Venus flytrap carnivorous lifestyle builds on herbivore defense strategies

The molecular composition of the trap

Although the Venus flytrap is uniquely adapted to circumvent malnutrition on poor soils, it still shows the typical morphological and gross physiological properties of a green plant. Nonetheless, Dionaea has evolved a highly specialized trap for hunting animals (Volkov et al. 2013), and profound morphological features distinguish the trap-forming leaf tip from the leaf base (petiole). These features can be attributed to trap-specific functions, namely, the need to catch and digest prey and, subsequently, the absorption of released nutrients. We hypothesized that these functional traits should relate to the nature and the abundance of transcripts specific to traps. Consequently, we compared the transcriptome of the trap with the overall transcriptomic landscape of D. muscipula, covering root, flower, and petiole tissue. Furthermore, we analyzed the transcriptomes of nonstimulated traps and glands versus those of traps and glands processing insects or stimulated with coronatine (COR), a molecular mimic of the phytohormone JA. In total, 6.5 billion reads (Supplemental Table S1) were produced using high-throughput RNA sequencing on the Illumina HiSeq platform. Following assembly, correction, and filtering, we found that the final transcriptome of the Venus flytrap comprised 51,196 potential genes. Assessment of the completeness of the reference transcriptome with Benchmarking Universal Single-Copy Orthologs (BUSCO) (Simao et al. 2015) revealed that 91% of all universal single-copy orthologs in the BUSCO's plantae benchmark set were present and complete. A conservative high-confidence ortholog assignment with CRB-BLAST (Aubry et al. 2014) resulted in 19,293 genes with a nonambiguous ortholog in the Arabidopsis thaliana reference genome.

By using this data set as a reference transcriptome, we aimed to unravel the molecular adaptations associated with prey capture and processing in the Dionaea leaf. Integrating data from flowers, roots, and petioles into this analysis, we identified general trends that characterize these organs using a principal component analysis (PCA) on variance-stabilized expression data for all 51,196 genes (Fig. 1A; Supplemental Table S2). The component with the largest spread (34.87%) separated the photosynthetically active petiole and trap from the root and the flower. The second largest spread component (22.08%) divided organs into reproductive (flower) and nonreproductive (trap, petiole, and root) organs. The third strongest component (15.50%) distinguished the trap from all the other tissues (Supplemental Fig. S1). Out of 2139 genes significantly contributing to the third component, only 540 (25,24%) were annotated with a gene ontology (GO) term. These genes were associated with responses to external stimuli and the generation of precursor metabolites and energy (Supplemental Table S2). Due to the pronounced ratio of nonannotated genes (74,76%), we could not further link the third component to other particular features at this point. To analyze the transcriptomic commonalities underlying the observed separations, respectively groupings, of the first three components more precisely, we identified differentially expressed genes (DEGs) in the root, trap, flower, and petiole using an all-versus-all pairwise comparison. From this analysis, we identified 17,715 out of the 51,196 genes as differentially expressed in at least one pairwise comparison, while 14,089 DEGs were detected in multiple different comparisons (Fig. 1B). A Venn analysis revealed 3626 organ-specific DEGs, with the highest number for the root (1470) followed by the flower (1113), petiole (655), and trap (388). However, flowers only exhibited organ-specific DEGs enriched for biological functions related to flower physiology, namely, anatomical structure morphogenesis, cell growth, and the cell cycle (Supplemental Table S3). Petioles, roots, and traps showed no obvious organ-specific signatures. The results of the PCA and the Venn analysis allowed us to speculate that the transcriptomic landscape of a nonstimulated trap is represented by a patchwork of genes that are also expressed in other organs. A global correlation measurement based on normalized expression counts demonstrated that the expression profile of nonstimulated traps strongly correlates with that of the petioles (Fig. 1C). Thus, our results provide the first transcriptome-wide molecular support for the common assumption that the trap is indeed a modified leaf.

