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The unique antimicrobial peptide repertoire of stick insects
Journal Pre-proof The unique antimicrobial peptide repertoire of stick insects Matan Shelomi, Chris Jacobs, Andreas Vilcinskas, Heiko Vogel PII:
S0145-305X(19)30347-7
DOI:
https://doi.org/10.1016/j.dci.2019.103471
Reference:
DCI 103471
To appear in:
Developmental and Comparative Immunology
Received Date: 24 July 2019 Revised Date:
17 August 2019
Accepted Date: 18 August 2019
Please cite this article as: Shelomi, M., Jacobs, C., Vilcinskas, A., Vogel, H., The unique antimicrobial peptide repertoire of stick insects, Developmental and Comparative Immunology (2019), doi: https:// doi.org/10.1016/j.dci.2019.103471. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
The unique antimicrobial peptide repertoire of stick insects Matan Shelomi1*, Chris Jacobs2, Andreas Vilcinskas3, Heiko Vogel2 1
Department of Entomology, National Taiwan University, Taipei, Taiwan Max Planck Institute for Chemical Ecology, Jena, Germany 3 Institute for Insect Biotechnology, Justus Liebig University of Giessen, Giessen, Germany
2
* Corresponding Author Matan Shelomi, [email protected] +886 0233665588 No 27 Lane 113 Sec 4 Roosevelt Rd, Taipei 10617 Taiwan Abstract The comparative analysis of innate immunity across different insect taxa has revealed unanticipated evolutionary plasticity, providing intriguing examples of immunity-related effector gene expansion and loss. Phasmatodea, the stick and leaf insects, is an order of hemimetabolous insects that can provide insight into ancestral innate immunity genes lost by later insect clades. We injected the stick insect Peruphasma schultei with a mixture of microbial elicitors to activate a strong immune response, followed by RNA-Seq analysis to screen for induced immunity-related effector genes. This revealed a highly diverse spectrum of antimicrobial peptides (AMPs) belonging to the attacin, coleoptericin, defensin, thaumatin, and tachystatin families. In addition, we identified a large group of short, cysteine-rich putative AMPs, some of which were strongly elicited. The immunity-related effector gene repertoire also included c-type and i-type lysozymes and several pattern-recognition proteins, such as proteins that recognize Gram-negative bacteria and peptidoglycans. Finally, we identified 45 hemolymph lipopolysaccharide-binding protein sequences, an unusually large number for insects. Taken together, our results indicate that at least some phasmids synthesize a broad spectrum of diverse AMPs that deserve further in-depth analysis.
1. Introduction Antimicrobial peptides (AMPs) are an ancient group of defense molecules, though the term is usually reserved for genetically-encoded and ribosomally-translated oligopeptides and proteins produced by higher eukaryotes as part of the innate immune system (Brodgen, 2005; Yi et al., 2014; Wu et al., 2018). Some are active against a broad range of pathogens, whereas others are specific towards Gram-positive or Gram-negative bacteria, fungi, parasites or viruses (Cytryńska et al. 2007). Insect AMPs can be grouped into three major structural classes: linear α-helical peptides lacking cysteine residues, peptides with a β-sheet globular structure stabilized by intramolecular disulfide bridges (e.g. defensins), and peptides that contain unusually high numbers of specific amino acid residues, such as proline or glycine. Secondary structures and disulfide bridges are often determinants of AMP activity (Brodgen, 2005; Bulet and Stocklin, 2005). Insects produce the largest known arsenal of AMPs (Yi et al., 2014). The number of AMPs differs widely between species, ranging from up to 50 in the invasive harlequin ladybird Harmonia axyridis (Vilcinskas et al., 2013) and the black soldier fly Hermetia illucens (Vogel et al., 2018), to zero in the pea aphid Acyrthosiphon pisum (Gerardo et al., 2010). These examples argue for considerable evolutionary plasticity in terms of the gain, loss, and neofunctionalization of AMP genes (Vilcinskas, 2013). A recent study identified evolutionarily-conserved and taxon-specific families, from the widespread and ancient defensins (Altincicek and Vilcinskas, 2007) to the more restricted gallerimycins, gloverins, heliomycins, lebocins and moricins, found only in the order Lepidoptera (Mylonakis et al., 2016; Rao et al., 2012). Most sequenced insect genomes and transcriptomes represent holometabolous species, whereas much less information is available for hemimetabolous insects, several of which have not been screened at all. Phasmatodea (the stick and leaf insects) is one such underexplored insect order. This group of obligate herbivores is part of the basal group of insects, the Polyneoptera, so studying phasmid AMPs may provide insight into ancestral innate immunity genes that were lost by later insect clades (Wipfler et al., 2019). Some phasmids are pests and others are invasive species, including the laboratory model Carausius morosus used for pioneering work on insect physiology (Headrick and Wilen, 2011). The only phasmid AMP sequence reported thus far was derived from C. morosus transcriptome data, but the study did not consider immune-challenged specimens so it is unlikely that the full spectrum of AMPs was expressed (Shelomi, 2017). In this investigation, we focused on the stick insect Peruphasma schultei (Pseudophasmatidae), a species regularly bred in captivity. We exposed P. schultei to a cocktail of microbial elicitors to trigger a strong immune response, and analyzed the transcriptome to identify putative immunity-related genes. Our goal was to explore the diversity of AMPs in a phasmid species and to identify any novel AMP groups that are not present in other insect orders.
