperature to break down the dormancy stage group. For example, the percentages of nonsynonymous mutations for adult’s emergence. When wheat plants were at Zadok’s growth were derived by dividing the number of nonsynonymous mutations stages 55–59 (the inflorescence is half or more emerged from the by the number of total mutations among group members. If there sheath), two or more gravid females were placed into a glass cylinder are multiple members that share the same mutation at the same pos- covering an individual wheat head. After 24 h of exposure to the ition, then the mutation is counted only once. However, if different wheat midge females, the glass cylinder was removed and the head members have two or more different mutations at the same position, was covered with a glassine pollination bag to help protect the eggs then the mutation was counted as two or more. from desiccating as they develop. Egg hatch and larval migration to their larval feeding sites on the surface of the developing seed occurs 3 d after oviposition. For RNA analysis, 3- to 4-d-old wheat midge Results larvae were collected from wheat heads with the aid of a 20× dissect- ing microscope. Salivary glands were obtained by dissecting first in- Composition of Transcripts Obtained From star larvae in saline buffer. Dissection was achieved by pulling away Dissected Salivary Glands the anterior tip of a larva with a pair of forceps while holding the In total, 3,523 cDNA clones were sequenced. After removing clones posterior end of the larva steady with another pair of forceps. The with small inserts and bad quality sequences, 1,301 cDNA sequences salivary glands of the larva move out of the cascade during this pro- were retained. Among these cDNAs, 330 (25.3%) encode SSGPs and cess along with other mouthpart tissues. Clean salivary glands were the remaining 971 (74.6%) encode proteins without a typical secre- then obtained by removing unwanted mouthpart tissues. For RNA tion signal peptide. Among the SSGP-encoding cDNAs, 235 encoded analysis and cDNA library construction, the dissected glands were unique proteins with no sequence similarity to any known sequences transferred into TRI reagent (Molecular Research, Inc., Cincinnati, in GenBank, whereas 33 encoded proteins with sequence similarity OH) and frozen in liquid nitrogen as soon as they were obtained. to known proteins such as carboxypeptidases, peptidases, lysosomal thioesterases, serpins, ankyrins, and ferritins (Supp Fig. S1 and Supp cDNA Library Construction and Sequencing Table S1 [online only]). Total RNA was isolated from 300 pairs of salivary glands using TRI Among the 971 non-SSGP transcripts, 295 (30.4%) encode pro- reagent following the protocol provided by the manufacturer. RNA teins with no meaningful (E-values greater than 10 ) sequence sim- quality and integrity were assessed using a Bioanalyzer (Agilent ilarity to any proteins in GenBank, 321 (33.1%) encode proteins Downloaded from https://academic.oup.com/jinsectscience/article-abstract/18/1/17/4883173 by Ed 'DeepDyve' Gillespie user on 16 March 2018 Journal of Insect Science, 2018, Vol. 18, No. 1 3 with sequence similarity to proteins with unknown function, and from gall midges are conserved unconventionally (Chen et al. 2010), the remaining 353 (36.4%) encode proteins with sequence similar- which would cause problems in correctly assembling short sequence ity to proteins with various functions. For the transcripts encoding reads from high-throughput sequencing. Second, the wheat midge is known proteins, 153 (43.1%) are proteins with functions in protein an understudied species genomically and may be difficult to annotate synthesis and the remaining 205 (57.7%) with other house-keeping small transcript fragments. functions, including energy-metabolic enzymes, structural proteins, Our analysis resulted in the identification of 97 groups of tran- transporters, and others (Supp Table S2 [online only]). scripts encoding SSGPs. Among these groups, 64% (62 groups) are singletons, indicating that our analysis is very preliminary and fur- ther sequencing more clones is likely to identify much more unique SSGP Classification SSGP transcripts. The most abundant group is group 1, which has SSGP-encoding transcripts were sorted into 97 groups according 48 unique transcripts (99 including redundant sequences). SSGP pro- to the sequence similarity among the cDNAs and derived proteins teins encoded by group 1 transcripts share no sequence similarity (Supp Table S1 [online only]). Among the 97 groups, 66 have either with any known sequences in GenBank, and therefore, the functions a single clone or multiple clones that encode the same protein. The of this group of genes remain to be determined. The fact that mem- remaining 31 groups have multiple clones that encode at least two bers among this group have been under strong positive selection different proteins. Proteins within a group share at least 30% amino indicates that this group of genes are likely to play important roles acid identity and have a highly conserved secretion signal peptide. 3 in the wheat midge–wheat interaction. Other abundant transcript Proteins between different groups share no meaningful (E > 10 ) groups include group 2, group 3, group 4, group 12, group 13, group sequence similarity and have a completely different secretion sig- 24, group 29, group 40, group 45, and group 67 (Supp Table S1 nal peptide (Supp Fig. S1 [online only]). Figure 1 shows amino acid [online only]). sequence alignments of two representative groups. Both groups have There are commonalities and differences between the putative a highly conserved signal peptide and a more diversified mature pro- SSGPs from wheat midge larvae and those from Hessian fly larvae, tein. The overall conservation among group members particularly a species that has been studied more extensively for SSGP-encoding in the signal peptide region suggests that the transcripts within a genes (Chen et al. 2004, 2008, 2010; Zhao et al. 2015, 2016). A com- group may have been derived from genes that share the same evolu- monality is that most of the SSGPs are small peptides (50–150 amino tionary origin and, therefore, can be considered the same gene fam- acid residues), and those small SSGPs share no sequence similarity ily (Fig. 1). Some sequence variation may have also resulted from with any known proteins in available databases. In addition, SSGP- different