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A transcriptomic insight into the infective juvenile stage of the insect parasitic nematode, Heterorhabditis indica



Nematodes are the most numerous animals in the soil. Insect parasitic nematodes of the genus Heterorhabditis are capable of selectively seeking, infecting and killing their insect-hosts in the soil. The infective juvenile (IJ) stage of the Heterorhabditis nematodes is analogous to Caenorhabditis elegans dauer juvenile stage, which remains in ‘arrested development’ till it finds and infects a new insect-host in the soil. H. indica is the most prevalent species of Heterorhabditis in India. To understand the genes and molecular processes that govern the biology of the IJ stage, and to create a resource to facilitate functional genomics and genetic exploration, we sequenced the transcriptome of H. indica IJs.


The de-novo sequence assembly using Velvet-Oases pipeline resulted in 13,593 unique transcripts at N50 of 1,371 bp, of which 53 % were annotated by blastx. H. indica transcripts showed higher orthology with parasitic nematodes as compared to free living nematodes. In-silico expression analysis showed 30 % of transcripts expressing with ≥100 FPKM value. All the four canonical dauer formation pathways like cGMP-PKG, insulin, dafachronic acid and TGF-β were active in the IJ stage. Several other signaling pathways were highly represented in the transcriptome. Twenty-four orthologs of C. elegans RNAi pathway effector genes were discovered in H. indica, including nrde-3 that is reported for the first time in any of the parasitic nematodes. An ortholog of C. elegans tol-1 was also identified. Further, 272 kinases belonging to 137 groups, and several previously unidentified members of important gene classes were identified.


We generated high-quality transcriptome sequence data from H. indica IJs for the first time. The transcripts showed high similarity with the parasitic nematodes, M. hapla, and A. suum as opposed to C. elegans, a species to which H. indica is more closely related. The high representation of transcripts from several signaling pathways in the IJs indicates that despite being a developmentally arrested stage; IJs are a hotbed of signaling and are actively interacting with their environment.


Nematodes are the most abundant metazoans on earth and show remarkable diversity in their ecological and feeding habits [1]. Although notorious as parasites and pathogens of humans, animals, and plants, the majority of nematodes are beneficial to us as they recycle nutrients in soils and oceans [1, 2]. Another beneficial nematode group known as entomopathogenic nematodes (EPNs) encompass two genera, Steinernema, and Heterorhabditis. These EPNs symbiotically associate with gram-negative gammaproteobacteria, Xenorhabdus, and Photorhabdus, respectively [3]. Because of their ability to kill insects rapidly and amenability to mass production, they are widely used for the biological control of the insect pests of crops [46]. The EPNs are models to study animal-microbe symbiosis [710], nematode parasitism [11] and ecology [12, 13].

The infective juvenile (IJ) stage of the Heterorhabditis spp. is a developmentally arrested stage analogous to the dauer stage of the C. elegans [14], and infective L3 stage of many animal parasitic nematodes [15]. IJs are the only EPN stage found in nature outside the insect-host, and are capable of surviving tough environmental conditions in the soil for long periods of time. The nematodes in the IJ stage do not feed or grow until they find a new insect-host, and they possess a remarkable ability to actively search, follow and infect their insect-host in the soil environment [16, 17]. IJs are known to show different kinds of parasitic behaviors [18]. They can be desiccated to quiescence or frozen in liquid nitrogen [19, 20], and then be revived back to life. Thus, there is a possibility to extend the lifespan/delay life cycle. Because of this remarkable environmental toughness of the IJs, all the EPN formulations, presently available in the market, are based on this stage. An extensive body of research exists on the genes, pathways, and processes involved in aging in the free-living nematode, C. elegans [2123]. A similar understanding of genes that increase the lifespan in EPNs would be directly beneficial in extending the shelf-life of EPN IJs, and IJ based formulations to improve their use as a pest control product [2426].

Genomic tools and technologies have allowed the researchers to uncover the amazing biology of nematodes [2729]. The genome of the EPN, Heterorhabditis bacteriophora TTO1-M31e strain has been sequenced [30] and is available in the public domain. Additionally, the expressed sequence tags (ESTs) of H. bacteriophora GPS-11 strain [31, 32] and transcriptome of the adult stage of H. bacteriophora TTO1-M31e were published earlier [33]. Large amount of information is available on molecular biology of the dauer/developmentally arrested L2 and L3 stages of various nematodes, such as free-living C. elegans and C. briggsae [3438], insect-associated Pristionchus [39, 40], animal parasitic Strongyloides stercoralis [41] and Ostertagia ostertagi [42] and many plant parasitic nematodes [4348]. However, such information is completely lacking for IJ stage of EPNs. Scanty information available on the Heterorhabditis IJ ‘recovery’ is not adequate to decipher the various molecular and physiological pathways specific to these IJs [33, 49]. Additionally, it is suggested that genes expressed in survival or dispersal stages in nematodes, such as dauer, and EPN IJs, are more likely to be novel, compared with the genes expressed in adult or larval stages [29].

H. indica was the first species of this genus recorded from India [50]. Since then, various surveys showed that H. indica is the most predominant species of Heterorhabditid nematode in India and is found in almost all the geographical parts of the country. Therefore, H. indica is naturally suitable for incorporation in insect biological control programs in India. In the present study, the transcriptomic analysis of the IJ stage of H. indica was carried out to understand the molecular processes and pathways active at this stage, and to create a resource for further functional genomics and genetic investigations.


Transcriptome sequencing and assembly

The mRNA sequencing of IJ stage of H. indica using the Illumina GAIIx platform yielded about 51.2 million reads of 100 base read-lengths generating 64x coverage. After quality filtering, 42.3 million high-quality reads totalling 4.2 gigabases of data were obtained. The de-novo sequence assembly was carried out by Velvet at different k-mer lengths (51–93 with step size of 4) with minimum contig length of 200. The optimal assembly was attained at k-mer 83 which resulted in 18,710 contigs with 909 bp N50 (Table 1). Merging of transcripts from 71 to 83 k-mer range by Oases resulted in 23,827 transcripts with 1,292 bp N50 size. Removing duplicates by cd-hit-est, and filtering out < 300 bp transcripts resulted in 13,593 unique transcripts with N50 of 1,371 bp (Table 1). Total of 13,592 proteins were predicted by ORFPredictor [51] which were then used for downstream analysis.

Table 1 Assembly statistics of H. indica transcriptome generated by Velvet and Oases

Characterization of H. indica transcripts

The blastx analysis of H. indica transcripts resulted in annotation of 7,246 transcripts (Additional file 1: Table S1a), of which 6,320 hits matched to animal and plant parasitic, as well as free-living nematodes i.e. A. suum (2,763 hits), Ancylostoma ceylanicum (741 hits), Haemonchus contortus (558 hits), Loa loa (466 hits), Brugia malayi (397 hits), Wucheria bancroftii (357 hits), C. elegans (269 hits), C. brenneri (193 hits), Heterodera glycines (167 hits), C. remanei (153 hits), C. briggsae (141 hits), H. avenae (67 hits), M. incognita (35 hits), Bursaphelenchus xylophilus (13 hits) (Fig. 1a). Due to absence of H. bacteriophora hits in the blastx results, we performed a standalone blastx of H. indica transcripts against H. bacteriophora protein dataset (PRJNA13977) downloaded from the wormbase ( The blastx resulted in 2,745 protein hits (Fig. 1b, Additional file 2: Table S1b).

Fig. 1

a Distribution of the top 10 nematode species with most homologs to Heterorhabditis indica. The distribution was calculated using best blastx hits. b. Venn diagram of H. indica transcripts matching H. bacteriophora proteins in a standalone blast

Comparison of the transcripts with complete genomes of other closely related rhabditid nematodes through reciprocal blast approach showed 3,364 orthologs of C. elegans, 3,103 of C. briggsae, 3,171 of C. remanei, 2,164 of P. pacificus and 346 of H. bacteriophora (Fig. 2a). However, higher numbers of orthologs were identified when the transcripts were compared to the animal parasitic nematodes-9,685 orthologs in A. suum, 6,819 in Strongyloides ratti while other parasites like Meloidogyne hapla, M. incognita, B. malayi and Trichinella spiralis ranked in between these two nematodes (Fig. 2b).