2. Materials and Methods 2.1 Microbes and insects
We cultivated Escherichia coli DH5α in lysogeny broth and prepared stock suspensions at a concentration of 1 × 108 CFU/mL. The P. schultei specimens used in this study were obtained from an insectarium breeding source, kept at room temperature and reared on privet leaves (Ligustrum vulgare). 2.2 Immune challenge and transcriptome analysis The immune challenge experiment was carried out by injecting three female specimens of P. schultei with a microbial cocktail, while three untreated females were maintained under the same condition as controls. The microbial cocktail comprised 10 µL of the E. coli stock suspension described above (1 × 106 CFU) and 5 µL of an elicitor mix comprising heat-killed Micrococcus lysodeikticus ATCC 4698 (10 mg/mL), Saccharomyces cerevisiae zymosan A (10 mg/mL), S. cerevisiae peptidoglycan (10 mg/ml), Bacillus subtilis peptidoglycan (10 mg/mL) and E. coli lipopolysaccharide (LPS) (10 mg/ml) in Sf-900 II insect cell medium (Gibco). Use of multiple elicitors ensures the maximum number of potential immune effectors, accounting for cross-regulatory mechanisms. The insects were placed in separate containers and maintained in a rearing chamber with a 16-h photoperiod at 26°C. After 24 h (peak AMP induction based on preliminary qPCR tests, data not shown), the specimens were flash frozen and pulverized in liquid nitrogen. The resulting powder was stored in TriZol reagent (Zymo Research) at –80°C. RNA was extracted from each specimen using the Direct-Zol RNA MiniPrep kit with a DNase step (Zymo Research) or the RNeasy Kit (Qiagen). The quality of the extracted RNA was determined using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific) or an RNA nanochip on a 2100 Bioanalyzer (Agilent Technologies), following the manufacturers’ protocols. The RNA was sent on dry ice to the Max Planck Genome Centre in Cologne, Germany for poly(A) mRNA enrichment, TrueSeq RNA library production and sequencing on an Illumina HiSeq 2500 sequencer using paired-end (2 x 150 bp) read technology, yielding approximately 30 million reads for each sample. The libraries were quality tested, trimmed, and partially pooled for de novo assembly using CLC Genomics Workbench v10.1 (Qiagen). Transcriptome annotation by BLAST, Gene Ontology and InterProScan was implemented in the Blast2GO software suite (Conesa et al., 2005) as previously described (Jacobs et al., 2016). Transcriptome completeness was assessed by comparing the assembled P. schultei transcripts against a set of 1658 highly conserved single-copy insect orthologs retrieved from the OrthoDB v9.1 database using the BUSCO v3 pipeline (Waterhouse et al., 2017). 2.3 Mapping and differential gene expression analysis Digital gene expression analysis was carried out using CLC Genomics workbench v10.1 to generate BAM mapping files, and expression levels were estimated by sequence counting using previously described parameters for read mapping and normalization (Jacobs et al., 2016; Vogel et al., 2014). The log2 reads per kilobase per million reads (RPKM) values (normalized values based on geometric means of the biological replicate samples) were subsequently used to calculate fold-changes in expression between the control and immune-challenged samples. To control for the effect of global normalization using the RPKM method, we analyzed a number of highly-conserved housekeeping genes, including those encoding actin, glyceraldehyde 3-phosphate dehydrogenase
(GAPDH), the ribosomal proteins Rps7, Rps3a and Rpl27, elongation factor 1α, and eukaryotic translation initiation factors 4a and 5. The overall variation among these housekeeping genes was <1.2-fold, confirming the absence of differential expression. The protein-coding portions of putative immunity-related contigs were identified and translated using the ExPASy online tool (https://web.expasy.org/cgibin/translate/dna_aa) (Artimo et al., 2012), and were further characterized by using them as BLAST queries to search the NCBI nr database (Camacho et al., 2009). Contigs that were not identified as AMPs were excluded from further analysis. The presence of signal peptides and propeptide cleavage sites, which are expected in AMPs, was determined using ProP v1.0 (Duckert et al., 2004). Some contigs were immediately identified as AMPs, whereas others were given tentative annotations based on the presence of certain features, for example if the encoded product was cationic, rich in proline or glycine, had a molecular mass below 10 kD, and/or was secreted. All the putative AMPs were screened using the AMP prediction tool from the Collection of Antimicrobial Peptides (CAMPR3) (Waghu et al., 2015; 2016) (http://www.camp3.bicnirrh.res.in/predict/). We also identified transcripts representing other immunity-related proteins, including lysozymes (Callewaert et al., 2010), hemolymph LPS-binding proteins (Koizumi et al., 1997), and pattern-recognition proteins such as Gram-negative bacteria binding proteins (GNBPs) (Warr et al., 2008) and peptidoglycan recognition proteins (PGRPs) (Royet et al., 2011). To identify immunity-related genes that were not induced by this particular septic challenge, we also performed a reverse BLAST approach to search the assembly for any sequences similar to our identified AMPs and other known insect AMPs. The complete assembly of P. schultei transcriptome data, including all confident and tentative AMP annotations and other immunity-related proteins, are shown along with contig consensus sequences, Blast2GO hits against the nr database, hit accessions, putative annotations, and relative expression levels across the six RNA-Seq samples in Supplementary File S1. 2.4 Data submission The short read data described herein have been deposited in the EBI short read archive (SRA) with accession numbers ERS2752894–ERS2752899. The complete study can also be accessed directly using the following URL: http://www.ebi.ac.uk/ena/data/view/PRJEB28718. 3. Results and Discussion We assembled a high-quality de novo transcriptome of P. schultei from 112 million reads with an average length of 143 bp after trimming, resulting in 90,953 contigs (minimum contig size = 250 bp) with an N50 contig size of 1,469 bp, a maximum contig length of 19,754 bp and a GC content of 41.2%. Among the 90,953 total contigs with mapped reads, 22,523 were successfully annotated, excluding hypothetical or unidentifiable proteins. The combined transcriptome assembly yielded 85.5% complete and 5.6% missing BUSCO genes. The top-hit species distribution based on BLAST searching was all insects (Supplementary Fig. S1), and all contigs matched known insect sequences, including existing P. schultei transcriptome datasets (Shelomi et al., 2014; Misof et al., 2014), suggesting all of our sequences were endogenous to the phasmid and not microbial in origin.
