- Research article
- Open Access
Establishment and analysis of a reference transcriptome for Spodoptera frugiperda
- Fabrice Legeai1, 2,
- Sylvie Gimenez3, 4,
- Bernard Duvic3, 4,
- Jean-Michel Escoubas3, 4,
- Anne-Sophie Gosselin Grenet3, 4,
- Florence Blanc3, 4,
- François Cousserans3, 4,
- Imène Séninet3, 4,
- Anthony Bretaudeau1, 5,
- Doriane Mutuel3, 4,
- Pierre-Alain Girard3, 4,
- Christelle Monsempes6,
- Ghislaine Magdelenat7,
- Frédérique Hilliou8,
- René Feyereisen8,
- Mylène Ogliastro3, 4,
- Anne-Nathalie Volkoff3, 4,
- Emmanuelle Jacquin-Joly6,
- Emmanuelle d’Alençon3, 4,
- Nicolas Nègre3, 4, 9Email author and
- Philippe Fournier3, 4
© Legeai et al.; licensee BioMed Central Ltd. 2014
- Received: 4 April 2014
- Accepted: 15 August 2014
- Published: 23 August 2014
Spodoptera frugiperda (Noctuidae) is a major agricultural pest throughout the American continent. The highly polyphagous larvae are frequently devastating crops of importance such as corn, sorghum, cotton and grass. In addition, the Sf9 cell line, widely used in biochemistry for in vitro protein production, is derived from S. frugiperda tissues. Many research groups are using S. frugiperda as a model organism to investigate questions such as plant adaptation, pest behavior or resistance to pesticides.
In this study, we constructed a reference transcriptome assembly (Sf_TR2012b) of RNA sequences obtained from more than 35 S. frugiperda developmental time-points and tissue samples. We assessed the quality of this reference transcriptome by annotating a ubiquitous gene family - ribosomal proteins - as well as gene families that have a more constrained spatio-temporal expression and are involved in development, immunity and olfaction. We also provide a time-course of expression that we used to characterize the transcriptional regulation of the gene families studied.
We conclude that the Sf_TR2012b transcriptome is a valid reference transcriptome. While its reliability decreases for the detection and annotation of genes under strong transcriptional constraint we still recover a fair percentage of tissue-specific transcripts. That allowed us to explore the spatial and temporal expression of genes and to observe that some olfactory receptors are expressed in antennae and palps but also in other non related tissues such as fat bodies. Similarly, we observed an interesting interplay of gene families involved in immunity between fat bodies and antennae.
- Spodoptera frugiperda
Many organisms of major importance in economy and health of human populations are non-model organisms and thus lack efficient genetic resources that could be used to speed up and facilitate the work of research groups throughout the world. However, the advent of Next Generation Sequencing (NGS), by decreasing sequencing costs of a factor 1,000 , provided the opportunity to sequence the genomes of new organisms (new-models) at different stages of completion. In general, obtaining complete genome sequences for a given organism is immediately followed by the computational and manual annotation of its gene catalog. Genes, in the sense of protein-coding genes, are the major focus of most genome sequencing consortia. Thus, obtaining first a complete transcriptome for an organism, might, in most cases, cover the needs of a specific scientific community. Furthermore, obtaining a good quality reference transcriptome as a first step of a genome sequence project could prove immensely beneficial for gene prediction and annotation.
The Lepidoptera Spodoptera frugiperda (Noctuidae) is an intensely studied organism, yet lacking a comprehensive genomic resource. S. frugiperda, also known as the Fall Army Worm (FAW) is a noctuid moth, classified as a major crop pest by USDA, INRA and other national agronomic agencies. In the United States and in Brazil, it is a threat to corn but is also found devastating cotton, sorghum and other grass-like crops such as rice [2, 3]. Its area of distribution concerns almost the entirety of the American continent , thus much work has been devoted to study the biology of this insect. In order to improve the tools at our disposal to efficiently understand the biology of S. frugiperda, we report here the generation of an NGS-based sequencing of RNA libraries obtained from a large variety and number of tissues and developmental time-points. These libraries have been assembled together in a reference transcriptome, dubbed Sf_TR2012b, comprising around 55,000 sequences available for search through a dedicated database. We further used the Sf_TR2012b assembly to annotate several gene families and study their expression profile. We focused especially on the genes involved in immunity and in olfaction. We validated some of our developmental genes predictions by qPCR to demonstrate that in Sf_TR2012b, some regulated transcripts without clear orthology are bona fide functional genes in S. frugiperda.