Fig. 2

a H. indica orthologs present in selected completely sequenced genomes of free-living nematodes C. elegans, C. remanei, C. briggsae, Pristionchus pacificus and Heterorhabditis bacteriophora. b H. indica orthologs in animal and plant parasitic nematodes

Putative functional classification using gene ontology and KEGG pathway analysis

All the transcripts were further functionally characterized into GO categories such as molecular functions, biological processes and cellular components. GO terms were assigned to 8,124 transcripts (Table 2, Fig. 3) of which 49.6 % (4,027) belonged to the binding category (GO:0005488) and 40.5 % (3,293) belonged to the catalytic activity of the molecular functions group (GO:0003824). Protein binding and nucleotide binding subcategories contributed 16.5 and 15.1 %, respectively, in the binding category, whereas hydrolase (14.1 %) and transferase (11.8 %) were the two most dominant subcategories in catalytic activity. The transcription regulator activity (GO:0030528) and translation regulator activity (GO:0045182) contributed 2.5 % and 0.7 % transcripts, respectively. In the biological process, 42.7 % (3,466) transcripts were grouped under metabolic processes (GO:0008152), and 40.5 % (3,293) under cellular processes (GO:0009987) (Table 2, Fig. 3). Other categories were biological regulation (GO:0065007; 9.4 %) transcripts, and stimulus (GO:0050896; 1.9 %) transcripts. Interestingly, developmental process (GO:0032502) showed only 0.2 % of the genes, while two transcripts for immune system process (GO:0002376), and one transcript each for reproduction (GO:0000003) and reproductive processes (GO:0022414) were obtained. Within the cellular component category, cell (GO:0005623;29.1 %), and organelle (GO:0043226;12.1 %) showed the maximum number of hits (Table 2).

Table 2 Gene ontology analysis of proteins, conceptually translated from contigs of H. indica
Fig. 3

GO term analysis for all predicted proteins in IJ transcriptome of H. indica

The transcripts were analysed to identify the key metabolic pathways and processes of which 4,738 proteins were mapped to various pathways (Table 3). The 60 most represented pathways included signaling pathways like PI3K-Akt, MAPK, Rap1, Ras, insulin, FoxO, AMPK, cAMP, Wnt, Hippo, chemokine, neurotrophin, sphingolipid, oxytocin, thyroid hormone, cGMP-PKG, and signaling pathways regulating pluripotency of stem cells (Table 3). Transcripts that were mapped to all the pathways in H. indica IJs are represented in Fig. 4.

Table 3 The sixty pathways most represented in the transcriptome of H. indica IJs
Fig. 4

Metabolic pathways active in H. indica infective juveniles as revealed by the transcriptomic analysis using iPATH2 interactive pathway explorer

The transcripts were also analyzed using the EuKaryotic Orthologous Groups (KOG) and Protein K(c)lusters (PRK) databases. The results of the analysis are presented in Additional file 1: Table S1. The KOG analysis is a eukaryote-specific version of the Clusters of Orthologous Groups (COG) tool for identifying ortholog and paralog proteins. Broadly, 1,519 transcripts were classified to signal transduction (KOG function ID-T), 985 to transcription (KOG function ID-K), 747 to translation, ribosomal structure and biogenesis (KOG function ID-J), 566 to RNA processing and modification (KOG function ID-A), 85 to defence mechanisms (KOG function ID-V) amongst other KOG classes (Additional file 1: Table S1). A total of 3,594 transcripts were annotated using PRK database (Additional file 1: Table S1).

Transcriptome quantitation and enrichment of significant biological categories and KEGG pathways

To get an estimate of transcript abundance, in silico quantitation of transcripts was done by mapping the reads from individual libraries to the non-redundant set of 13,593 transcripts using TopHat, and transcript abundance were calculated using Cufflinks. The FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values for all the transcripts are given in Additional file 3: Table S2. The highly abundant transcripts were searched against KOG and PRK databases to identify their functions. We identified 202 transcripts showing ≥1000 FPKM, and 4,124 transcripts with ≥100 FPKM (Additional file 3: Table S2). The KOG analysis predicted functions for 76 proteins with ≥ 1,000 FPKM values, of which three most abundant protein classes were translation, ribosomal structure and biogenesis (KOG function ID-J), post translational modification, protein turnover, chaperones (KOG function ID-O) and intracellular trafficking, secretion, and vesicular transport (KOG function ID-U) (Table 4, Additional file 3: Table S2). In the 2,345 proteins with ≥ 100 FPKM values (Additional file 3: Table S2), other predominant protein functional classes that showed up in 2,345 proteins with ≥ 100 FPKM values were signal transduction (KOG function ID-T), energy production and conversion (KOG function ID-C), RNA processing and modification (KOG function ID-A), and transcription (KOG function ID-K). The PRK database analysis showed a similar result (Additional file 3: Table S2).

Table 4 KOG analysis of genes with FPKM Values ≥ 1000 and ≥100

Metabolic pathway analysis was done using KEGG Automatic Annotation Server against C. elegans, C. briggsae, B. malayi, Loa loa and Trichinella spiralis pathways. The analysis of KEGG pathways represented by the abundant transcripts revealed that, among others, at FPKM ≥ 1,000, the various signaling pathways like PI3K-Akt, Hippo, HIF-signaling pathway, Rap, MAPK, calcium, sphingolipid, cGMP-PKG, insulin signaling pathway were represented by at least one or more protein (Additional file 4: Table S3). However, at FPKM ≥ 100, in addition to the above pathways, several other signaling pathways like FoxO, cAMP, Ras, sphingolipid, epithelial cell, AMPK, TGF-ß were detected (Additional file 4: Table S3).

The kinome of H. indica IJs

The kinome analysis was done to identify the protein kinases important in signal transduction in all the above mentioned signaling pathways that regulate metabolism, cell cycle, growth and development, and responses to environmental stimuli. As against 438 kinases reported from C. elegans [52], we detected 272 in H. indica IJ transcriptome at stringent blastp parameters of at least 40 % sequence identity and 50 % query coverage (Table 5). These 438 (C. elegans) kinases were classified into 187 groups, and we found that 137 kinase groups were common between C. elegans and H. indica, whereas, 50 kinase groups were not found in H. indica. The details of kinase groups common between C. elegans, and H. indica are given in Table 5, and kinases that could not be discovered in H. indica but present in C. elegans are listed in Additional file 5: Table S4.

Table 5 Kinases belonging to different group/family/subfamily represented in IJ stage of H. indica

The secretome of H. indica IJs

A total of 2,374 secreted proteins were predicted (Additional file 6: Table S5a). The important proteins found in the analysis were related to neuropeptide signaling, for example, 2 each of GPCR-Family 2 like and GPCR rhodopsin-like including GPCR rhodopsin-like 7TM, and GPCR Family 3 C-terminal domains. Several hydrolases were identified, including 33 hydrolases belonging to small GTPases, glycoside hydrolases, transthyretin/hydroxyisourate hydrolase, alpha/beta hydrolase and epoxide hydrolase. The secretome showed the presence of a large contingent of peptidases that have a known role in degrading insect tissues. We could identify 38 peptidases belonging to different classes, such as metallopeptidases, trypsin-like cysteine/serine peptidases, cysteine peptidases, peptidase S1 (serine endopeptidases), S1A, S8, S10, S24, S26, S28, S53, S54, M10, M13, M14, M28, M12, M41. Some of these peptidases like carboxypeptidase possess regulatory domains. A search of the MEROPS database [53] for identification of putative peptidases (proteases, proteinases, and proteolytic enzymes) identified 64 known peptidases of the different parasitic and free-living nematodes (Additional file 7: Table S5b). Five transcription factors including STAT, p53, TFIID were also identified. Several genes involved in signaling, such as 13 members of protein kinases were present in the secreted contingent, including serine threonine, tyrosine, and thiamine phosphate kinase. Similarly, 12 members of phosphatases were found. Lastly, the transcripts showed the presence of several known stress response genes such as glutathione peroxidases, heat shock protein 70 and heat shock protein 90.