We found that 14,839 of the contigs were differentially expressed between infected and uninfected specimens (p<0.1) with 9,541 showing significant differential expression at p<0.05, 3,062 at p<0.01, and 442 at p<0.001 (Supplementary Fig. S2; Supplementary File S1). Enriched GO terms associated with the groups of strongly and differentially expressed genes in the immune-challenged specimens and controls are shown in Supplementary Fig. S3, and the most-specific GO terms over- or underrepresented in the immune-induced samples are shown in Supplementary Fig. S4. GO terms such as "wound healing", "defense response", "response to gram-negative bacterium" and "lysozyme activity" are upregulated, all of which correlates well with a predominant immune-induction. Other enriched GO terms such as serine protease activity, serine protease regulation, and ATP-binding might also be indirectly involved in immune response or would healing, growth, etc (Makarova et al., 2016). The P. schultei transcriptome data revealed 19 “traditional” AMPs (Fig. 1) based on BLAST annotations, although the true number may be higher or lower depending on how the transcriptome assembler dealt with alleles of the same genes among our replicates. Eleven of these genes were significantly upregulated in the immune-challenged insects (adj. p<0.05) while the others were neither strongly nor differentially expressed in either the control or immune-challenged cohorts. All 19 sequences returned BLAST hits matching other insect AMPs. We checked two published P. schultei transcriptome shotgun assembly datasets registered under GenBank accession numbers GHOS00000000.1 (Shelomi et al., 2014) and GAWJ00000000.2 (Misof et al., 2014) using BLASTn, and found successful hits for most of these AMPs. Eight were identified as attacins and coleoptericin-like AMPs with closest matches to termite AMPs or the abovementioned C. morosus transcript, in a group that also included coleoptericin, diptericin, prolixin, and sarcotoxin. All were significantly upregulated by the immune challenge (Fig. 1). Attacins are glycine-rich AMPs composed of discrete N-terminal and C-terminal domains with distinct phylogenetic distributions among insect taxa. In line with our findings, only the C-terminal attacin domain is known from genomes representing holometabolous insects and in the orders Orthoptera (Locusta), Isoptera (Macrotermes) and Hemiptera (Rhodnius) (Asling et al., 1995; Mylonakis et al., 2016). In accordance with the predicted origin of cecropins within the subgroup of holometabolous insects (Mylonakis et al., 2016), we did not find any cecropin-like sequences in the P. schultei transcriptome assembly. Another five were identified as defensins, among which three were significantly upregulated by the immune challenge. Defensins have a β-sheet globular structure stabilized by intramolecular disulfide bonds (Zhao et al., 2015). This group of AMPs is widely distributed among insect taxa, and can even be found in apterygote insects (Altincicek and Vilcinskas, 2007; Johnston and Rolff, 2013) and ticks, suggesting it belongs to an ancient group of arthropod immunity-related effector genes (Tonk et al., 2015). Two tachysatins (a conotoxin-like protein and an agatoxin-like protein) and four thaumatin-like proteins were identified, none of which were significantly induced (Fig. 1). Tachystatins are broad-spectrum AMPs discovered in horseshoe crabs, and they are similar to funnel web spider venoms (Osaki et al., 1991). Like the conotoxins, they contain cysteine knots that confer exceptional stability, making them useful leads for medical applications (Daly and Craig, 2004). Thaumatins are large (≥200 amino acids),
primarily antifungal proteins discovered in plants but later found in animals including insects (Brandazza et al., 2004). Their distribution among insect taxa is best described as “scattered”, ranging from aphids (Gerardo et al., 2010) to termites and beetles (Terrapon et al., 2015; Altincicek et al., 2008), suggesting horizontal gene transfer from plants early in the evolution of the Ecdysozoa (Petre et al., 2011; Mylonakis et al., 2016). In addition to the 19 “traditional” AMPs, we also identified 55 putative AMPs, 33 of which were significantly induced and one of which was significantly repressed (Supplementary Fig. S5). These were selected because they are small, secreted proteins (mostly ~100 residues) each containing 8–12 cysteine residues, and 39 were predicted to be AMPs by CAMPR3, including the unique downregulated transcript. Seven of the putative AMPs featured a whey acidic protein four-disulfide core (WAP) domain (Supplementary Fig. S5). WAP proteins are cysteine-rich and are similar to antimicrobial peptides known as crustins and waprins, found mostly in crustaceans and snake venom, respectively (Nair et al., 2007; Smith et al., 2008; Arockiaraj et al., 2013). Among the seven WAP proteins, five were predicted to be AMPs by CAMPR3 and four of those were significantly elicited. We therefore identified 58 proteins with predicted AMP activity, of which 32 were also significantly differentially expressed in P. schultei following the injection of microbial elicitors. We also searched for non-AMP immunity-related transcripts and found four c-type (conventional) lysozymes, one of which was significantly induced, and six i-type (invertebrate) lysozymes, four of which were significantly induced and another significantly repressed following the immune challenge (Fig. 1). This matches the range of orthologs found in other insects such as the ladybird beetle Harmonia axyridis (Vogel et al., 2017). Insect c-type lysozymes act against Gram-positive bacteria by hydrolyzing the β(1-4) linkages between N-acetylglucosamine and N-acetylmuramic acid residues in bacterial cell wall peptidoglycans, and work in concert with other AMPs (Beckert et al., 2015). However, little is known about the role of i-type lysozymes in antimicrobial defenses (Beckert et al., 2016). The set of immunity-related genes in the P. schultei transcriptome can be expanded beyond AMPs and lysozymes to encompass those encoding pattern-recognition proteins, including proteins that recognize Gram-negative bacteria (GNBPs), peptidoglycans (PGRPs) and a remarkable spectrum of 45 LPS-binding proteins, which are extracellular c-type lectins that likely eliminate foreign microbes and intracellular symbionts that escape into the hemolymph. Thus far, such proteins were identified in the order Blattodea (cockroaches and termites) and the silkworm moth Bombyx mori (Koizumi et al., 1997; Jomori and Natori, 1991). Although LPS-binding proteins are likely more widespread in insects, we lack a comprehensive functional characterization of this gene family to confirm its specific role in innate immunity. In P. schultei we found nine non-induced GNBPs, three non-induced c-type lectins, and six PGRPs among which five were significantly induced (p<0.05) (Fig. 2). We also found 45 distinct LPS-binding proteins, four of which were significantly induced and one significantly repressed by the immune challenge (Supplementary File S1). In a screen for immune-mediated effectors, we identified three laccases (two significantly induced), two phenoloxidases (none induced), four Reeler-like defense proteins (none induced), two sequences homologous to the putative protozoan defense protein Hdd11 (one of which, containing 12 cysteine residues, was significantly induced)
(Bao et al., 2003), and one highly significantly (p<0.0001) induced protein that could not be annotated but is similar to an immunity gene in the migratory locust Locusta migratoria (GenBank accession number AJF38200.1). Lastly, we looked at the relative expression levels of toll-like receptors (TLRs), toll binding proteins and G-protein coupled receptors (GPCRs) identified in the Peruphasma schultei transcriptome after the injection of microbial elicitors. While these signal pathway genes were not the focus of this experiment and are usually less highly expressed than the immune response genes whose transcription they eventually activate (Tauszig et al., 2000), we still found several significantly induced by our immune challenge (Supplementary File S6). Taken together, our results show that the P. schultei immunity-related transcriptome encompasses a much broader spectrum of AMPs than other hemimetabolous insects. It remains unclear why stick insects would require such a large number of AMPs. There is accumulating evidence that insects protect and control their core microbiota (Login et al., 2011; Masson et al., 2016), encompassing beneficial microbes mediating adaptation to specific dietary challenges, against potentially harmful microbial species taken up with the diet (Shukla et al., 2018). Prior data suggests the stick insects do not have an obligate gut microbiome or any symbiotic associations with microbes (Shelomi et al. 2013, 2015), suggesting they have been free to evolve a wider diversity of AMPs without risk of harming or insufficiently protecting symbionts. The cysteine-rich peptides in particular show unprecedented diversification (Dimarcq et al., 1998) that deserves further investigation, i.e. for confirmation of their activity against microbial invaders. Given that phasmids are relatively long-lived herbivorous insects, our results form the basis of future studies to address the evolutionary ecology of their immune system. Acknowledgements The authors thank David Heckel (Max Planck Institute for Chemical Ecology) for support, and Dr Richard M Twyman for editing the manuscript. We also thank Daniel Dittmar (Max Planck Institute for Chemical Ecology) for the Peruphasma schultei specimens. AV acknowledges generous funding by the Hessen State Ministry of Higher Education, Research and the Arts (HMWK) via the ‘LOEWE Center for Insect Biotechnology and Bioresources’.