Construction of a reference transcriptome
Biological samples from which RNA has been extracted for the construction of the reference transcriptome
454 RNA sequencing
Illumina RNA sequencing
A. 14 Developmental time-points
B. 13 Dissected tissues
C. 10 samples sequenced by Illumina
D. Abbreviations of the libraries
Male adults antennae
Female adults antennae
L2 larvae (early stage)
L2 larvae (late stage)
L2 larvae (early stage)*
L3 larvae (early stage)
L2 larvae (mid-stage)
L3 larvae (late stage)
L2 larvae (late stage)*
L6 larvae (late stage)
L3 larvae (early stage)*
Dimboa treated midguts§
L3 larvae (mid-stage)
Hemocytes and imaginal discs
L4 and L5 larvae antennae and palps§
L3 larvae (late stage)*
Induced fat body§
L6 larvae (early stage)
Gonads from female pupae
L5 larvae tracheae§
L6 larvae (mid-stage)
Gonads from male pupae
L6 larvae (late stage)*
L5&L6 larvae tracheae
Gut stem cells
Statistics of the Sf_TR2012b transcriptome assembly compared to D. melanogaster and B. mori
Total length (nt)
Evaluation of the Sf_TR2012b assembly
Then, we wanted compare our approach with other insects datasets for which the transcriptome was obtained differently. We thus compared the number of contigs (55,000) and the total size (37 M nt) of the Sf_TR2012b assembly with 2 other insect models, Drosophila melanogaster and Bombyx mori. The Drosophila melanogaster transcriptome contains more than 28,000 sequences for a total length of 81 M nt (Table 2). The Drosophila transcripts set is built primarily from computer predictions of the genome sequence and also from permanent curations due to the large Drosophila research community and more than a hundred years of genetics research on this model. Due to the conservation of the majority of genes between insects, we should expect a “complete” transcriptome to come close to this number. A standard approach to construct a reference transcriptome is Sanger sequencing of an EST library. We can find an example of this in public repositories for Bombyx mori. We can see that the total size of this assembly is around 11 Mb (Table 2) because of the assembly of Sanger sequences in Unigenes.Then, we seeked to have a better view of the total content of the Sf_TR2012b assembly by performing systematic blastx against nr (Figure 1B) to checked whether our RNA sequences were corresponding to bona fide proteins and also to checked whether we had some contaminants. 23,126 (42.1%) of the contigs were matching eukaryotic proteins. Of those, 19,895 (86.0%) contigs are similar to a Heliconius protein (Hmel1-1_Release_20120601), while 9,709 Heliconius proteins (75.7%) are similar to a TR2012b contig.
Similarly, 21,439 (92.7%) contigs are similar to a Monarch protein (Dp_geneset_OGS2), while 10,887 Danaus Plexippus (72.0%) proteins are similar to a TR2012b contig. Both comparisons have been made using blastx with a threshold of 1e-10 (p-value) without complexity filter.
We found only 200 contigs (0.4%) matched prokaryotes (archea, bacteria and viruses), making large scale contaminations of our samples highly improbable. What was more surprising was that 57.6% of our RNA sequences didn’t match any known protein sequences. Among these 31,650 sequences without hit against the NR databank, 491 include at least one protein domain found with the following algorithms (BlastProDom, FPrintScan, Gene3D, HMMPanther, HMMPfam, HMMSmart, PatternScan, ProfileScan and superfamily). These comparisons have been done using Interproscan (v4.8) directly on the transcript sequences. This low number of hit does not necessarily reflect that the other sequences are spurious, but they may derived from UTR, or the predicted ORF (Interproscan uses getorf from emboss) might be uncompleted or the sequences may contain a frameshift. This is enhanced by the observation that the transcripts without hit are shorter than the transcripts with hits (mean : 526.5, median : 446 versus global mean : 868.7, and global median : 694). Finally, some of the transcripts without any match against NR databank might correspond to lncRNA, as well as transcribed repeat elements.