Since the overall trap transcriptome integrates the expression profile of various typical leaf cell types, we could not exclude the existence of carnivorous signatures in certain trap-specific cell types such as glands. Considering that prey digestion and nutrient uptake are directly linked to the activity of about 37,000 glands on the inner surface of the trap (Juniper et al. 1989; Escalante-Perez et al. 2011), we generated a transcriptomic profile of isolated nonstimulated glands, as well as the trap rim, which is free of digesting glands, to clarify this issue. Correlating the resulting expression profiles of the gland, rim, root, and petiole revealed that the rim is most akin to the petiole, and glands showing highest correlation with the roots (Fig. 1D). Testing for differential expression in glands and roots compared with petioles revealed 7427 DEGs in glands and 6897 in roots. Both tissues shared 5013 DEGs exhibiting strong signals of transport, stress response, and protein metabolic processes, according to the GO. A detailed study of the underlying molecular functions using lower level MapMan bins (level 3) (Thimm et al. 2004), coupled with a generally applicable gene-set enrichment (GAGE) (Luo et al. 2009), revealed that DEGs from both tissues were enriched for gene sets involved in transcriptional regulation via AP2, C2H2, WRKY, and bHLH-like transcription factors, protein synthesis (ribosome biogenesis), protein modification, and degradation, as well as protein targeting along the secretory pathway (Supplemental Table S4). The observed similarity of glands and roots is therefore based on gene expression profiles that reflect the heterotrophic physiology and secretory capability of both tissues.

Gland functional morphology

Our finding that glands show a gene expression profile reminiscent of heterotrophic secreting tissues prompted us to analyze the gland structure in more detail (Fig. 2). Early morphological studies show that each gland is composed of 46 cells (Juniper et al. 1989). Transmission electron microscopy (TEM) shows the typical three-layer arrangement of a gland (Fig. 2C). Two basal cells are surmounted by four endodermoid (stalk) cells, comprising layer 3 (L3, blue). Overlying this is an inner layer (L2, brown) formed by eight cells, followed by an outer layer (L1, green) of 32 secretory cells that form the head of a mature gland (Lloyd 1942; Rea et al. 1983). All gland cells are connected by plasmodesmata, thus creating a metabolic and ionic cytoplasmic continuum. Both the cell wall and the cuticle of the glands are less developed and more permeable to solutes than are the common epidermal cells (Joel et al. 1983). In nonstimulated glands, the innermost stalk cells (L3) are tightly packed with oleosomes (Fig. 2F), implying that a triacylglycerol (TAG) reservoir represents the primary currency for energy consumptive processes. Supporting this observation is our finding that transcripts involved in TAG breakdown, including peroxisomal beta-oxidation, are highly expressed in glands (Supplemental Table S5). Highly abundant mitochondria in L2 cells (Fig. 2E) suggest that activated fatty acids provide the dominant resource for ATP generation at this site. The pronounced appearance of plasma membrane invaginations at the basal ends of L1 and in L2 cells (Fig. 2D,E) is indicative of an increased interface capacity for nutrient recognition and transport (see below). Finally, the existence of an expanded rough endoplasmic reticulum in L1 together with pronounced transcriptional activity in glands indicates that a highly dynamic protein biosynthesis and translocation machinery is present in the outer cell layer even when in the nonstimulated state (see Fig. 2D and below).

In nature, gland secretion is stimulated upon insect capture and, in the laboratory, by the application of JA or its molecular mimic COR (Radhika et al. 2010). Both stimuli result in gross ultrastructural changes in gland cells. In L1 cells, gland stimulation results in the dispersal of large apical vacuoles (Supplemental Fig. S2). Structural changes in head cells of the L2 layer are even more dramatic. Glands of insect digesting traps exhibit L2 cells containing large vacuoles (probably generated through vesicle fusions) and a heavily invaginated plasma membrane that completely covers the stimulated cells and is indicative of active secretion processes. In line with this observation, we have previously shown that upon stimulation with COR, glands change their surface-to-volume ratio by increasing their mean surface area by >30% (Escalante-Perez et al. 2011).

Active glands in secretion mode

To associate the ultrastructural changes in glands with cellular activities, we performed a comparative transcriptomic analysis of nonstimulated and active glands. Glands were stimulated with insects so as to activate secretion, and stimulated glands were mechanically separated from the inner trap surface 24 h after stimulus onset and subjected to transcriptome profiling. Stimulated glands show 3447 genes up-regulated and 2826 genes down-regulated. The majority of the up-regulated genes are indicative of highly active transport, signal transduction, and stress responses (Supplemental Table S6). In detail, transcriptional regulators for stress responses together with components of the secretory pathway, including pathogen-related proteins, cysteine, and serine proteases contributed to the strong stress signal.