Appendix A. Supplementary data Supplementary data to this article can be found with the online version of the article. Research data was deposited with Mendeley Data and is available next to the article online.
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Figures and legends
Figure 1. Heat map showing the relative expression levels of AMPs and lysozymes identified in the Peruphasma schultei transcriptome after the injection of microbial elicitors. Fold changes in gene expression in the challenged insects are compared to controls on the right. Red arrows indicate upregulation and green arrows indicate downregulation of the corresponding contigs. Significant differences (p-values corrected for the false discovery rate) are shown with asterisks (*p < 0.05, **p < 0.01, ***p < 0.005). Genes of interest corresponding to the different antimicrobial peptide and lysozyme families are grouped. The housekeeping genes RPL27 and EF1α are used for normalization and are shown to confirm the uniform expression of these control genes across samples. The map is based on log2-transformed RPKM values shown in the gradient heat map (blue represents weakly-expressed genes, and red represents stronglyexpressed genes). CONTROL = control samples, INDUCED = challenged samples.
Figure 2. Heat map showing the relative expression levels of PGRPs and GNBPs identified in the Peruphasma schultei transcriptome after the injection of microbial elicitors. Fold changes in gene expression in the challenged insects are compared to controls on the right. Red arrows indicate upregulation and green arrows indicate downregulation of the corresponding contigs. Significant differences (p-values corrected for the false discovery rate) are shown with asterisks (*p < 0.05, **p < 0.01, ***p < 0.005). The map is based on log2-transformed RPKM values shown in the gradient heat map (blue represents weakly-expressed genes, and red represents strongly-expressed genes). CONTROL = control samples, INDUCED = challenged samples.
Supplementary Data
Supplementary Figure S1. Top BLAST hit distribution for contigs in the Peruphasma schultei de novo transcriptome assembly.
Supplementary Figure S2. Venn diagram of transcript distribution in the Peruphasma schultei de novo transcriptome assembly. The circles show the number of differentially expressed (p<0.05) contigs and the number of highly expressed contigs in the control insects and immune-challenged insects. The number of contigs with successful annotations is shown in parentheses. It is possible for a contig to be highly expressed in one of, neither of, or both groups of insects. It is possible for a differentially expressed contig to be highly expressed in both samples, in neither, or in the sample in which it is upregulated but not the other.
Supplementary Figure S3. Gene Ontology (GO) terms for differentially and highly expressed genes in the Peruphasma schultei de novo transcriptome assembly. The GO data produced using CLC Genomics was visualized with WEGO v2.0 at GO level 2 (http://wego.genomics.org.cn/).
High in INDUCED vs. CONTROL - Differential GO-term distribution Bar Chart % Seqs 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 ATP binding extracellular space serine-type endopeptidase activity carbohydrate binding calcium ion binding serine family amino acid metabolic process integral component of plasma membrane RNA-directed DNA polymerase activity activity
GO Name
negative regulation of peptidase activity defense response tyrosine metabolic process G protein-coupled receptor activity lysozyme activity metalloendopeptidase activity defense response to Gram-positive bacterium growth factor activity JNK cascade toll-like receptor signaling pathway peptidoglycan binding wound healing defense response to protozoan
Test Set
Reference Set
Supplementary Figure S4. Differential distribution of Gene Ontology (GO) terms. Bar chart shows the GO terms that were significantly (false discovery rate (FDR) < 0.05) enriched (over- or underrepresented) in the immune-induced samples. Charts were simplified to display only the most specific GO terms by removing parent terms representing existing child terms using the function “Reduce to most specific terms” in Blast2GO. Differences are shown as the percentage of sequences associated with a specific GO category in the test set (total number of contigs inducible in the challenged insects (min. 2fold induced; p < 0.05)) versus the reference set (transcriptome backbone assembly) using Fisher’s exact test in BLAST2GO-PRO.
Supplementary Figure S5. Heat map showing the relative expression levels of cysteinerich putative AMPs identified in the Peruphasma schultei transcriptome after the injection of microbial elicitors. Fold changes in gene expression in the challenged insects are compared to controls on the right. Red arrows indicate upregulation and green arrows indicate downregulation of the corresponding contigs. Significant differences (p-values
corrected for the false discovery rate) are shown with asterisks (*p < 0.05, **p < 0.01, ***p < 0.005). Genes of interest corresponding to cysteine-rich peptides, secreted peptides and WAP domain peptides are grouped. The housekeeping genes RPL27 and EF1α are used for normalization and are shown to confirm the uniform expression of these control genes across samples. The map is based on log2-transformed RPKM values shown in the gradient heat map (blue represents weakly-expressed genes, and red represents strongly-expressed genes). CONTROL = control samples, INDUCED = challenged samples.
Supplementary Figure S6. Heat map showing the relative expression levels of toll-like receptors, toll binding proteins and GPCRs identified in the Peruphasma schultei transcriptome after the injection of microbial elicitors. Fold changes in gene expression in the challenged insects are compared to controls on the right. Red arrows indicate upregulation and green arrows indicate downregulation of the corresponding contigs. Significant differences (p-values corrected for the false discovery rate) are shown with asterisks (*p < 0.05, **p < 0.01, ***p < 0.005). The housekeeping genes RPL27 and EF1α are used for normalization and are shown to confirm the uniform expression of these control genes across samples. The map is based on log2-transformed RPKM values shown in the gradient heat map (blue represents weakly-expressed genes, and red represents strongly-expressed genes). CONTROL = control samples, INDUCED = challenged samples. Supplementary File S1. Sequence, annotation and mapping file for the Peruphasma schultei transcriptome. Contig IDs, sequence length, contig consensus sequences, top BLAST hits (if any) against the nr database, hit accessions including accession number, E-value and percentage similarity, putative annotations, mapping results and relative expression levels across the six RNA-Seq samples are shown. The gene ontology (GO) terms were obtained from BLAST2GO.
Highlights • • • •
The stick insect Peruphasma schultei has a strong immune response to microbial challenges. Phasmatodea have a greater diversity of antimicrobial peptides than related insects. These include attacins, coleoptericins, defensins, thaumatins, and tachystatins. They express a large number of hemolymph lipopolysaccharide-binding proteins.