For the sequences matching eukaryota, we performed blastx against eukaryotic core gene sets such as the CEGMA (v2.5) geneset (http://korflab.ucdavis.edu/datasets/cegma/) and the BUSCO proteins set (ftp://cegg.unige.ch/OrthoDB7/BUSCO). We found that 452 among 457 CEGMA proteins (98.9%) are similar to 1831 Sf_TR2012b contigs. Similarly 2961 among 3369 BUSCO drosophila proteins (87.9%) and 3025 among the 3299 BUSCO Danaus plexippus proteins (91.7%) were present in the Sf_TR2012b assembly, suggesting that the core components of the FAW transcriptome were present in our assembly.
Assessment of Sf_TR2012b quality and usability through gene families annotation
In order to evaluate the proportion of genes present/absent from our reference transcriptome, we manually annotated different families of genes. Ribosomal proteins (rbp) are mostly conserved among eukaryota and are present as highly expressed small genes throughout most genomes. Thus the percentage of rbp found should be indicative of the minimal requirement for finding most housekeeping genes. We used a set of 81 proteins annotated as rbp in B. mori (D. Heckel, personal communication) (Additional file 3: Table S2) and searched for homologs in Sf_TR2012b. Out of those 81 sequences, we could find 74 hits matching the complete B. mori transcript. For 7 rbp, we found only partial matches (Figure 1C). Thus, we conclude that more than 90% of housekeeping genes are represented in Sf_TR2012b assembly.
Conversely, we searched for more constrained genes belonging to 3 functional families : homeobox-domain genes (Hox), odorant and pheromone-binding proteins and immune related genes. Hox proteins can be identified with certainty thanks to the conserved signature of their homeodomains even between distantly related species . Within species however, many paralogs can be identified. Contrary to the ribosomal proteins, the expression of genes encoding Hox proteins in D. melanogaster is usually temporally and spatially restricted. Thus we expected these particular proteins to be more difficult to find in our assembled transcriptome due to their underrepresentation in the RNA samples collected, compared to other abundant transcripts such as ribosomal proteins. We used a collection of 105 D. melanogaster homeodomain protein sequences from the Homeodomain Resource Database  as a tblastn query of our Sf_TR2012b transcriptome assembly. 30 (28.6%) unique Drosophila homeodomain orthologs were thus detected (Additional file 4: Table S3, Figure 1C).
Similarly, we used a set of chemosensory genes previously identified by transcriptome sequencing in the closely related species S. littoralis, the cotton leaf worm [10–12], and that comprises both highly expressed (odorant-binding proteins, OBPs, and chemosensory proteins, CSPs) and low expressed (chemosensory receptors) genes. Found in abundance in the olfactory organs, OBPs and CSPs are proposed to transport odorants to membrane bound receptors [13, 14]. Two families of volatile molecule receptors have been described in insects, the olfactory receptors (ORs) and the ionotropic receptors (IRs), these two types being involved in the recognition of different volatile families as demonstrated in D. melanogaster. Co-receptors highly conserved among species are required for these receptor functioning: ORco [16–18] is required to form with ORs heterodimers while IR25a and IR8a are proposed to complex with IRs . We used a set of 121 chemosensory genes (36 OBPs, 21 CSPs, 47 ORs and 17 IRs) previously identified in S. littoralis to search for homologs in the S. frugiperda reference transcriptome. 50 (87.7%) of the highly expressed transcripts (OBPs and CSPs) were recovered whereas we could recover only 15 out of 64 S. littoralis low expressed chemosensory receptors transcripts (23.4%) (Figure 1C). Interestingly, we could identify 11 putative new chemosensory transcripts, bearing the hallmark signal peptide but with no ortholog in S. littoralis, encompassing 7 OBPs, 3 CSPs and one IR). The numbers of OBPs and CSPs annotated in S. frugiperda, (38 OBPs and 22 CSPs - Additional file 5: Table S4) is within the range of the numbers of OBPs and CSPs usually annotated in Lepidoptera genomes, e.g. in B. mori[20, 21]. Only partial sets of ORs and IRs could be identified compared to the numbers of such genes annotated in either S. littoralis or B. mori[22, 23], certainly because of their low expression level. Accordingly, we found many frameshifts and inappropriate stop codons in the predicted ORs. However, the three co-receptors ORco, IR25a and IR8a could all be annotated in S. frugiperda. This reflects their high expression levels due to their function as co-receptors.