The combination of profile-based signal peptide annotations with high-throughput screening (HTS) proteomics data from the digestive fluid of the “green stomach” allowed the Venus flytrap secretome to be characterized in more detail. Proteomics data were generated for traps stimulated with either insect, COR, or trigger hair bending (mechanical stimulation). Within the group of up-regulated, signal sequence containing transcripts, we identified 42 highly abundant DEGs. By using the HTS proteomic data set of the secretion fluid, we independently confirmed that these 42 candidate genes are indeed actively secreted (Fig. 3A, left; Supplemental Table S7). Among them, 15 proteins were secreted irrespective of the nature of the stimulus (COR, trigger hair stimulation, or insect). They encoded secretome-related proteins with hydrolase activity such as proteases, phosphatases, and chitinases, as well as defensin-like (DEFL) cysteine-rich proteins (Fig. 3A, right; Supplemental Table S7). In contrast, only 13 down-regulated DEGs had proteomic evidence together with an annotated signal peptide (Fig. 3A, denominators; Supplemental Table S7). Among these, we found transcripts encoding the Nepenthesin-like aspartic protease (Buch et al. 2015) and a Lipid Transfer Protein (LTP). Both these genes were already highly expressed in nonstimulated glands, but the majority of hydrolase genes showed a massive induction ranging from fivefold to 900-fold following stimulation (Fig. 3A, right).

To gain insights into the kinetics of the secretion process, we followed the time course of selected marker hydrolases in traps, namely, SAG12 and SCPL49 proteases using qPCR. COR-stimulated traps showed rapid transcriptional activation of hydrolases 1–2 h after stimulus onset. As Paszota et al. (2014) have already shown for VF CHITINASE I, SAG12 and SCPL49 hydrolase expression reached peak levels at 24–48 h after stimulation, irrespective of the nature of the stimulus (insect, trigger hair bending/mechanics, or COR) (Fig. 3B). To further investigate the functional interaction between mechano- and chemo-sensing during prey capture, we performed a kinetic analysis of hydrolase activation in response to two consecutive stimuli (Fig. 3C). Traps were first stimulated mechanically. This was followed by a second stimulus 8 h later, applied either as a second trigger hair stimulation or chemically by means of chitin application (a component of the insect coat). The first mechanical stimulation of the trap's trigger hairs resulted in a strong increase in VF CHITINASE I expression after 8 h (Escalante-Perez et al. 2011; Schulze et al. 2012; Scherzer et al. 2013). VF CHITINASE I expression remained elevated until 24 h but returned to its resting level after 48 h if no further stimulus was applied. In contrast, a second mechanical stimulation at 8 h further increased the already high VF CHITINASE I mRNA levels (413-fold) until 48 h after the initial stimulus. If the second stimulus was of a prey-associated chemical such as chitin, VF CHITINASE I expression after 48 h was boosted up to almost 2000-fold.

In noncarnivorous plants, chemical sensing often involves receptor-like kinases (RLKs), such as the chitin sensing LysM subfamily (Antolin-Llovera et al. 2012). By searching for members of the trap's sensory system, we identified 237 RLKs. Nonstimulated traps already differentially expressed 201 RLKs compared with petioles. Active traps and glands showed 63 and 134 RLKs differentially expressed, respectively, compared with their corresponding nonstimulated control (Supplemental Table S8). A Dionaea LysM-type chitin oligosaccharide-responsive CERK1-like kinase (Miya et al. 2007) was significantly up-regulated following gland insect stimulation, suggesting that glands might be able to assess the movement (mechanics), chemistry, and quality of the prey during progressive digestion and adjust the secretion process accordingly.

Trap insect processing builds on JA-based touch/wound signaling

Stimulus-induced hydrolase expression indicates that molecular pathways resembling defense and/or wounding responses accompany the carnivorous phenomenon. This hypothesis is corroborated by the strong stress-like response that prevails in stimulated glands (see above and Supplemental Table S6). To elucidate the system-wide response to insect feeding, we generated transcriptomic profiles of insect-activated traps, identifying 2137 genes as up-regulated, with 852 suppressed. Only a subset of 103 insect-stimulated DEGs were regulated in an insect-specific manner, and the majority of DEGs were differentially expressed in at least one other pairwise comparison of nonstimulated tissues.