Repeat elements in H. indica transcriptome

The transcriptome data was used to analyze the repeat elements because no information is available for repeat elements in this species. Transcript sequences were examined for the presence of repeat elements using Repeat Masker v-4.0.5 program. Approximately 1.4 % of the total transcripts were found to be encoded by different repetitive elements, of which 1.21 % belonged to simple repeats, and 0.29 % were low complexity repeats (Additional file 8: Table S6a). A total of 31 retroelements were found in the transcripts, with four long interspersed repeat elements (LINEs), although no short interspersed repeat elements (SINEs) were found. Among retroelements, 27 long terminal repeats (LTR) were found which was higher than non-LTR elements. Also, 15 DNA transposons of different classes, 103 small RNA, and three satellites were found (Additional file 8: Table S6a).

Using MISA to identify short sequence repeats (SSRs) revealed 2,968 sequences showing the presence of 3,635 SSRs. Out of the 2,968 sequences, 465 sequences contained more than one SSRs and 209 SSRs were present in compound formation (Additional file 9: Table S6b). Mononucleotide repeats (46.6 %), and trinucleotide repeats (46.05 %) represented the largest fraction of SSRs, followed by di-nucleotide repeats (6.3 %). The number of tetra-(32), penta-(5) and hexa-(1) nucleotide repeats were below 0.1 % (Additional file 9: Table S6b).

RNAi pathway genes and other gene classes in H. indica IJs

C. elegans genome encodes 77 RNAi pathway effector genes, which is the most number of RNAi pathway effector genes discovered in any nematode [54]. We could identify 24 RNAi pathway effector genes in the present transcriptome (Table 7). Different RNAi effector genes identified were six genes encoding for small RNA biosynthetic proteins, four genes for dsRNA uptake, spreading and siRNA amplification, three for Argonautes, two each for RNA-induced silencing complex genes (RISC) and RNAi inhibitors, and seven for nuclear RNAi effectors (Table 6, Additional file 10: Table S7). The presence of nrde-3 in H. indica (percent identity, 30.27; query coverage, 98; E-value, 1.00E-21), which is responsible for nuclear translocation of RNAi triggers in C. elegans, is recorded for the first time in any parasitic nematode.

Table 6 RNAi effector genes discovered in the IJ stage of the H. indica

Additionally, the H. indica transcriptome was analysed for presence of members of functionally important gene classes like neuropeptides (FMRFamide-related peptides (flp), non-insulin, non-FMRFamide-related neuropeptide-like proteins (nlp), uncoordinate (unc), dauer formation (daf), fatty acid and retinol binding protein (far), nuclear hormone receptor (nhr), C-type lectin domain containing proteins (lec), lysozymes (lys) and lethal (let) gene classes at two stringency levels of 25 and 30 % sequence similarity and query coverage. The results are presented in Table 7. Interestingly, we also found an ortholog of C. elegans tol-1 in the transcriptome of H. indica IJs (32.9 % identity, 88 query coverage at 2e–180).

Table 7 Members of flp, nlp, unc, ins, daf, far, nhr, lec, let and lys gene class present in the transcriptome of H. indica IJs. Gene counts for each gene class for C. elegans were taken from wormbase


The transcriptome sequencing and assembly of H. indica IJs resulted in 13,593 unique, high-quality transcripts at N50 value of 1,371 bp. Further, 6,320 out of 13,593 (53 %) transcripts could be annotated by blastx against nr database. Most of the blastx hits showed similarity with A. suum and not H. bacteriophora which is a closely related species. This anomaly may be attributed to the absence of H. bacteriophora sequences from nr database. Standalone blast identified 2,745 hits with H. bacteriophora.

The free living-developmentally arrested infective stage is characteristic of many parasitic nematodes [5558]. The “dauer hypothesis” proposes that similar molecular mechanisms regulate the developmental arrest and activation of both C. elegans dauer larvae and analogous developmentally arrested 3rd stage larvae (L3i) of parasitic nematodes [56, 57, 59] despite their evolutionary divergence [60, 61]. In the free-living model nematode, C. elegans, a developmentally arrested dauer stage is formed during conditions of low food abundance, high temperature [62], high dauer pheromone levels [63, 64] and high population density [65, 66]. The daf (abnormal dauer formation) genes identified in C. elegans that are involved in formation and regulation of dauer stages are placed into four dauer pathways-a cyclic guanosine monophosphate (cGMP) signaling pathway, an insulin/IGF-1-like signaling (IIS) pathway regulated by insulin-like peptide (ILP) ligands, a dauer transforming growth factor-β (TGF-β) pathway regulated by the Ce-DAF-7 ligand, and a nuclear hormone receptor (NHR) regulated by a class of steroid ligands known as dafachronic acids (DAs) [35]. Epistatic analysis revealed that the cGMP signaling pathway operates upstream of the parallel IIS and dauer TGF-β pathways, which converge on the DA biosynthetic pathway, ultimately regulating the NHR Ce-DAF-12 [38, 41]. Analysis of dauer pathways in the L3i stage of S. stercoralis revealed that out of four pathways involved in dauer formation, two were conserved while two were not, suggesting their conserved and novel modes of developmental regulation [41, 67]. Our results show that at least two of the canonical dauer pathways-insulin signaling pathway and cGMP-PKG signaling pathway were represented in the top 60 active pathways by at least 46 and 32 proteins, respectively (Table 3). Further, TGF-β pathway was represented by 27 proteins, and the dafachronic acid pathway was represented by a single but important gene, daf-1 (Additional file 11: Table S8). DAF-1 encodes a TGF-beta type I receptor homolog, which, in association with the DAF-4, regulates dauer formation in response to environmental signals through the ASI chemosensory neuron [6870]. Our results show that similar to C. elegans, all the four dauer formation pathways are conserved and active in the IJ stage of H. indica.

EPN IJs are not known to feed, but they utilize the lipids and glycogen energy reserves stored in the body for their survival. We found genes involved in various pathways like fatty acid degradation, glycolysis, and glyoxalate in the IJ transcriptome. All these three pathways catabolize energy reserves such as fatty acids and glucose and generate ATPs that are utilized for the IJ survival. Glyoxalate pathway has been known to be important for dauer stages of C. elegans [71] and has also been reported in an EPN, Romanomermis [72].

We found several signaling pathways in the transcriptome of H. indica IJs essential for nematode survival under stressed conditions and various other activities (Table 3). Some of these signaling pathways, such as PI3K-Akt and mTOR signaling pathways are involved in regulation of cell cycle and in mediating oxidative stress responses and extending the lifespan in the nematodes [73, 74]. Presence of other signaling pathways such as the MAPK known to be involved in nematode response to various cellular and environmental stimuli including stresses and cell proliferation, regulation of fertilization in nematodes, especially sperm activation [75, 76] suggest that these signaling pathways might control the IJ nematodes from being reproductive in the arrested stage. cGMP-PKG signaling is involved in olfactory sensing and behavior regulation in the nematodes [77, 78] and flies [78, 79], and pharyngeal pumping rate, mouth form dimorphism, the duration of forward locomotion, and the amount of fat stored in the intestine in necromenic insect associated nematode, Pristionchus [80]. This indicated that the H. indica IJs also actively sense their environment and adapt their metabolism and behavior accordingly.