S0145-305X(19)30347-7
DOI:
https://doi.org/10.1016/j.dci.2019.103471
Reference:
DCI 103471
To appear in:
Developmental and Comparative Immunology
Received Date: 24 July 2019 Revised Date:
17 August 2019
Accepted Date: 18 August 2019
Please cite this article as: Shelomi, M., Jacobs, C., Vilcinskas, A., Vogel, H., The unique antimicrobial peptide repertoire of stick insects, Developmental and Comparative Immunology (2019), doi: https:// doi.org/10.1016/j.dci.2019.103471. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
The unique antimicrobial peptide repertoire of stick insects Matan Shelomi1*, Chris Jacobs2, Andreas Vilcinskas3, Heiko Vogel2 1
Department of Entomology, National Taiwan University, Taipei, Taiwan Max Planck Institute for Chemical Ecology, Jena, Germany 3 Institute for Insect Biotechnology, Justus Liebig University of Giessen, Giessen, Germany
2
* Corresponding Author Matan Shelomi, [email protected] +886 0233665588 No 27 Lane 113 Sec 4 Roosevelt Rd, Taipei 10617 Taiwan Abstract The comparative analysis of innate immunity across different insect taxa has revealed unanticipated evolutionary plasticity, providing intriguing examples of immunity-related effector gene expansion and loss. Phasmatodea, the stick and leaf insects, is an order of hemimetabolous insects that can provide insight into ancestral innate immunity genes lost by later insect clades. We injected the stick insect Peruphasma schultei with a mixture of microbial elicitors to activate a strong immune response, followed by RNA-Seq analysis to screen for induced immunity-related effector genes. This revealed a highly diverse spectrum of antimicrobial peptides (AMPs) belonging to the attacin, coleoptericin, defensin, thaumatin, and tachystatin families. In addition, we identified a large group of short, cysteine-rich putative AMPs, some of which were strongly elicited. The immunity-related effector gene repertoire also included c-type and i-type lysozymes and several pattern-recognition proteins, such as proteins that recognize Gram-negative bacteria and peptidoglycans. Finally, we identified 45 hemolymph lipopolysaccharide-binding protein sequences, an unusually large number for insects. Taken together, our results indicate that at least some phasmids synthesize a broad spectrum of diverse AMPs that deserve further in-depth analysis.
1. Introduction Antimicrobial peptides (AMPs) are an ancient group of defense molecules, though the term is usually reserved for genetically-encoded and ribosomally-translated oligopeptides and proteins produced by higher eukaryotes as part of the innate immune system (Brodgen, 2005; Yi et al., 2014; Wu et al., 2018). Some are active against a broad range of pathogens, whereas others are specific towards Gram-positive or Gram-negative bacteria, fungi, parasites or viruses (Cytryńska et al. 2007). Insect AMPs can be grouped into three major structural classes: linear α-helical peptides lacking cysteine residues, peptides with a β-sheet globular structure stabilized by intramolecular disulfide bridges (e.g. defensins), and peptides that contain unusually high numbers of specific amino acid residues, such as proline or glycine. Secondary structures and disulfide bridges are often determinants of AMP activity (Brodgen, 2005; Bulet and Stocklin, 2005). Insects produce the largest known arsenal of AMPs (Yi et al., 2014). The number of AMPs differs widely between species, ranging from up to 50 in the invasive harlequin ladybird Harmonia axyridis (Vilcinskas et al., 2013) and the black soldier fly Hermetia illucens (Vogel et al., 2018), to zero in the pea aphid Acyrthosiphon pisum (Gerardo et al., 2010). These examples argue for considerable evolutionary plasticity in terms of the gain, loss, and neofunctionalization of AMP genes (Vilcinskas, 2013). A recent study identified evolutionarily-conserved and taxon-specific families, from the widespread and ancient defensins (Altincicek and Vilcinskas, 2007) to the more restricted gallerimycins, gloverins, heliomycins, lebocins and moricins, found only in the order Lepidoptera (Mylonakis et al., 2016; Rao et al., 2012). Most sequenced insect genomes and transcriptomes represent holometabolous species, whereas much less information is available for hemimetabolous insects, several of which have not been screened at all. Phasmatodea (the stick and leaf insects) is one such underexplored insect order. This group of obligate herbivores is part of the basal group of insects, the Polyneoptera, so studying phasmid AMPs may provide insight into ancestral innate immunity genes that were lost by later insect clades (Wipfler et al., 2019). Some phasmids are pests and others are invasive species, including the laboratory model Carausius morosus used for pioneering work on insect physiology (Headrick and Wilen, 2011). The only phasmid AMP sequence reported thus far was derived from C. morosus transcriptome data, but the study did not consider immune-challenged specimens so it is unlikely that the full spectrum of AMPs was expressed (Shelomi, 2017). In this investigation, we focused on the stick insect Peruphasma schultei (Pseudophasmatidae), a species regularly bred in captivity. We exposed P. schultei to a cocktail of microbial elicitors to trigger a strong immune response, and analyzed the transcriptome to identify putative immunity-related genes. Our goal was to explore the diversity of AMPs in a phasmid species and to identify any novel AMP groups that are not present in other insect orders.