Similar to the stimulated glands, insect-activated traps displayed a high transport activity together with a massive wounding response compared with nonstimulated tissues (Supplemental Table S9). Identified DEGs were mainly enriched for processes controlled or triggered by the stress hormones JA and abscisic acid, followed by ethylene, auxin, and salicylic acid. Corroborating previous findings that the wounding hormone JA plays an important role in carnivorous plants (Escalante-Perez et al. 2011; Nakamura et al. 2013; Libiakova et al. 2014), our analyses now demonstrate that not only JA biosynthesis but also its signaling components were differentially regulated. A detailed inspection showed that insect processing induces JA core components in traps (Fig. 4A; Supplemental Table S10), and a highly similar transcriptional response was triggered when the secretion-inducing JA mimic COR was applied to glands and traps (Supplemental Tables S11, S12).

To obtain more detailed insight into JA signaling in active traps, we performed quantitative PCR (qPCR) to follow the kinetics of the JA receptor COI1 (CORONATINE INSENSITIVE 1) (Fig. 4B) and its coreceptor JAZ1 (JASMONIC ACID ZIM DOMAIN 1) (Fig. 4C; Chini et al. 2007; Wasternack and Hause 2013). COI1 is moderately expressed in nonstimulated traps, but within 4 h of either COR or insect stimulation, it is rapidly and sustainably repressed (Fig. 4B). In contrast to COI1, JAZ1 expression transiently increases in stimulated traps (12-fold) (Fig. 4C), reaching peak expression after 4 h and remaining at sustained elevated levels for 24 h. Both COR and insects seem to activate the oxylipin pathway that leads to the formation of 12-oxo-phytodienoic acid (OPDA). Insects, however, further promote biosynthesis of JA-Ile, the true COI1 ligand (see Fig. 4A; Sheard et al. 2010). These results indicate that insects mechanically induce JA-Ile signals, thereby shifting glands into the secretion mode. This finding is further substantiated by our observation that the JA antagonist coronatine-O-methyloxime (COR-MO), which prevents COI1–JAZ interaction leading to JAZ degradation, represses the mechanical induction of hydrolase expression (Fig. 4D).

Furthermore, the set of insect-stimulated DEGs in traps was comprised of genes related to the production of reactive oxygen species (ROS) (Supplemental Table S13) scavengers (Mittler et al. 2004) and components of the ER-quality-control machinery (ER-QC) (Supplemental Table S13; Liu and Howell 2010; Williams et al. 2014). Both processes are related to oxidative stress management and are essential for controlling protein translation and maturation (Howell 2013). These signals likely originate in active glands characterized by persisting translation, folding, and targeting of extracellular hydrolases (see above), as well as plasma membrane receptors and transporters (see below). Remarkably, we found 38 genes that are predicted to negatively regulate the programmed cell death (PCD) prevalent in active traps (Supplemental Table S14). Among these, we identified Dionaea HEXOKINASE 1 (HXK1), a negative regulator of PCD in higher plants (Kim et al. 2006; Bruggeman et al. 2015). Likewise, we detected a Dionaea homolog of NECROTIC SPOTTED LESIONS 1 (NSL1), a known suppressor of cell death programs and defense responses (Noutoshi et al. 2006). Furthermore, the cyclic nucleotide gated (CNG) type channel DEFENCE-NO-DEATH-1 (DND1), a key player in pathogen defense responses and PCD in Arabidopsis (Xu and Brosche 2014), was up-regulated in active traps. Our findings suggest that ROS production is initiated by insect processing and that traps actively form a shield against detrimental ROS effects.

Accessing prey nutrients

When commencing digestion of the victim, metabolites and minerals containing the essential plant nutrients nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), sulfur (S), and magnesium (Mg) are released (Adamec 1997). Absorption of the products of digestion is mediated by glands aided by stimulus-induced formation of cuticular gaps (Robins and Juniper 1980; Joel et al. 1983). Accordingly, we observed that upon insect stimulation of traps or glands and the increasing availability of prey-derived nutrients, the expression profile of the Dionaea transportome changes dramatically. We identified 145 down-regulated and 148 up-regulated transcripts, which are classified as transporters according to the transporter classification (TC) system (Supplemental Table S15). By using all TC-database classified transcripts as background, we sought for the most significantly enriched class. As would be expected from a nutrient uptake system, secondary active-transporters represented the most significantly enriched class (GAGE q-value: 2.6 × 10⁻¹³). Regarding the subcellular localization of the corresponding transport proteins, we observed that the plasma membrane transporters were most strongly regulated (GAGE q-value: 3.5 × 10⁻⁸) within all differentially expressed transporters. On the contrary, plastid localized transporters (GAGE q-value: 1.3 × 10⁻⁹) appeared down-regulated, with the exception of the energy-supplying, plastidic ATP/ADP antiporter NTT1, which was strongly induced (Flügge et al. 2011).