alleles of the same gene. Amino acid sequence alignments encoding genes from both the wheat midge and Hessian fly appear of all groups with multiple members are shown in Supp Fig. S2 to be under strong diversifying selection pressure. Evidence for this (online only). is the fact that over 70% of point mutations among group mem- bers are nonsynonymous (Table 1). A similar phenomenon was also Sequence Variations Among Group Members found in Hessian fly SSGP-encoding genes, where over 80% of point Group members among those with significant sequence variations mutations among group members were nonsynonymous (Chen et al. were divided into mature protein (MP)-coding region, signal pep- 2004). The fast-evolving nature of SSGP-encoding genes in both tide (SP)-coding region, and noncoding regions, and percentages of insect species is another indicator that these genes are involved in nucleotides with sequence variation in each region were analyzed interactions with their host plants (Thompson 1998). There is no (Table 1). Among these three regions, sequence variation in the sequence similarity between SSGPs from wheat midge larvae and SP-coding region was the lowest except group 24, probably due those from Hessian fly larvae, suggesting that SSGPs from these to the functional constraint of the secretion role of signal peptides. two insect species perform different biochemical functions and have Variation rates in MP-coding region and noncoding regions were different mechanisms to manipulate host plants. In addition, many much higher (Supp Table S3 and Supp Fig. S2 [online only]). SSGP-encoding genes from Hessian fly exhibit an unconventional To examine if group members were under selection pressure for conservation pattern, in which the 5′- and 3′-noncoding regions and diversification, the percentages of nonsynonymous and synonymous introns are highly conserved, whereas the regions encoding mature mutations in the MP-coding region were also analyzed. Over 70% of proteins are highly diversified (Chen et al. 2010, Zhao et al. 2015). nucleotide substitutions were nonsynonymous (Table 1). No such unconventional conservation pattern was found among group members of SSGP-encoding genes from the wheat midge. In addition to small SSGPs, there are a few transcripts that en- Discussion code secreted proteins with sequence similarity to known proteins, Many insects inject effectors into host tissues to manipulate plants which include proteases, protease inhibitors, lysosomal thioester- including suppressing host defense, inhibiting plant growth, and ases, ankyrins, and ferritins. Whether these proteins are injected into reprogramming plant metabolism (Stuart 2015). Some insects also host plants or secreted into body fluid of the insect remains to be inject effectors into host tissues for predigesting food before in- determined. Proteases and protease inhibitors have also been found gestion and for various other functions (Miles 1999, Harris et al. in saliva from other insect species (Miles 1999, Chen et al. 2008, Liu 2015). The salivary glands of insects are the main tissue to produce et al. 2016). Proteases could act as digestive enzymes for preoral di- effector proteins for host injection. Therefore, analyzing transcripts gestion of food before ingestion, whereas protease inhibitors could in the salivary glands and identifying those proteins with a secre- neutralize defense proteases from host plants (Pechan et al. 2002). tion signal peptide is an efficient way to identify putative effector Lysosomal thioesterases, ankyrins, and ferritins play house-keeping proteins (Chen et al. 2004, 2008). In this study, we analyzed the functions inside insects. However, some proteins with house-keeping composition of transcripts in salivary glands of the first instars of functions in insects can also be injected into host plants and play ef- the wheat midge through a traditional Sanger sequencing approach. fector roles in insect–plant interactions (Miles 1999). There are two reasons to follow a traditional sequencing approach In summary, we have conducted a global analysis on genes in this study. First, previous studies have shown that effector genes expressed in the salivary glands of first instars of the wheat midge Downloaded from https://academic.oup.com/jinsectscience/article-abstract/18/1/17/4883173 by Ed 'DeepDyve' Gillespie user on 16 March 2018 4 Journal of Insect Science, 2018, Vol. 18, No. 1 Fig. 1. Amino acid alignments of two representative groups. The boundary between predicted signal peptide and mature proteins is indicated by an arrow. Only partial alignment for the second group is shown in the figure. for the first time and identified numerous genes encoding SSGPs. be used to produce recombinant proteins for various biochemical The availability of the putative effector genes provides a founda- assays or for antibody production to analyze tissue distribution tion for further research to characterize the roles of these genes in within both the insect bodies and the host tissues if they are injected wheat midge and wheat interactions. For example, the cDNAs could into plants during feeding. The availability of these genes is also Downloaded from https://academic.oup.com/jinsectscience/article-abstract/18/1/17/4883173 by Ed 'DeepDyve' Gillespie user on 16 March 2018 Journal of Insect Science, 2018, Vol. 18, No. 1 5 Table 1. Analysis of sequence variation among group or subgroup members Group or subgroup % Nucleotides with mutations among group or subgroup % Nonsynonymous mutations in MP coding members MP coding SP coding Noncoding Group 1, subgroup 1 26.5 3.3 35.0 77.5 Group 1, subgroup 2 17.0 8.3 20.4 80.5 Group 24 7.2 4.2 0 82.6 Group 29 14.3 9.3 9.7 88.9 Group 40 11.9 1.6 11.7 71.9 Elzinga, D. A., M. De Vos, and G. Jander. 2014. Suppression of plant defenses useful for comparative analysis of salivary proteins from different by a Myzus persicae (green peach aphid) salivary effector protein. Mol. insect species. Plant. Microbe Interact. 27: 747–756. Harris, M. O., J. J. Stuart, M. Mohan, S. Nair, R. J. Lamb, and O. Rohfritsch. Supplementary Data 2003. Grasses and gall midges: plant defense and insect adaptation. Annu. Rev. Entomol. 48: 549–577. Supplementary data are available at Journal of Insect Science online. Harris, M. O., T. L. Friesen, S. S. Xu, M. S. Chen, D. Giron, and J. J. Stuart. 2015. 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