2. Materials and Methods 2.1 Microbes and insects
We cultivated Escherichia coli DH5α in lysogeny broth and prepared stock suspensions at a concentration of 1 × 108 CFU/mL. The P. schultei specimens used in this study were obtained from an insectarium breeding source, kept at room temperature and reared on privet leaves (Ligustrum vulgare). 2.2 Immune challenge and transcriptome analysis The immune challenge experiment was carried out by injecting three female specimens of P. schultei with a microbial cocktail, while three untreated females were maintained under the same condition as controls. The microbial cocktail comprised 10 µL of the E. coli stock suspension described above (1 × 106 CFU) and 5 µL of an elicitor mix comprising heat-killed Micrococcus lysodeikticus ATCC 4698 (10 mg/mL), Saccharomyces cerevisiae zymosan A (10 mg/mL), S. cerevisiae peptidoglycan (10 mg/ml), Bacillus subtilis peptidoglycan (10 mg/mL) and E. coli lipopolysaccharide (LPS) (10 mg/ml) in Sf-900 II insect cell medium (Gibco). Use of multiple elicitors ensures the maximum number of potential immune effectors, accounting for cross-regulatory mechanisms. The insects were placed in separate containers and maintained in a rearing chamber with a 16-h photoperiod at 26°C. After 24 h (peak AMP induction based on preliminary qPCR tests, data not shown), the specimens were flash frozen and pulverized in liquid nitrogen. The resulting powder was stored in TriZol reagent (Zymo Research) at –80°C. RNA was extracted from each specimen using the Direct-Zol RNA MiniPrep kit with a DNase step (Zymo Research) or the RNeasy Kit (Qiagen). The quality of the extracted RNA was determined using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific) or an RNA nanochip on a 2100 Bioanalyzer (Agilent Technologies), following the manufacturers’ protocols. The RNA was sent on dry ice to the Max Planck Genome Centre in Cologne, Germany for poly(A) mRNA enrichment, TrueSeq RNA library production and sequencing on an Illumina HiSeq 2500 sequencer using paired-end (2 x 150 bp) read technology, yielding approximately 30 million reads for each sample. The libraries were quality tested, trimmed, and partially pooled for de novo assembly using CLC Genomics Workbench v10.1 (Qiagen). Transcriptome annotation by BLAST, Gene Ontology and InterProScan was implemented in the Blast2GO software suite (Conesa et al., 2005) as previously described (Jacobs et al., 2016). Transcriptome completeness was assessed by comparing the assembled P. schultei transcripts against a set of 1658 highly conserved single-copy insect orthologs retrieved from the OrthoDB v9.1 database using the BUSCO v3 pipeline (Waterhouse et al., 2017). 2.3 Mapping and differential gene expression analysis Digital gene expression analysis was carried out using CLC Genomics workbench v10.1 to generate BAM mapping files, and expression levels were estimated by sequence counting using previously described parameters for read mapping and normalization (Jacobs et al., 2016; Vogel et al., 2014). The log2 reads per kilobase per million reads (RPKM) values (normalized values based on geometric means of the biological replicate samples) were subsequently used to calculate fold-changes in expression between the control and immune-challenged samples. To control for the effect of global normalization using the RPKM method, we analyzed a number of highly-conserved housekeeping genes, including those encoding actin, glyceraldehyde 3-phosphate dehydrogenase
(GAPDH), the ribosomal proteins Rps7, Rps3a and Rpl27, elongation factor 1α, and eukaryotic translation initiation factors 4a and 5. The overall variation among these housekeeping genes was <1.2-fold, confirming the absence of differential expression. The protein-coding portions of putative immunity-related contigs were identified and translated using the ExPASy online tool (https://web.expasy.org/cgibin/translate/dna_aa) (Artimo et al., 2012), and were further characterized by using them as BLAST queries to search the NCBI nr database (Camacho et al., 2009). Contigs that were not identified as AMPs were excluded from further analysis. The presence of signal peptides and propeptide cleavage sites, which are expected in AMPs, was determined using ProP v1.0 (Duckert et al., 2004). Some contigs were immediately identified as AMPs, whereas others were given tentative annotations based on the presence of certain features, for example if the encoded product was cationic, rich in proline or glycine, had a molecular mass below 10 kD, and/or was secreted. All the putative AMPs were screened using the AMP prediction tool from the Collection of Antimicrobial Peptides (CAMPR3) (Waghu et al., 2015; 2016) (http://www.camp3.bicnirrh.res.in/predict/). We also identified transcripts representing other immunity-related proteins, including lysozymes (Callewaert et al., 2010), hemolymph LPS-binding proteins (Koizumi et al., 1997), and pattern-recognition proteins such as Gram-negative bacteria binding proteins (GNBPs) (Warr et al., 2008) and peptidoglycan recognition proteins (PGRPs) (Royet et al., 2011). To identify immunity-related genes that were not induced by this particular septic challenge, we also performed a reverse BLAST approach to search the assembly for any sequences similar to our identified AMPs and other known insect AMPs. The complete assembly of P. schultei transcriptome data, including all confident and tentative AMP annotations and other immunity-related proteins, are shown along with contig consensus sequences, Blast2GO hits against the nr database, hit accessions, putative annotations, and relative expression levels across the six RNA-Seq samples in Supplementary File S1. 2.4 Data submission The short read data described herein have been deposited in the EBI short read archive (SRA) with accession numbers ERS2752894–ERS2752899. The complete study can also be accessed directly using the following URL: http://www.ebi.ac.uk/ena/data/view/PRJEB28718. 3. Results and Discussion We assembled a high-quality de novo transcriptome of P. schultei from 112 million reads with an average length of 143 bp after trimming, resulting in 90,953 contigs (minimum contig size = 250 bp) with an N50 contig size of 1,469 bp, a maximum contig length of 19,754 bp and a GC content of 41.2%. Among the 90,953 total contigs with mapped reads, 22,523 were successfully annotated, excluding hypothetical or unidentifiable proteins. The combined transcriptome assembly yielded 85.5% complete and 5.6% missing BUSCO genes. The top-hit species distribution based on BLAST searching was all insects (Supplementary Fig. S1), and all contigs matched known insect sequences, including existing P. schultei transcriptome datasets (Shelomi et al., 2014; Misof et al., 2014), suggesting all of our sequences were endogenous to the phasmid and not microbial in origin.