Focusing on the substrate classes of the transporters, we found metal, nitrogen, and cation transporters to be the most regulated classes (Supplemental Table S16). We identified the highly induced plasma membrane phosphate transporters PHT1 and PHT2 (Shin et al. 2004). Consistent with the essential requirement of the molybdenum (Mo) cofactor in catalytic centers of several plant metabolic key enzymes, including ABA synthesis, a high-affinity molybdate transporter MOT1 (Tomatsu et al. 2007) was prominent in insect processing glands. Likewise, plasma membrane transporters for sulfate, as well as nitrogen-containing solutes, were induced by insects, suggesting that metabolites and macromolecules, such as amino acids, peptides, and nucleotides/DNA, all represent part of Dionaea’s diet. Furthermore, we found that the Dionaea ammonium transporter DmAMT1 is induced in active traps, as well as a Dionaea AKT1-like potassium channel and a HAK5-like high-affinity potassium transporter (Scherzer et al. 2015). When we analyzed the kinetics of transporter expression, it became apparent that upon trap stimulation, the expression of DmAMT1, DmHAK5, and DmHKT1 was induced 2–4 h after the onset of the stimulus, reaching peak expression after 12 h (Fig. 5A).

To test how the increased RNA abundance of DmAMT1, DmHAK5, and DmHKT1 translates into transporter activity, we stimulated Dionaea traps with COR and followed the time course of membrane potential changes of glands in response to NH₄⁺, K⁺, and Na⁺ feeding. Comparing the transporter transcription and gland plasma membrane depolarization in response to the cations tested, we found that transporter activity lagged >3 h behind gene expression (Fig. 5A). Detailed analysis using qPCR demonstrated that frequent firing of APs strongly triggered the expression of transporters involved in nutrient acquisition (Fig. 5B). As observed for hydrolases (see Fig. 4D), their transcriptional induction is inhibited upon blocking JA signaling downstream from APs by pretreating traps with the recently identified JA-antagonist COR-MO (Fig. 5B; Monte et al. 2014). Taken together, our findings suggest that stimulated glands translate hapto-electric signaling into JA-dependent transcriptional regulation of hydrolases and transporters. This ensures perfect gearing of secretion and prey digestion to the acquisition of prey-derived nutrients.

Active traps display conserved stress signals

When noncarnivorous plants are wounded by herbivores, they produce the touch hormone JA. Upon JA binding to the receptor/coreceptor complex COI1–JAZ1, this hormone signaling induces a local as well as a systemic electrical signal (Mousavi et al. 2013; Hedrich et al. 2016). This results in the production of substances toxic to herbivores or in making the plant meal indigestible. To test the hypothesis that the overall transcriptomic response of insect-stimulated Dionaea traps resembles defined stress-related responses known from noncarnivorous plants, we performed an interspecies comparison of active Dionaea plants to known coexpressed gene clusters in A. thaliana (Zaag et al. 2015). Taking advantage of the Complete Arabidopsis Transcriptome MicroArray (CATMA) gene expression reference database for Arabidopsis, we identified a diverse pattern of validated abiotic and biotic stress-related gene clusters (Supplemental Table S17) up-regulated in active traps. Further exploration of this response based on available Arabidopsis microarray data did indeed show that according to semantic similarities of GO enrichments, the Dionaea transcriptomic profile during insect capture and digestion closely resembles that of plants facing herbivore attack or wounding as opposed to fungal or bacterial infections (Fig. 6).