The analysis of the H. indica secretome identified several hydrolases, a large contingent of peptidases, kinases, phosphatases, and enzymes involved in stress responses. Some of these enzymes are important for the degradation of insect cuticle, tissue, and hemocoel, whereas peptidases are also known to be involved in regulatory functions. The presence of a large number of kinases and phosphatases indicates vibrant signaling in the IJ stage. All these findings suggest that although IJ is a developmentally arrested stage; it is still a hotbed of signaling and is actively sensing its environment.

H. indica is a rhabditid as C. elegans, which shows the presence of 77 RNAi pathway genes [54]. Primary sequence similarity based search was carried out to identify putative orthologs of C. elegans RNAi pathway genes in H. indica. We found 24 orthologs of C. elegans RNAi pathway effector genes in H. indica IJs. The completed genome sequence of another species of the same genus, H. bacteriophora revealed the presence of only 12 RNAi pathway genes [30] indicating either incompleteness of the genome or false negatives because of poor annotation of H. bacteriophora genome. Interestingly, the RNAi pathways can differ significantly even amongst very closely related nematode species, as is evident by the fact that the number of RNAi effector genes varied from 60 to 77 amongst different species of Caenorhabditis spp. [54]. Out of the four RNAi effector genes present in most known parasitic nematodes, drsh-1, rsd-3, ego-1, and smg-2 were present in H. indica IJs. However, ego-1 was absent in the two parasitic nematodes Trichinella spiralis, and A. caninum [54], suggesting that it is not universally present in parasitic nematodes as thought earlier. We found nrde-3 in H. indica IJs at a low stringency cutoff, which is responsible for nuclear translocation of RNAi triggers in C. elegans, and is involved in processes that lead to the heritability of gene silencing events. The absence of nrde-3 in parasitic nematodes has led to speculations that silencing events cannot be passed between generations of parasitic nematodes [54]. However, sequences with loose homology to the C. elegans nrde-3 could be discovered in H. bacteriophora genome as well, suggesting that the absence of nrde-3 in H. bacteriophora might be a false negative caused by a failure to predict the H. bacteriophora nrde-3 gene. Its presence in Heterorhabditis nematodes indicated that the silencing events could probably be passed between generations, and opens up a whole new array for use of Heterorhabditid nematodes as a model for epigenetic regulation of RNAi pathways.

The sequence divergence between C. elegans and H. indica prevented discovery of C. elegans orthologs of important gene class members at a high stringency. By lowering the stringency of the blastn to 30 % identity and query coverage, we could identify several additional members of the various gene classes in H. indica, but these orthologs would need further validation. The H. indica transcriptome showed the presence of at least 22 flp, 25 nlp and 18 ins neuropeptide genes, 69 unc, 21 daf and 0 (4 at 25 %) far genes, 98 nhr, nine lec, 15 let but no lys gene class members (Table 7, Additional file 11: Table S8). In the daf gene class, daf-1, daf-2 and daf-4 were identified, all of which are important in dauer formation in C. elegans. daf-1 encodes a TGF-beta type I receptor homolog, which together with the TGF-β-like type II receptor DAF-4, is required for the regulation of dauer formation by environmental signals [8184]. Similarly, daf-7 encodes a member of the TGF-β superfamily; which is involved in signaling pathway that interprets environmental conditions to regulate energy balance pathways that affect dauer larval formation, fat metabolism, egg laying, feeding behavior and sperm motility [8588]. Identification of several insulin-like peptide (ins) genes proved the role of insulin signaling in IJ formation and maintenance in H. indica. Neuropeptides like flp and nlp are involved in environmental sensing by the nematode. In the flp gene class, flp-1, flp-3, flp-5, flp-12, flp-17 and flp-18 were the prominent members. In the recent years, flp genes are emerging as important targets for nematode management, and it has been shown that disruption of flp gene expression impaired nematode parasite’s ability to locate its host [8995]. Other neuropeptides found in H. indica, like nlp-4, has no known homologs in other nematode species [90, 96, 97], whereas nlp-18 in C. elegans encodes four predicted neuropeptide-like proteins; and is expressed in a variety of neurons, spermatheca, the rectal gland, and the intestine [98]. Another important protein class, nematode lectins, are protein molecules that bind to carbohydrate moieties. They are involved in cell-cell recognition and are important in nematode recognition of bacteria and innate immune responses against pathogens. Nine members of the lec gene class were identified in H. indica including lec-6. lec-6 encodes a 'proto' type galectin (beta-galactosyl-binding lectin) containing a single carbohydrate recognition domain and is suggested to be important for cell adhesion and aggregation, proliferation, or programmed cell death in C. elegans [99101]. Likewise, in H. indica, members of the lectin protein family might possibly be involved in recognition of the symbiont bacteria. Similarly, tol-1 found expressing in H. indica IJs has been reported to be involved in behavioral responses to the pathogenic microbes by promoting the development of sensory neurons that monitor microbial metabolism and are required for a pathogen-avoidance behavior in C. elegans [102]. Hence, it is possible that tol-1 could be involved in the maintenance of a specific symbiotic relationship between Heterorhabditis nematodes with Photorhabdus bacterium, but this hypothesis would need further testing.


Here we presented a transcriptomic insight into the infective juvenile stage of the EPN, H. indica. After using cd-hit-est and filtering out <300 bp transcripts, we have identified 13,592 unique transcripts in H. indica infective juveniles. 18.6 % of the proteins were similar to an animal parasite A. suum. We found that similar to C. elegans, all the four dauer formation pathways-cGMP-PKG signaling pathway, insulin signaling pathway, dafachronic acid pathway, and TGF-β were conserved in H. indica and were active in the IJ stage of the nematode. Several important signaling pathways were found active in the IJs indicating that despite being a developmentally arrested stage, IJs are a hotbed of signaling and are actively interacting with their environment. Similarly, glycolysis and fatty acid degradation pathways were highly active in IJs indicating a breakdown of food reserves required for survival. Twenty-four orthologs of C. elegans RNAi pathway effector genes were found in H. indica IJ transcriptome, including nrde-3 that has been identified in any of the parasitic worms for the first time. Using a low stringency approach, we have identified several additional members of important gene classes in H. indica. Our results and analysis lay down the groundwork for further functional genomic investigations on these gene classes in Heterorhabditis nematodes.


Nematode collection and multiplication

The Heterorhabditis indica nematodes were isolated from the soil collected from Ghaziabad district, UP, India by using greater wax moth Galleria melonella as a bait. The nematodes were maintained in the laboratory on Galleria using standard procedures.

RNA extraction, cDNA synthesis, library preparation and sequencing

Total RNA was extracted from the frozen IJs using Nucleospin RNA isolation kit (Macherey-Nagel GmbH & Co. KG, Düren, Germany) according to the manufacturer’s instructions. Extracted RNA was assessed for quality and quantity using an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA with an RNA integrity number (RIN) of 8.0 was used for mRNA purification. mRNA was purified from 1 mg of intact total RNA using oligodT beads (Illumina® TruSeq® RNA Sample Preparation Kit v2). The purified mRNA was fragmented at elevated temperature (90 °C) in the presence of divalent cations and reverse transcribed with Superscript II Reverse Transcriptase (Invitrogen Life Technologies) by priming with random hexamers. Second strand cDNA was synthesized in the presence of DNA polymerase I and RNaseH. The cDNA was cleaned using AgencourtAmpure XP SPRI beads (Beckman-Coulter). Illumina adapters were ligated to the cDNA molecules after end repair and the addition of an ‘A’ base followed by SPRI clean-up. The resultant cDNA library was amplified using PCR for the enrichment of adapter-ligated fragments, quantified using a Nanodrop spectrophotometer (Thermo Scientific) and validated for quality with a Bioanalyzer (Agilent Technologies). It was then sequenced on the Illumina Hiseq 2000 platform at SciGenom Next-Gen sequencing facility, Cochin, India. Both the raw and assembled sequence data generated has been deposited in the European Nucleotide Archive (ENA) database ( for public access (raw data accession no.: PRJEB10852, assembled contigs accession numbers: HADG01000001-HADG01013593). The assembled nucleotide and protein sequences are also available for blast and download at The assembled data is included with the manuscript as Additional file 12.