We found that 14,839 of the contigs were differentially expressed between infected and uninfected specimens (p<0.1) with 9,541 showing significant differential expression at p<0.05, 3,062 at p<0.01, and 442 at p<0.001 (Supplementary Fig. S2; Supplementary File S1). Enriched GO terms associated with the groups of strongly and differentially expressed genes in the immune-challenged specimens and controls are shown in Supplementary Fig. S3, and the most-specific GO terms over- or underrepresented in the immune-induced samples are shown in Supplementary Fig. S4. GO terms such as "wound healing", "defense response", "response to gram-negative bacterium" and "lysozyme activity" are upregulated, all of which correlates well with a predominant immune-induction. Other enriched GO terms such as serine protease activity, serine protease regulation, and ATP-binding might also be indirectly involved in immune response or would healing, growth, etc (Makarova et al., 2016). The P. schultei transcriptome data revealed 19 “traditional” AMPs (Fig. 1) based on BLAST annotations, although the true number may be higher or lower depending on how the transcriptome assembler dealt with alleles of the same genes among our replicates. Eleven of these genes were significantly upregulated in the immune-challenged insects (adj. p<0.05) while the others were neither strongly nor differentially expressed in either the control or immune-challenged cohorts. All 19 sequences returned BLAST hits matching other insect AMPs. We checked two published P. schultei transcriptome shotgun assembly datasets registered under GenBank accession numbers GHOS00000000.1 (Shelomi et al., 2014) and GAWJ00000000.2 (Misof et al., 2014) using BLASTn, and found successful hits for most of these AMPs. Eight were identified as attacins and coleoptericin-like AMPs with closest matches to termite AMPs or the abovementioned C. morosus transcript, in a group that also included coleoptericin, diptericin, prolixin, and sarcotoxin. All were significantly upregulated by the immune challenge (Fig. 1). Attacins are glycine-rich AMPs composed of discrete N-terminal and C-terminal domains with distinct phylogenetic distributions among insect taxa. In line with our findings, only the C-terminal attacin domain is known from genomes representing holometabolous insects and in the orders Orthoptera (Locusta), Isoptera (Macrotermes) and Hemiptera (Rhodnius) (Asling et al., 1995; Mylonakis et al., 2016). In accordance with the predicted origin of cecropins within the subgroup of holometabolous insects (Mylonakis et al., 2016), we did not find any cecropin-like sequences in the P. schultei transcriptome assembly. Another five were identified as defensins, among which three were significantly upregulated by the immune challenge. Defensins have a β-sheet globular structure stabilized by intramolecular disulfide bonds (Zhao et al., 2015). This group of AMPs is widely distributed among insect taxa, and can even be found in apterygote insects (Altincicek and Vilcinskas, 2007; Johnston and Rolff, 2013) and ticks, suggesting it belongs to an ancient group of arthropod immunity-related effector genes (Tonk et al., 2015). Two tachysatins (a conotoxin-like protein and an agatoxin-like protein) and four thaumatin-like proteins were identified, none of which were significantly induced (Fig. 1). Tachystatins are broad-spectrum AMPs discovered in horseshoe crabs, and they are similar to funnel web spider venoms (Osaki et al., 1991). Like the conotoxins, they contain cysteine knots that confer exceptional stability, making them useful leads for medical applications (Daly and Craig, 2004). Thaumatins are large (≥200 amino acids),
primarily antifungal proteins discovered in plants but later found in animals including insects (Brandazza et al., 2004). Their distribution among insect taxa is best described as “scattered”, ranging from aphids (Gerardo et al., 2010) to termites and beetles (Terrapon et al., 2015; Altincicek et al., 2008), suggesting horizontal gene transfer from plants early in the evolution of the Ecdysozoa (Petre et al., 2011; Mylonakis et al., 2016). In addition to the 19 “traditional” AMPs, we also identified 55 putative AMPs, 33 of which were significantly induced and one of which was significantly repressed (Supplementary Fig. S5). These were selected because they are small, secreted proteins (mostly ~100 residues) each containing 8–12 cysteine residues, and 39 were predicted to be AMPs by CAMPR3, including the unique downregulated transcript. Seven of the putative AMPs featured a whey acidic protein four-disulfide core (WAP) domain (Supplementary Fig. S5). WAP proteins are cysteine-rich and are similar to antimicrobial peptides known as crustins and waprins, found mostly in crustaceans and snake venom, respectively (Nair et al., 2007; Smith et al., 2008; Arockiaraj et al., 2013). Among the seven WAP proteins, five were predicted to be AMPs by CAMPR3 and four of those were significantly elicited. We therefore identified 58 proteins with predicted AMP activity, of which 32 were also significantly differentially expressed in P. schultei following the injection of microbial elicitors. We also searched for non-AMP immunity-related transcripts and found four c-type (conventional) lysozymes, one of which was significantly induced, and six i-type (invertebrate) lysozymes, four of which were significantly induced and another significantly repressed following the immune challenge (Fig. 1). This matches the range of orthologs found in other insects such as the ladybird beetle Harmonia axyridis (Vogel et al., 2017). Insect c-type lysozymes act against Gram-positive bacteria by hydrolyzing the β(1-4) linkages between N-acetylglucosamine and N-acetylmuramic acid residues in bacterial cell wall peptidoglycans, and work in concert with other AMPs (Beckert et al., 2015). However, little is known about the role of i-type lysozymes in antimicrobial defenses (Beckert et al., 2016). The set of immunity-related genes in the P. schultei transcriptome can be expanded beyond AMPs and lysozymes to encompass those encoding pattern-recognition proteins, including proteins that recognize Gram-negative bacteria (GNBPs), peptidoglycans (PGRPs) and a remarkable spectrum of 45 LPS-binding proteins, which are extracellular c-type lectins that likely eliminate foreign microbes and intracellular symbionts that escape into the hemolymph. Thus far, such proteins were identified in the order Blattodea (cockroaches and termites) and the silkworm moth Bombyx mori (Koizumi et al., 1997; Jomori and Natori, 1991). Although LPS-binding proteins are likely more widespread in insects, we lack a comprehensive functional characterization of this gene family to confirm its specific role in innate immunity. In P. schultei we found nine non-induced GNBPs, three non-induced c-type lectins, and six PGRPs among which five were significantly induced (p<0.05) (Fig. 2). We also found 45 distinct LPS-binding proteins, four of which were significantly induced and one significantly repressed by the immune challenge (Supplementary File S1). In a screen for immune-mediated effectors, we identified three laccases (two significantly induced), two phenoloxidases (none induced), four Reeler-like defense proteins (none induced), two sequences homologous to the putative protozoan defense protein Hdd11 (one of which, containing 12 cysteine residues, was significantly induced)
(Bao et al., 2003), and one highly significantly (p<0.0001) induced protein that could not be annotated but is similar to an immunity gene in the migratory locust Locusta migratoria (GenBank accession number AJF38200.1). Lastly, we looked at the relative expression levels of toll-like receptors (TLRs), toll binding proteins and G-protein coupled receptors (GPCRs) identified in the Peruphasma schultei transcriptome after the injection of microbial elicitors. While these signal pathway genes were not the focus of this experiment and are usually less highly expressed than the immune response genes whose transcription they eventually activate (Tauszig et al., 2000), we still found several significantly induced by our immune challenge (Supplementary File S6). Taken together, our results show that the P. schultei immunity-related transcriptome encompasses a much broader spectrum of AMPs than other hemimetabolous insects. It remains unclear why stick insects would require such a large number of AMPs. There is accumulating evidence that insects protect and control their core microbiota (Login et al., 2011; Masson et al., 2016), encompassing beneficial microbes mediating adaptation to specific dietary challenges, against potentially harmful microbial species taken up with the diet (Shukla et al., 2018). Prior data suggests the stick insects do not have an obligate gut microbiome or any symbiotic associations with microbes (Shelomi et al. 2013, 2015), suggesting they have been free to evolve a wider diversity of AMPs without risk of harming or insufficiently protecting symbionts. The cysteine-rich peptides in particular show unprecedented diversification (Dimarcq et al., 1998) that deserves further investigation, i.e. for confirmation of their activity against microbial invaders. Given that phasmids are relatively long-lived herbivorous insects, our results form the basis of future studies to address the evolutionary ecology of their immune system. Acknowledgements The authors thank David Heckel (Max Planck Institute for Chemical Ecology) for support, and Dr Richard M Twyman for editing the manuscript. We also thank Daniel Dittmar (Max Planck Institute for Chemical Ecology) for the Peruphasma schultei specimens. AV acknowledges generous funding by the Hessen State Ministry of Higher Education, Research and the Arts (HMWK) via the ‘LOEWE Center for Insect Biotechnology and Bioresources’.