Plant growth

Dionaea plants were grown as described previously (Scherzer et al. 2013). Briefly, D. muscipula plants were purchased from CRESCO Carnivora and grown in plastic pots at 22°C in a 16-h:8-h light–dark photoperiod. All experiments were performed with healthy mature plants. Nonstimulated traps, petioles, roots, trap rims, and flowers were immediately frozen in liquid nitrogen and processed. Traps and glands were stimulated by feeding traps with live crickets or by spraying a 100 μM COR solution (Sigma-Aldrich) directly onto open traps. Tissues were harvested upon trap closure or secretion onset (within 4–12 h) and immediately frozen in liquid nitrogen. Isolation of secretory gland complexes was achieved by gently abrading the inner trap surface using a sharp razor blade. For mechanical induction of gene expression, trigger hairs were stimulated 60 times (1/min), and samples were collected 24 h after the first stimulus. In inhibitor tests, 100 µM COR-MO (synthesized according to the method described by Böhm et al. 2016b) was sprayed 4 h before mechanical stimulation was applied to the Venus flytraps.

RNA extraction, sequencing, and qPCR

RNA was isolated separately from each sample using a modified cetyltrimethylammonium bromide (CTAB)–based protocol. In brief, 0.1 g plant material powdered in liquid nitrogen was thoroughly mixed with 0.7 mL of hot (65°C) RNA-extraction buffer (2% CTAB, 2% polyvinylpyrrolidone K 25 [PVP], 100 mM TRIS/HCl at pH 8.0, 25 mM Na-EDTA at pH 8.0, 2 M NaCl, with 2.5% [v/v] 2-mercaptoethanol added immediately before use). Following 10 min incubation at 65°C and extraction with 1 vol of chloroform/isoamyl alcohol (24:1, v/v), RNA was precipitated from the supernatant by adding 175 µL of 8 M LiCl overnight (4°C). RNA was collected by centrifugation, resuspended in DEPC H₂O, and precipitated in the presence of 0.1 vol 3 M Na acetate (pH 5.2) and 2.5 vol of 96% EtOH. After a washing step using 70% EtOH, RNA was dissolved in 40 µL of DEPC H₂O. DNA contamination was removed by DNase I treatment on a column (Roche). RNA quantity and quality were determined by capillary electrophoresis (Experion automated electrophoresis system and Experion RNA high sense analysis kit, Bio-Rad Laboratories). TruSeq RNA libraries (partially stranded) were generated, and RNA sequencing was performed using an Illumina HiSeq 2000 sequencer at LGC Genomics.

Individual transcript levels were analyzed by quantitative real-time PCR (qPCR). qPCR was performed using a Realplex Mastercycler (Eppendorf), 1:20 diluted cDNA, and the ABsolute QPCR SYBR green capillary mix (Thermo Scientific). Expression levels were quantified using a standard for each primer pair and normalized to 10,000 molecules of actin (DmACT) cDNA transcripts.

Primers employed were as follows: SCPL49LCfwd, 5′-ACTTAATCCGGGTATCA-3′; SCPL49LCrev, 5′-AGGTCCATAGGTATTCA-3′; VF CHITINASE ILCfwd, 5′-GAAAGTTATTACGGTCG-3′; VF CHITINASE ILCrev, 5′-CTTTACCACACTCAACG-3′; SAG12fwd, 5′-CGCATTCGAGTATATGA-3′; SAG12rev, 5′-CAACATTCCTTTGCATC-3′; AMT1LCfwd, 5′-TTGCT ACCAAGAAACAC-3′; AMT1LCrev, 5′-TGAGTTGATGTAAGGAG-3′; HAK5.1LCfwd, 5′-GTATGTTT TGTGCTGG-3′; HAK5.1Lrev, 5′-GTCCGAGCATATAAGTC-3′; HKT1LCfw, 5′-GAATGTAGCAGT CGAG-3′; HKT1LCrev, 5′-CCCGTGCATGTTAAAT-3′; JAZ1.1LCfwd, 5′-GTGTTCAACGACTTCC-3′; JAZ1.1LCrev, 5′-TTGTTAAGGTGTATGGC-3′; COI1LCfwd, 5′-TTACGTCGGACTGTAT-3′; and COI1LCrev, 5′-AACTCTAAGCTAAGACAT-3′.