De novo transcriptome assembly and analysis

Paired orphan sequence reads obtained from IJs were used for assembly of the transcriptome [103]. The low quality reads (Phred score <30) were removed and sequencing statistics was generated with the help of NGSQC Toolkit version v2.3.3 [104]. High quality filtered paired-end raw reads (Phred Score ≥ 30) obtained from IJs were assembled using Velvet (v.1.2.08) and Oases (v.0.2.08) pipeline [105]. Velvet was run at different k-mer lengths (51–93 with a step size of 4)—with minimum contig length of 200. The optimal assembly was attained at k-mer 83. The oases module was used for merging transcript assemblies from k-mer 71 to 83 (71, 75, 79, 83) with minimum transcript length of 100 using the script “” (k-mer range 71–83, insert length 250 bp, coverage depth cut off 5). Cd-hit-est was used to remove redundant transcripts at 90 % similarity. Transcripts <300 nucleotide length were removed resulting in a unique set of non-redundant transcripts.

Annotation and quantification of the transcriptome

ORFPredictor web server ( [51] was used to predict proteins from the 13,593 transcripts (>300 bp length) using the default cut-off value of 1e–5, and 13,592 proteins were predicted which were used for annotation. Annotation for all the unique transcripts (>300 bp) was done using blastp [106], homology search against Uniprot [107], the National Center for Biotechnology Information (NCBI)-NR Protein database [106] and NEMABASE4 ( In addition, blastx was performed to identify homologues at ≥30 % query coverage and ≥50 % sequence identity and e-value 1e–5 in other databases including RefSeq (PRK), SWISSPROT [108], European Molecular Biology Laboratory(EMBL)[109], DNA Databank of Japan (DDBJ) [110], Protein Information Resource (PIR) [111] and Protein Data Bank (RCSB)[112]. Nematode orthologs were identified from NCBI COG [113] database and other completely sequenced genomes by the reciprocal blast method[106]. To study gene orthologs across free-living and parasitic nematode species, we used the predicted protein sets from 11 genomes available in the public domain (Wormbase, NCBI, and Sanger) viz., C. elegans, C. remanei, C. briggsae, M. hapla, M. incognita, H. bacteriophora, Pristionchus pacificus, Brugia malayi, S. ratti, Trichinella spiralis and A. suum. Blastp hits with e-value scores 1e–5 and query coverage above 50 % were considered as annotated homologous proteins and python script was employed for filtering reciprocal best hits. KEGG orthologs were identified using the KEGG Automated Annotation Server (KAAS) using nematode database. iPATH server was used for mapping it to KEGG reference pathway [114]. The gene ontology and domains were identified using InterProScan 5 with default parameters [115]. The resulting hits were processed to retrieve associated GO terms describing biological processes, molecular functions, and cellular components. Homologs of the C. elegans RNAi pathway genes were also identified in the H. indica transcriptome by performing tblastx with e-value ≤ 1e–5.

The high-quality reads were mapped to the non-redundant assembled transcripts using TopHat v-2.0.9. [116119]. Assembly of transcript models from RNA-Seq alignments and estimation of transcripts and their abundance was performed using Cufflinks v-2.1.1 [119]. Both these software packages were used with default parameters for our analysis [119].

Potentially secreted peptides were identified using the SignalP 4.1 software [120] from the 174,700 peptides of minimum protein length ≥30, and those with transmembrane motifs were removed using TMHMM [121]. MEROPS database was searched to identify proteases, proteinases, and proteolytic enzymes [122]. Repeat elements were identified in transcripts using Repeat Masker v.4.0.5 S and Repbase v.20140131 using default parameters against species “Nematoda”. Short Sequence Repeats (SSRs) were identified using MISA (MIcroSAtellite; with at least 10 repeats for mono-, 6 repeats for di-, and 5 repeats for tri-, tetra-, penta- and hexanucleotide for simple SSRs.

Availability of supporting data

The data supporting the results of this research paper are included within this article and its additional files. The raw and assembled sequence data has been deposited in the Eueopean Nucleotide Archive (ENA) database for public access (raw data accession no.: PRJEB10852, assembled contigs accession numbers: HADG01000001-HADG01013593). The assembled nucleotide and protein sequences are available for blast and download at The assembled sequences are also supplied as an Additional file 12 with this manuscript.


  1. 1.

    Bongers T, Ferris H. Nematode community structure as a bioindicator in environmental monitoring. Trends Ecol Evol. 1999;14(6):224–8.

    PubMed  Article  Google Scholar 

  2. 2.

    Ferris H, Bongers T. Nematode indicators of organic enrichment. J Nematol. 2006;38(1):3–12.

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Ciche TA, Darby C, Ehlers R-U, Forst S, Goodrich-Blair H. Dangerous liaisons: the symbiosis of entomopathogenic nematodes and bacteria. Biol Control. 2006;38(1):22.

    Article  Google Scholar 

  4. 4.

    Kaya HK, Aguillera MM, Alumai A, Choo HY, de la Torre M, Fodor A, et al. Status of entomopathogenic nematodes and their symbiotic bacteria from selected countries or regions of the world. Biol Control. 2006;38(1):134.

    Article  Google Scholar 

  5. 5.

    Dolinski C, Choo HY, Duncan LW. Grower acceptance of entomopathogenic nematodes: case studies on three continents. J Nematol. 2012;44(2):226–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Lacey LA, Georgis R. Entomopathogenic nematodes for control of insect pests above and below ground with comments on commercial production. J Nematol. 2012;44(2):218–25.

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Somvanshi VS, Kaufmann-Daszczuk B, Kim KS, Mallon S, Ciche TA. Photorhabdus phase variants express a novel fimbrial locus, mad, essential for symbiosis. Mol Microbiol. 2010;77(4):1021–38.

    CAS  PubMed  Google Scholar 

  8. 8.

    Somvanshi VS, Sloup RE, Crawford JM, Martin AR, Heidt AJ, Kim KS, et al. A single promoter inversion switches photorhabdus between pathogenic and mutualistic states. Science. 2012;337(6090):88–93.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Vivas EI, Goodrich-Blair H. Xenorhabdus nematophilus as a model for host-bacterium interactions: rpoS is necessary for mutualism with nematodes. J Bacteriol. 2001;183(16):4687–93.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Ruby EG. Symbiotic conversations are revealed under genetic interrogation. Nat Rev Microbiol. 2008;6(10):752–62.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Hallem EA, Rengarajan M, Ciche TA, Sternberg PW. Nematodes, bacteria, and flies: a tripartite model for nematode parasitism. Curr Biol. 2007;17(10):898–904.

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Stock SP. Insect-parasitic nematodes: from lab curiosities to model organisms. J Invertebr Pathol. 2005;89(1):57.

    PubMed  Article  Google Scholar 

  13. 13.

    Campos-Herrera R, Barbercheck M, Hoy CW, Stock SP. Entomopathogenic nematodes as a model system for advancing the frontiers of ecology. J Nemaol. 2012;44(2):162–76.

    Google Scholar 

  14. 14.

    Ciche T. The biology and genome of Heterorhabditis bacteriophora. WormBook: the online review of C. elegans biology, 2007:1–9.

  15. 15.

    Ahmed R, Chang Z, Younis AE, Langnick C, Li N, Chen W, et al. Conserved miRNAs are candidate post-transcriptional regulators of developmental arrest in free-living and parasitic nematodes. Genome Biol Evol. 2013;5(7):1246–60.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  16. 16.