Appendix A. Supplementary data Supplementary data to this article can be found with the online version of the article. Research data was deposited with Mendeley Data and is available next to the article online.
References
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Figures and legends
Figure 1. Heat map showing the relative expression levels of AMPs and lysozymes identified in the Peruphasma schultei transcriptome after the injection of microbial elicitors. Fold changes in gene expression in the challenged insects are compared to controls on the right. Red arrows indicate upregulation and green arrows indicate downregulation of the corresponding contigs. Significant differences (p-values corrected for the false discovery rate) are shown with asterisks (*p < 0.05, **p < 0.01, ***p < 0.005). Genes of interest corresponding to the different antimicrobial peptide and lysozyme families are grouped. The housekeeping genes RPL27 and EF1α are used for normalization and are shown to confirm the uniform expression of these control genes across samples. The map is based on log2-transformed RPKM values shown in the gradient heat map (blue represents weakly-expressed genes, and red represents stronglyexpressed genes). CONTROL = control samples, INDUCED = challenged samples.
Figure 2. Heat map showing the relative expression levels of PGRPs and GNBPs identified in the Peruphasma schultei transcriptome after the injection of microbial elicitors. Fold changes in gene expression in the challenged insects are compared to controls on the right. Red arrows indicate upregulation and green arrows indicate downregulation of the corresponding contigs. Significant differences (p-values corrected for the false discovery rate) are shown with asterisks (*p < 0.05, **p < 0.01, ***p < 0.005). The map is based on log2-transformed RPKM values shown in the gradient heat map (blue represents weakly-expressed genes, and red represents strongly-expressed genes). CONTROL = control samples, INDUCED = challenged samples.
Supplementary Data
Supplementary Figure S1. Top BLAST hit distribution for contigs in the Peruphasma schultei de novo transcriptome assembly.
Supplementary Figure S2. Venn diagram of transcript distribution in the Peruphasma schultei de novo transcriptome assembly. The circles show the number of differentially expressed (p<0.05) contigs and the number of highly expressed contigs in the control insects and immune-challenged insects. The number of contigs with successful annotations is shown in parentheses. It is possible for a contig to be highly expressed in one of, neither of, or both groups of insects. It is possible for a differentially expressed contig to be highly expressed in both samples, in neither, or in the sample in which it is upregulated but not the other.
Supplementary Figure S3. Gene Ontology (GO) terms for differentially and highly expressed genes in the Peruphasma schultei de novo transcriptome assembly. The GO data produced using CLC Genomics was visualized with WEGO v2.0 at GO level 2 (http://wego.genomics.org.cn/).
High in INDUCED vs. CONTROL - Differential GO-term distribution Bar Chart % Seqs 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 ATP binding extracellular space serine-type endopeptidase activity carbohydrate binding calcium ion binding serine family amino acid metabolic process integral component of plasma membrane RNA-directed DNA polymerase activity activity
GO Name
negative regulation of peptidase activity defense response tyrosine metabolic process G protein-coupled receptor activity lysozyme activity metalloendopeptidase activity defense response to Gram-positive bacterium growth factor activity JNK cascade toll-like receptor signaling pathway peptidoglycan binding wound healing defense response to protozoan
Test Set
Reference Set
Supplementary Figure S4. Differential distribution of Gene Ontology (GO) terms. Bar chart shows the GO terms that were significantly (false discovery rate (FDR) < 0.05) enriched (over- or underrepresented) in the immune-induced samples. Charts were simplified to display only the most specific GO terms by removing parent terms representing existing child terms using the function “Reduce to most specific terms” in Blast2GO. Differences are shown as the percentage of sequences associated with a specific GO category in the test set (total number of contigs inducible in the challenged insects (min. 2fold induced; p < 0.05)) versus the reference set (transcriptome backbone assembly) using Fisher’s exact test in BLAST2GO-PRO.
Supplementary Figure S5. Heat map showing the relative expression levels of cysteinerich putative AMPs identified in the Peruphasma schultei transcriptome after the injection of microbial elicitors. Fold changes in gene expression in the challenged insects are compared to controls on the right. Red arrows indicate upregulation and green arrows indicate downregulation of the corresponding contigs. Significant differences (p-values
corrected for the false discovery rate) are shown with asterisks (*p < 0.05, **p < 0.01, ***p < 0.005). Genes of interest corresponding to cysteine-rich peptides, secreted peptides and WAP domain peptides are grouped. The housekeeping genes RPL27 and EF1α are used for normalization and are shown to confirm the uniform expression of these control genes across samples. The map is based on log2-transformed RPKM values shown in the gradient heat map (blue represents weakly-expressed genes, and red represents strongly-expressed genes). CONTROL = control samples, INDUCED = challenged samples.
Supplementary Figure S6. Heat map showing the relative expression levels of toll-like receptors, toll binding proteins and GPCRs identified in the Peruphasma schultei transcriptome after the injection of microbial elicitors. Fold changes in gene expression in the challenged insects are compared to controls on the right. Red arrows indicate upregulation and green arrows indicate downregulation of the corresponding contigs. Significant differences (p-values corrected for the false discovery rate) are shown with asterisks (*p < 0.05, **p < 0.01, ***p < 0.005). The housekeeping genes RPL27 and EF1α are used for normalization and are shown to confirm the uniform expression of these control genes across samples. The map is based on log2-transformed RPKM values shown in the gradient heat map (blue represents weakly-expressed genes, and red represents strongly-expressed genes). CONTROL = control samples, INDUCED = challenged samples. Supplementary File S1. Sequence, annotation and mapping file for the Peruphasma schultei transcriptome. Contig IDs, sequence length, contig consensus sequences, top BLAST hits (if any) against the nr database, hit accessions including accession number, E-value and percentage similarity, putative annotations, mapping results and relative expression levels across the six RNA-Seq samples are shown. The gene ontology (GO) terms were obtained from BLAST2GO.
Highlights • • • •
The stick insect Peruphasma schultei has a strong immune response to microbial challenges. Phasmatodea have a greater diversity of antimicrobial peptides than related insects. These include attacins, coleoptericins, defensins, thaumatins, and tachystatins. They express a large number of hemolymph lipopolysaccharide-binding proteins.