Data preparation and transcriptome assembly

Raw read data sets were screened for quality issues with FastQC (version 0.11.4). Passed data sets were quality trimmed using skewer (version 0.1.67; -Q 30; -q 30; -l 75; -m pe) (Jiang et al. 2014) The transcriptome was assembled using Trinity (release 2013-02-16; -jaccard_clip; -min_kmer_cov 2; -path_reinforcement_distance 75) (Grabherr et al. 2011). The assembly was screened for artificial fusion events caused by low-complexity regions or highly similar UTRs. A linkage map was constructed for transcripts with more than one potential coding region (see below). Evidence from homology-based database searches and mapped paired-end reads were used to link the potential coding regions. Paired-end mappings were produced with Bowtie (version 0.12.7) (Langmead et al. 2009), while homology evidence was generated by searching transcripts against a plant-comprising subset of the UniProt database (2015; release 2014-06-19) (UniProt-Consortium 2015) using BLAST (version 2.2.29) (Camacho et al. 2009). Where no linkage evidence was found, the transcript was considered to be fused. Fusion sites were detected by searching for the lowermost covered site between two coding regions. Transcripts were cut apart, and the region with the lowermost coverage was trimmed from both the resulting transcripts. Whenever a transcript was defused, the original isoform-gene relation returned by Trinity was disregarded. To recreate a reliable isoform–gene relation, we partitioned all defused isoforms derived from the same gene using transitivity clustering (version 1.0) (Wittkop et al. 2010) into new genes. If sufficient evidence for a linkage was found, the transcripts were considered intron containing. Introns were removed by aligning high-scoring templates from the homology search to the transcript using GeneWise (version 2.4.1) (Birney et al. 2004). Only proper alignments were used to cut introns from transcripts; otherwise, transcripts were left untouched.

Feature annotation

Refined transcripts were annotated using homology- and profile-based methods. Coding regions were identified using TransDecoder (http://transdecoder.github.io; release 2014-01-16). Putative peptides were preferred when they had a significant match to a Pfam domain. Only peptides with a length of at least 30 aa were considered. Protein families and domains were classified using InterProScan (release 44.0) Quevillon et al. 2005). Phobius (version 1.01) (Kall et al. 2004), SignalP (version 4.0) (Petersen et al. 2011), and TMHMM (version 2.0) (Moller et al. 2001) were integrated into the default signature recognition methods. GO terms were predicted using Blast2GO (version 2.5.2) (Conesa et al. 2005). GO terms were augmented with Interpro annotations using ANNEX (Myhre et al. 2006). MapMan bins were annotated with Mercator (Lohse et al. 2014). Interspersed repeats and low-complexity regions were identified using RepeatMasker (version 4.0.3) (Smit et al. 2013–2015). Putative orthologs between the Venus flytrap transcriptome and A. thaliana (“TAIR10”) (Lamesch et al. 2012) were assigned using a conservative conditional reciprocal best BLAST (CRB-BLAST; release 2015-05-19) (Aubry et al. 2014). TCDB classifications were assigned with a two-step approach. First, a CRB-BLAST against the TCDB sequence set was used to identify conditional reciprocal best hits. If no distinct one-to-one hit was found, the sum of the best hits (e-value ≤10⁻⁵) was evaluated. If all hits were annotated with the same TCDB family, the annotation was mapped to the Venus flytrap sequence.

Transcript filtering

Prior to differential expression analyses and enrichment studies, the following filtering steps were applied to the refined assembly: (1) Noncoding RNAs were excluded by searching for isoforms without a potential coding region; (2) ambiguous low abundance genes were excluded when no sequenced sample produced an expected count higher than five; (3) in addition, ambiguous isoforms were removed by only considering isoforms with an abundance >1% of the abundance of its parental gene; and (4) possible contaminations were filtered with a two-step approach. Isoforms were split into overlapping k-mers (k-mer size 19 bp). Each k-mer was then searched in a database of trusted k-mers created from genomic sequencing data (data not shown). Isoforms without a single k-mer present in the trusted k-mer databases were disregarded. The remaining isoforms were searched against the complete nonredundant database (release 2015-01-13) (Sayers et al. 2009) using BLASTN. Resulting hits were taken to calculate the lowest common ancestor (LCA) with MEGAN4 (Huson et al. 2011), using default settings. Isoforms with a LCA in bacteria, fungi, or metazoa were disregarded. (5) Transposon-like isoforms detected by RepeatMasker were excluded if they only contained the transposable element or a protein domain associated with interspersed repeats in the current RepBase release (release 2014-04-2) (Jurka et al. 2005).