Thanks to these four points of comparison, we think on one hand our current assembly is sufficiently deep to uncover most genes of S. frugiperda. They are usually complete sequences if they have a high level of expression. But on the other hand, we might have missed around 70% of the rarest transcripts. Altogether, the Sf_TR2012b assembly seems perfectly adequate in order to identify a large part of the coding sequences of S. frugiperda.
Access to Sf_TR2012b through Lepidodb
Measure of transcripts expression by RNAseq
Common and specific developmental genes
Expression of the genes of olfaction
Expression of the genes of immunity
Analyses of immune genes expression in the different development stages or tissues are in agreement with what is commonly described in the literature. For instance, induced fat body (FB) strongly expressed genes encoding AMPs (Additional file 9: Figure S4, Figure 6B). On the other hand, surprisingly, antennae and palps (A/P) also strongly expressed genes encoding AMPs. Interestingly, some AMPs genes are expressed in FB and A/P whereas others seem to be preferentially expressed in only one of the two “tissues”. What is the most remarkable is that some AMPs genes (i. e., some cecropins and defensins) are less expressed in bacterial challenged FB than in unchallenged A/P, suggesting that those tissues develop a constitutive immune response.
Thanks to high-throughput sequencing of antennal transcriptomes, this phenomenon has recently been observed in other Lepidoptera, in Diptera (reviewed in ) and in some Hymenoptera like the leaf-cutter ant in which most of the immune genes are highly expressed in the antenna of the queen ant . Those observations raise the question of the interplay between immune and gustatory/olfactory systems. One may wonder if expression of immune genes in gustatory/olfactory systems is a bona fide immune response or is somehow involved in nutritional strategies like food selection.
In conclusion, we provide in this study a reference transcriptome for S. frugiperda, available through a dedicated database, along with measures of differential expression across 10 different samples. We found this resource invaluable to annotate and study the expression of different families of genes. In particular, we were able to identify and validate Lepidoptera specific genes involved in development. We also analyzed a set of genes involved in olfaction across two Spodoptera species. And finally, we annotated an almost complete set of immune related genes and observed in particular that some anti-microbial peptides are highly expressed in chemosensory organs, even in absence of induction, raising the possibility that antennae and palps can naturally act as primary organs of immune response, since they are in open contact with the natural environment.
Total RNA has been extracted using Trizol® reagent according to manufacturer recommendations from the biological samples indicated in Table 1. For each condition, whether a developmental time point or a dissected tissue, a sufficient amount of fresh tissue has been used in order to extract around 10 μg of total RNA. Staging of the larval time-points were made according to the size of the cephalic capsule combined with the time elapsed from one stage to another. Dissections of were performed in standard conditions without any peculiarities except for gut stem cells isolation, where midgut tissues were dissected from anesthetized larvae just before the 5th molt and stem cells were isolated as previously reported .
2 samples in particular were subjected to specific conditions.
1/ the FatBody induced sample, (library Fbi from Table 1, column C) 1 day-old S. frugiperda sixth-instar larvae were bacteria-challenged with a mixture of Escherichia coli (CIP7624) and Micrococcus luteus (CIP5345) (106 bacteria/larva). Eight hours post infection, the fat body from 6 larvae was recovered and RNA extracted.
2/ the Midgut DIMBOA sample (MD library, Table 1 column C), L5 larvae were fed corn plants of the Ci31A variety that contains high levels of DIMBOA. DIMBOA (2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one) is an antibiotic molecule naturally present in maize that protects it from pests and pathogens. Midguts were then dissected and washed prior to RNA extraction.