    Dillman AR, Guillermin ML, Lee JH, Kim B, Sternberg PW, Hallem EA. Olfaction shapes host-parasite interactions in parasitic nematodes. Proc Natl Acad Sci U S A. 2012;109(35):E2324–33.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Hallem EA, Dillman AR, Hong AV, Zhang Y, Yano JM, DeMarco SF, et al. A sensory code for host seeking in parasitic nematodes. Cur Biol. 2011;21(5):377–83.

    CAS  Article  Google Scholar 

  18. 18.

    Griffin CT. Perspectives on the behavior of entomopathogenic nematodes from dispersal to reproduction: traits contributing to nematode fitness and biocontrol efficacy. J Nematol. 2012;44(2):177–84.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Shapiro-Ilan DI, Brown I, Lewis EE. Freezing and desiccation tolerance in entomopathogenic nematodes: diversity and correlation of traits. J Nematol. 2014;46(1):27–34.

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Nugent MJ, O'Leary SA, Burnell AM. Optimised procedures for the cryopreservation of different species of Heterorhabditis. Funda Appl Nematol. 1996;19(1):1–6.

    Google Scholar 

  21. 21.

    Chen D, Li Patrick W-L, Goldstein BA, Cai W, Thomas EL, Chen F, et al. Germline signaling mediates the synergistically prolonged longevity produced by double mutations in daf-2 and rsks-1 in C. elegans. Cell Rep. 2013;5(6):1600–10.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Antebi A. Genetics of aging in Caenorhabditis elegans. PLoS Genet. 2007;3(9):1565–71.

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Alvares SM, Mayberry GA, Joyner EY, Lakowski B, Ahmed S. H3K4 demethylase activities repress proliferative and postmitotic aging. Aging Cell. 2014;13(2):245–53.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Grewal P, Converse V, Georgis R. Influence of production and bioassay methods on infectivity of two ambush foragers (nematoda: steinernematidae). J Invertebr Pathol. 1999;73(1):40–4.

    PubMed  Article  Google Scholar 

  25. 25.

    Lewis E, Pérez E. Ageing and developmental behaviour. In: Gaugler R, Bilgrami AL, editors. Nematode behaviour. UK: CABI; 2004. p. 151–76.

    Chapter  Google Scholar 

  26. 26.

    Grewal P, Wang X, Taylor R. Dauer juvenile longevity and stress tolerance in natural populations of entomopathogenic nematodes: is there a relationship? Int J Parasitol. 2002;32(6):717–25.

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Gal TZ, Glazer I, Koltai H. Stressed worms: responding to the post-genomics era. Mol Biochem Parasitol. 2005;143(1):1–5.

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Dillman AR, Mortazavi A, Sternberg PW. Incorporating genomics into the toolkit of nematology. J Nematol. 2012;44(2):191–205.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Mitreva M, Blaxter ML, Bird DM, McCarter JP. Comparative genomics of nematodes. Trends Genet. 2005;21(10):573–81.

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Bai X, Adams BJ, Ciche TA, Clifton S, Gaugler R, Kim KS, et al. A lover and a fighter: the genome sequence of an entomopathogenic nematode Heterorhabditis bacteriophora. PLoS One. 2013;8(7), e69618.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Bai X, Grewal PS, Hogenhout SA, Adams BJ, Ciche TA, Gaugler R, et al. Expressed sequence tag analysis of gene representation in insect parasitic nematode Heterorhabditis bacteriophora. J Parasitol. 2007;93(6):1343–9.

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Sandhu SK, Jagdale GB, Hogenhout SA, Grewal PS. Comparative analysis of the expressed genome of the infective juvenile entomopathogenic nematode, Heterorhabditis bacteriophora. Mol Biochem Parasitol. 2006;145(2):239–44.

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Bai X, Adams BJ, Ciche TA, Clifton S, Gaugler R, Hogenhout SA, et al. Transcriptomic analysis of the entomopathogenic nematode Heterorhabditis bacteriophora TTO1. BMC Genomics. 2009;10:205.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  34. 34.

    Riddle DL, Swanson MM, Albert PS. Interacting genes in nematode dauer larva formation. Nature. 1981;290(5808):668–71.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Hu PJ: Dauer. WormBook : the online review of C elegans biology. 2007;1–19.

  36. 36.

    Inoue T, Ailion M, Poon S, Kim HK, Thomas JH, Sternberg PW. Genetic analysis of dauer formation in Caenorhabditis briggsae. Genetics. 2007;177(2):809–18.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Schroeder NE, Flatt KM. In vivo imaging of dauer-specific neuronal remodeling in C. elegans. J Vis Exp. 2014;91, e51834.

    PubMed  Google Scholar 

  38. 38.

    Fielenbach N, Antebi A. C. elegans dauer formation and the molecular basis of plasticity. Genes Dev. 2008;22(16):2149–65.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Sinha A, Langnick C, Sommer RJ, Dieterich C. Genome-wide analysis of trans-splicing in the nematode pristionchus pacificus unravels conserved gene functions for germline and dauer development in divergent operons. RNA. 2014;20(9):1386–97.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Sinha A, Sommer RJ, Dieterich C. Divergent gene expression in the conserved dauer stage of the nematodes pristionchus pacificus and Caenorhabditis elegans. BMC Genomics. 2012;13:254.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Stoltzfus JD, Minot S, Berriman M, Nolan TJ, Lok JB. RNAseq analysis of the parasitic nematode Strongyloides stercoralis reveals divergent regulation of canonical dauer pathways. PLoS Negl Trop Dis. 2012;6(10), e1854.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Moore J, Tetley L, Devaney E. Identification of abundant mRNAs from the third stage larvae of the parasitic nematode, ostertagia ostertagi. Biochem J. 2000;347(Pt 3):763–70.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Elling AA, Mitreva M, Recknor J, Gai X, Martin J, Maier TR, et al. Divergent evolution of arrested development in the dauer stage of Caenorhabditis elegans and the infective stage of heterodera glycines. Genome Biol. 2007;8(10):R211.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. 44.

    Szakasits D, Heinen P, Wieczorek K, Hofmann J, Wagner F, Kreil DP, et al. The transcriptome of syncytia induced by the cyst nematode heterodera schachtii in Arabidopsis roots. Plant J. 2009;57(5):771–84.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Dubreuil G, Magliano M, Deleury E, Abad P, Rosso M. Transcriptome analysis of root‐knot nematode functions induced in the early stages of parasitism. New Phytol. 2007;176(2):426–36.

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Eves-van den Akker S, Lilley CJ, Danchin EGJ, Rancurel C, Cock PJA, Urwin PE, et al. The transcriptome of nacobbus aberrans reveals insights into the evolution of sedentary endoparasitism in plant-parasitic nematodes. Genome Biol Evol. 2014;6(9):2181–94.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Wang F, Li D, Wang Z, Dong A, Liu L, Wang B, et al. Transcriptomic analysis of the rice white Tip nematode, aphelenchoides besseyi (nematoda: aphelenchoididae). PLoS One. 2014;9(3), e91591.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

    Kumar M, Gantasala NP, Roychowdhury T, Thakur PK, Banakar P, Shukla RN, et al. De novo transcriptome sequencing and analysis of the cereal cyst nematode, heterodera avenae. PloS One. 2014;9(5), e96311.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. 49.

    Moshayov A, Koltai H, Glazer I. Molecular characterisation of the recovery process in the entomopathogenic nematode Heterorhabditis bacteriophora. Int J Parasitol. 2013;43(10):843–52.

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Poinar GO, Karunakar GK, David H. Heterorhabditis indicus n. sp. (rhabditida: nematoda) from India: separation of Heterorhabditis spp. By infective juveniles. Funda Appl Nematol. 1992;15:467–72.

    Google Scholar 

  51. 51.

    Min XJ, Butler G, Storms R, Tsang A. OrfPredictor: predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res. 2005;33(Web Server issue):W677–680.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Manning G. Genomic overview of protein kinases. WormBook: the online review of C elegans biology. 2005;1–19.