Transcript abundance estimation and differential expression testing

Isoform and gene abundances were quantified with RSEM (version 1.2.5) (Li and Dewey 2011) using the unfiltered read data sets. Differential expression tests and enrichment analysis, as well as abundance visualizations, were carried out using gene counts. Isoform abundances were only used to select a representing isoform for genes of interest. The manual selection prioritized isoforms over others that were complete (presence of ORF with flanking UTRs), and had a protein domain annotation and an expression profile similar to its parental gene. Gene count matrices for each experiment were normalized using the normalization method implemented in the DESeq package (version 1.22.0) (Anders and Huber 2010). Samples that were possible outliers were detected using arrayQualityMetrics (version 3.26.0) (Kauffmann et al. 2009) and a variance stabilized transformation of the normalized gene count data set. The same expression data set was used for the PCA. DEGs were detected using DESeq. Only genes exhibiting an adjusted P-value equal or smaller than 0.01 were considered as significant. DEGs for A. thaliana microarray experiments (GSE48676, GSE49981, GSE5520, and GSE50526) were detected using GEO2R (Barrett et al. 2013).

Enrichment analysis and semantic similarity

GO and GOSlim enrichment studies were carried out using topGO (version 2.22.0) (Alexa et al. 2006) using the generic GO and the plant slim subset developed by The Arabidopsis Information Resource (“TAIR”). Measures for differential expression (P-value) were included using the weighted algorithm, while Fisher's exact test was used as a test statistic. Terms were considered significant with a P-value equal or smaller than 0.01 after Bonferroni correction. Semantic similarity of GO enrichments was measured with the GNU R Bioconductor package GOSemSim (Yu et al. 2010) using Wang's measurement method and the best-match average (BMA) strategy to combine scores for individual terms. Gene set enrichments (GSEAs) were carried out with GAGE (Luo et al. 2009) using MapMap bins, TCs, or manually assigned gene classifications. DESeq derived adjusted P-values were used as per gene score, and gene sets were considered significant at a q-value equal or lower than 0.1.

Definition of the putative secretome and transportome

The secretome of the Venus flytrap was defined with the following rule set. Each putative member had to be a DEG when comparing nonstimulated with insect-stimulated glands (both down- and up-regulated DEGs were considered). The underlying gene needed to have at least one differentially expressed isoform with an annotated signal peptide and evidence of a detectable peptide in the same open reading frame. The transportome was defined similar to the secretome. Putative members had to be differentially expressed in nonstimulated or insect-stimulated glands. The gene needed to have at least one differentially expressed isoform annotated with two or more transmembrane domains. Furthermore, the same sequence needed to have a proper TCDB classification as described above.

Proteomics

Electron microscopy

Plant material was cut into small pieces and immediately fixed in 5% glutaraldehyde in a 0.1 M cacodylate buffer for 3 h at 4°C. Pieces were washed twice, first with 7% sucrose in a 0.1 M cacodylate buffer and then with a 0.1 M cacodylate buffer alone. For post-fixation, the solution was replaced by 2% osmium tetroxide in a 0.1 M cacodylate buffer for 2 h on ice. Following a three-stage washing step with 0.05 M cacodylate buffer, the plant material was dehydrated using an ascending ethanol series and then stored in 100% ethanol overnight at RT. After two 1-h incubations at RT in 100% ethanol and propylene oxide, respectively, the plant material was again stored overnight in fresh propylene oxide at RT. The next day, it was equilibrated with and subsequently embedded in Spurr's epoxy resin (Spurr 1969). After changing the solution several times using an increasing ratio of Spurr:propylene oxide, plant pieces were transferred to appropriate embedding molds filled with 100% Spurr. Contrasted ultrathin sections (60–70 nm) of the embedded plant material were examined using a JEOL JEM-2100 TEM.

Electrophysiology

Prior to measurements, the lobe of a cut trap was glued to the bottom of a recording chamber and left to recover for 30 min in a standard solution containing 0.1 mM KCl, 10 mM CaCl₂, and 15 mM MES/TRIS (pH 6). Osmolarity was adjusted to 240 mOsm/kg using D-sorbitol. Membrane potential recordings were performed as described elsewhere (Scherzer et al. 2015) using a standard solution supplemented with varying concentrations of either NaCl, KCl, or NH₄Cl as indicated in the figure legend.

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