For the 454 sequencing, a normalized pool (equimolar for each sample from the Table 1 columns A and B) has been prepared. 37 μg of RNA from the pool has been used for the construction of the library. 10 μg of RNA has been extracted for each of the10 samples destined to Illumina sequencing (Table 1, column C).
Sequencing and statistics
The RNA samples have been sent to the GATC Company for 454 sequencing (construction of one normalized cDNA library and sequencing on the GS FLX (Roche/454), Titanium chemistry) and Illumina sequencing (construction of tagged standard cDNA libraries and sequencing of 1 × 56 bp on a Genome Analyzer II (Illumina/Solexa)) according to manufacturers instructions. The 454 sequencing generated 1,080,352 reads of a mean length of 322 bp. The number of reads generated by Illumina for each library are indicated in Additional file 7: Figure S2 and are in the range of 3 to 11 millions reads.
Assembly and alignments
The flowchart of the assembly process is presented in Additional file 1: Figure S1. A first step of assembly of the 454 reads has been performed by GATC Company using the CD-HIT software  and resulted in 183,373 clusters. The 1,042,944 454 reads clipped from adaptator (GATC) were compared to Univec (https://www.ncbi.nlm.nih.gov/tools/vecscreen/univec/, version of march 2011) leading to the removal of 3,297 reads similar to known vector sequences. The 79,148 ESTs previously sequenced by Sanger method and coming from 8 different libraries (Sf9 cell lines, Sf21 cell lines, hemocytes, induced hemocytes, midgut, induced midgut, fat body and a tissue mix)  were also compared to UniVec resulting in the removal of 1132 ESTs. We performed an assembly of the 78,016 ESTs and 1,039,647 454 reads using the MIRA software . This step resulted in 52,865 contigs with an N50 of 794 bp. Then we clipped the sequences at both ends by 80 bp and mapped the combined 90,454,901 reads from the 10 Illumina libraries onto this reference with Gassst . The resulting 17,724,510 short reads that were unmapped underwent a subsequent step of short reads assembly using the velvet and Oases softwares . We finally used MIRA to assemble together the 24,505 contigs from the Velvet/Oases assembly and the 52,865 contigs from the previous MIRA assembly of the 454 sequences and EST sequences. This final assembly is Sf_TR2012b.
To produce the time-course expression datasets, the reads from the 10 Illumina libraries have been aligned against Sf_TR2012b and the BACs set using bowtie . Alignment files were further processed by samtools .
We compared the Sf_TR2012b proteins to the Bombyx mori GeneSet A B and C (http://sgp.dna.affrc.go.jp/ComprehensiveGeneSet/) using blastx with a threshold of 1e-10 (p-value) without complexity filter.
Analysis of expression data
The Illumina 50 bp reads were mapped on the transcriptome with bowtie using the options (-a -m 1 --best --strata), reporting the best alignment (i.e. having the least number of mismatches). The raw counts by library were calculated by contig using a home-made program (based on the perl Bio::DB::Sam library). The raw counts have been divided by the total number of aligned reads in order to obtain the RPM normalized values. As well, for each library we computed the normalizations by quantile normalize BetweenArrays function of the limma R library  and the by size factors (functions estimateSizeFactors and sizeFactors from the DESeq R package ).
25 BACs from Spodoptera frugiperda genome have been isolated as previously described . Their sequence has been deposited at the European Bioinformatics Institute nucleotide archive : http://www.ebi.ac.uk/ena/. The accession numbers are listed in Additional file 10.
For the qPCR validation we extracted RNA from independent samples of Spodoptera frugiperda eggs and L2 larvae. A reverse transcription has been performed, using SuperscriptIII from Invitrogen to obtain the cDNA. We performed SYBRGreen (Roche) based qPCRs on 384-well plates on a LightCycler 480. Each reaction has been performed in triplicate on the plate. The quantification method was ∆∆Cp. In addition, we performed the qPCR validations for all the primers on 3 independent biological replicates for eggs and L2 larvae. Primers sequences are indicated in the Additional file 10.
The 454 and Illumina sequencing have been performed thanks to a grant “INRA AIP Bio-ressources 2009”. BACs were sequenced thanks to a 2003 Genoscope project “Lepidoptera comparative genomics”.
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