  53. 53.

    Rawlings ND, Barrett AJ, Bateman A. MEROPS: the database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res. 2012;40(Database issue):D343–50.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Dalzell JJ, McVeigh P, Warnock ND, Mitreva M, Bird DM, Abad P, et al. RNAi effector diversity in nematodes. PLoS Negl Trop Dis. 2011;5(6), e1176.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Lok JB. Strongyloides stercoralis: a model for translational research on parasitic nematode biology. WormBook : the online review of C elegans biology. 2007;1–18.

  56. 56.

    Viney ME. How did parasitic worms evolve? Bioessays. 2009;31(5):496–9.

    CAS  PubMed  Article  Google Scholar 

  57. 57.

    Dieterich C, Sommer RJ. How to become a parasite-lessons from the genomes of nematodes. Trends Genet. 2009;25(5):203–9.

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Bird DM, Opperman CH. Caenorhabditis elegans: a genetic guide to parasitic nematode biology. J Nematol. 1998;30(3):299–308.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Burglin TR, Lobos E, Blaxter ML. Caenorhabditis elegans as a model for parasitic nematodes. Int J Parasitol. 1998;28(3):395–411.

    CAS  PubMed  Article  Google Scholar 

  60. 60.

    Blaxter ML, De Ley P, Garey JR, Liu LX, Scheldeman P, Vierstraete A, et al. A molecular evolutionary framework for the phylum nematoda. Nature. 1998;392(6671):71–5.

    CAS  PubMed  Article  Google Scholar 

  61. 61.

    Holterman M, van der Wurff A, van den Elsen S, van Megen H, Bongers T, Holovachov O, et al. Phylum-wide analysis of SSU rDNA reveals deep phylogenetic relationships among nematodes and accelerated evolution toward crown clades. Mol Biol Evol. 2006;23(9):1792–800.

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Ailion M, Thomas JH. Dauer formation induced by high temperatures in Caenorhabditis elegans. Genetics. 2000;156(3):1047–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Butcher RA, Fujita M, Schroeder FC, Clardy J. Small-molecule pheromones that control dauer development in Caenorhabditis elegans. Nat Chem Biol. 2007;3(7):420–2.

    CAS  PubMed  Article  Google Scholar 

  64. 64.

    Butcher RA, Ragains JR, Li W, Ruvkun G, Clardy J, Mak HY. Biosynthesis of the Caenorhabditis elegans dauer pheromone. Proc Natl Acad Sci U S A. 2009;106(6):1875–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Braendle C, Milloz J, Felix MA. Mechanisms and evolution of environmental responses in Caenorhabditis elegans. Curr Top Dev Biol. 2008;80:171–207.

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Golden JW, Riddle DL. The Caenorhabditis elegans dauer larva: developmental effects of pheromone, food, and temperature. Dev Biol. 1984;102(2):368–78.

    CAS  PubMed  Article  Google Scholar 

  67. 67.

    Marcilla A, Garg G, Bernal D, Ranganathan S, Forment J, Ortiz J, et al. The transcriptome analysis of Strongyloides stercoralis L3i larvae reveals targets for intervention in a neglected disease. PLoS Negl Trop Dis. 2012;6(2), e1513.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Patterson GI, Padgett RW. TGFβ-related pathways: roles in Caenorhabditis elegans development. Trends Genet. 2000;16(1):27–33.

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Riddle DL, Albert PS. Genetic and environmental regulation of dauer larva development. Cold Spring Harbor Monograph Arch. 1997;33:739–68.

    CAS  Google Scholar 

  70. 70.

    Harvey SC, Shorto A, Viney ME. Quantitative genetic analysis of life-history traits of Caenorhabditis elegans in stressful environments. BMC Evol Biol. 2008; 8:15.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  71. 71.

    O’Riordan VB, Burnell AM. Intermediary metabolism in the dauer larva of the nematode Caenorhabditis elegans-II. The glyoxylate cycle and fatty-acid oxidation. Comp Biochem Physiol Part B: Comp Biochem. 1990;95(1):125–30.

    Article  Google Scholar 

  72. 72.

    Gordon R. Glyoxylate pathway in the free-living stages of the entomophilic nematode romanomermis culicivorax. J Nematol. 1987;19(3):277–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Lehtinen MK, Yuan Z, Boag PR, Yang Y, Villén J, Becker EB, et al. A conserved MST-FOXO signaling pathway mediates oxidative-stress responses and extends life span. Cell. 2006;125(5):987–1001.

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Johnson SC, Rabinovitch PS, Kaeberlein M. mTOR is a key modulator of ageing and age-related disease. Nature. 2013;493(7432):338–45.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    Singaravelu G, Singson A. Calcium signaling surrounding fertilization in the nematode Caenorhabditis elegans. Cell Calcium. 2013;53(1):2–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    Liu Z, Wang B, He R, Zhao Y, Miao L. Calcium signaling and the MAPK cascade are required for sperm activation in Caenorhabditis elegans. Biochim Biophys Acta. 2014;1843(2):299–308.

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Noelle D, Coburn CM, Eastham J, Kistler A, Gallegos G, Bargmann CI. The cyclic GMP-dependent protein kinase EGL-4 regulates olfactory adaptation in C. elegans. Neuron. 2002;36(6):1079–89.

    Article  Google Scholar 

  78. 78.

    Reaume CJ, Sokolowski MB. cGMP-dependent protein kinase as a modifier of behaviour. In: Schmidt HH, Hofmann F, Stasch JP, editors. cGMP: generators, effectors and therapeutic implications. Handbook of experimental pharmacology. Vol. 191. Berlin, Heidelberg: Springer; 2009. p. 423–43.

    Chapter  Google Scholar 

  79. 79.

    Sokolowski MB. Genes for normal behavioral variation: recent clues from flies and worms. Neuron. 1998;21(3):463–6.

    CAS  PubMed  Article  Google Scholar 

  80. 80.

    Kroetz SM, Srinivasan J, Yaghoobian J, Sternberg PW, Hong RL. The cGMP signaling pathway affects feeding behavior in the necromenic nematode pristionchus pacificus. PLoS One. 2012;7(4), e34464.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Gumienny T, Savage-Dunn C. TGF-β signaling in C. elegans. WormBook: the online review of C elegans biology. 2012;1–34.

  82. 82.

    Shaw WM, Luo S, Landis J, Ashraf J, Murphy CT. The C. elegans TGF-β dauer pathway regulates longevity via insulin signaling. Curr Biol. 2007;17(19):1635–45.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Georgi LL, Albert PS, Riddle DL. daf-1, a C. elegans gene controlling dauer larva development, encodes a novel receptor protein kinase. Cell. 1990;61(4):635–45.

    CAS  PubMed  Article  Google Scholar 

  84. 84.

    Lee SS, Kennedy S, Tolonen AC, Ruvkun G. DAF-16 target genes that control C. elegans life-span and metabolism. Science. 2003;300(5619):644–7.

    CAS  PubMed  Article  Google Scholar 

  85. 85.

    Nolan KM, Sarafi-Reinach TR, Horne JG, Saffer AM, Sengupta P. The DAF-7 TGF-β signaling pathway regulates chemosensory receptor gene expression in C. elegans. Genes Dev. 2002;16(23):3061–73.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Murakami M, Koga M, Ohshima Y. DAF-7/TGF-β expression required for the normal larval development in C. elegans is controlled by a presumed guanylyl cyclase DAF-11. Mech Dev. 2001;109(1):27–35.

    CAS  PubMed  Article  Google Scholar 

  87. 87.

    Crook M, Grant WN. Dominant negative mutations of Caenorhabditis elegans daf-7 confer a novel developmental phenotype. Dev Dyn: An Off Pub Am Assoc Anatomists. 2013;242(6):654–64.

    CAS  Article  Google Scholar 

  88. 88.

    McKnight K, Hoang HD, Prasain JK, Brown N, Vibbert J, Hollister KA, et al. Neurosensory perception of environmental cues modulates sperm motility critical for fertilization. Science. 2014;344(6185):754–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Kimber MJ, McKinney S, McMaster S, Day TA, Fleming CC, Maule AG. flp gene disruption in a parasitic nematode reveals motor dysfunction and unusual neuronal sensitivity to RNA interference. FASEB J. 2007;21(4):1233–43.

    CAS  PubMed  Article  Google Scholar 

  90. 90.

    Li C, Nelson LS, Kim K, Nathoo A, Hart AC. Neuropeptide gene families in the nematode Caenorhabditis elegans. Ann N Y Acad Sci. 1999;897(1):239–52.

    CAS  PubMed  Article  Google Scholar 

  91. 91.

    McVeigh P, Geary TG, Marks NJ, Maule AG. The FLP-side of nematodes. Trends Parasitol. 2006;22(8):385–96.

    CAS  PubMed  Article  Google Scholar 

  92. 92.

    Li C. The ever-expanding Neuropeptide gene families in the nematode Caenorhabditis elegans. Parasitol. 2005;131(Suppl):S109–27.

    CAS  Google Scholar 

  93. 93.

    Nelson LS, Rosoff ML, Li C. Disruption of a Neuropeptide gene, flp-1, causes multiple behavioral defects in Caenorhabditis elegans. Science. 1998;281(5383):1686–90.

    CAS  PubMed  Article  Google Scholar 

  94. 94.

    Papolu PK, Gantasala NP, Kamaraju D, Banakar P, Sreevathsa R, Rao U. Utility of host delivered RNAi of two FMRF amide like peptides, flp-14 and flp-18, for the management of root knot nematode, Meloidogyne incognita. PLoS ONE. 2013;8(11):e80603.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  95. 95.

    Dong L, Li X, Huang L, Gao Y, Zhong L, Zheng Y, et al. Lauric acid in crown daisy root exudate potently regulates root-knot nematode chemotaxis and disrupts Mi-flp-18 expression to block infection. J Exp Bot. 2013;65(1):131–41.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  96. 96.

    Fraser AG, Kamath RS, Zipperlen P, Martinez-Campos M, Sohrmann M, Ahringer J. Functional Genomic analysis of C. elegans chromosome I by systematic RNA interference. Nature. 2000;408(6810):325–30.

    CAS  PubMed  Article  Google Scholar 

  97. 97.

    Nathoo AN, Moeller RA, Westlund BA, Hart AC. Identification of Neuropeptide-like protein gene families in Caenorhabditis elegans and other species. Proc Natl Acad Sci U S A. 2001;98(24):14000–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Kamath RS, Fraser AG, Dong Y, Poulin G, Durbin R, Gotta M, et al. Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature. 2003;421(6920):231–7.

    CAS  PubMed  Article  Google Scholar 

  99. 99.

    Ahmed H, Bianchet MA, Amzel LM, Hirabayashi J, Kasai K-i, Giga-Hama Y, et al. Novel carbohydrate specificity of the 16-kDa galectin from Caenorhabditis elegans: binding to blood group precursor oligosaccharides (type 1, type 2, Tα, and Tβ) and gangliosides. Glycobiology. 2002;12(8):451–61.

    CAS  PubMed  Article  Google Scholar 

  100. 100.

    Hirabayashi J, Arata Y, Hayama K, Kasai K-i. Galectins from the nematode Caenorhabditis elegans and the glycome project. Trends Glycosci Glycotechnol. 2001;13(73):533–49.

    CAS  Article  Google Scholar 

  101. 101.

    Gonczy P, Echeverri C, Oegema K, Coulson A, Jones SJM, Copley RR, et al. Functional Genomic analysis of cell division in C. elegans using RNAi of genes on chromosome III. Nature. 2000;408(6810):331–6.

    CAS  PubMed  Article  Google Scholar 

  102. 102.

    Brandt Julia P, Ringstad N. Toll-like receptor signaling promotes development and function of sensory neurons required for a C. elegans pathogen-avoidance behavior. Curr Biol. 2015;25(17):2228–37.

    CAS  PubMed  Article  Google Scholar 

  103. 103.

    Miller HC, Biggs PJ, Voelckel C, Nelson NJ. De novo sequence assembly and characterisation of a partial transcriptome for an evolutionarily distinct reptile, the tuatara (sphenodon punctatus). BMC Genomics. 2012;13(1):439.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  104. 104.

    Patel RK, Jain M. NGS QC toolkit: a toolkit for quality control of next generation sequencing data. PLoS One. 2012;7(2), e30619.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. 105.

    Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18(5):821–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  106. 106.

    Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. 107.

    Magrane M, Consortium U. UniProt knowledgebase: a hub of integrated protein data. Database. 2011;2011:bar009.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  108. 108.

    Bairoch A, Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 2000;28(1):45–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. 109.

    Kulikova T, Aldebert P, Althorpe N, Baker W, Bates K, Browne P, et al. The EMBL nucleotide sequence database. Nucleic Acids Res. 2004;32 suppl 1:D27–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  110. 110.

    Tateno Y, Imanishi T, Miyazaki S, Fukami-Kobayashi K, Saitou N, Sugawara H, et al. DNA data bank of Japan (DDBJ) for genome scale research in life science. Nucleic Acids Res. 2002;30(1):27–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. 111.

    Barker WC, Garavelli JS, Huang H, McGarvey PB, Orcutt BC, Srinivasarao GY, et al. The protein information resource (PIR). Nucleic Acids Res. 2000;28(1):41–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  112. 112.

    Rose PW, Beran B, Bi C, Bluhm WF, Dimitropoulos D, Goodsell DS, et al. The RCSB protein data bank: redesigned web site and web services. Nucleic Acids Res. 2011;39 suppl 1:D392–401.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. 113.

    Tatusov RL, Koonin EV, Lipman DJ. A genomic perspective on protein families. Science. 1997;278(5338):631–7.

    CAS  PubMed  Article  Google Scholar 

  114. 114.

    Yamada T, Letunic I, Okuda S, Kanehisa M, Bork P. iPath2.0: interactive pathway explorer. Nucleic Acids Res. 2011;39 suppl 2:W412–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  115. 115.

    Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30(9):1236–40.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  116. 116.

    Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  117. 117.

    Kim D, Salzberg SL. TopHat-fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 2011;12(8):R72.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  118. 118.

    Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14(4):R36.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  119. 119.

    Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and cufflinks. Nat Protoc. 2012;7(3):562–78.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  120. 120.

    Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011;8(10):785–6.

    CAS  PubMed  Article  Google Scholar 

  121. 121.

    Krogh A, Larsson B, Von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305(3):567–80.

    CAS  PubMed  Article  Google Scholar 

  122. 122.

    Rawlings ND, Waller M, Barrett AJ, Bateman A. MEROPS: the database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res. 2014;42(Database issue):D503–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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We acknowledge funding from ICAR-IARI and Department of Biotechnology, Government of India grant no. BT/PR/5163. We thank the Director, IARI and the Joint Director (Research) for the support through In-house grants. We thank Mr. J. P. Singh, Secretary, Foundation for Agricultural Resources Management and Environmental Remediation, Ghaziabad, UP for providing the H. indica strain.

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Corresponding author

Correspondence to Uma Rao.

Additional information

Competing interests

The authors have no competing interests to declare.

Authors’ contributions

UR, PB, and VSS conceptualized the experiment. PP performed the sequence assembly. SG and MS analyzed the data with help from PT and MK. VSS and UR wrote the manuscript with contributions from PB, SG, MK, MS and PT. All the authors have read and approve of the final version of the manuscript.

Additional files

Additional file 1: Table S1a.

Blast, KOG and PRK analysis of the H. indica IJ transcripts. (XLSX 2073 kb)

Additional file 2: Table S1b.

Standalone blast of H. indica IJ transcripts against H. bacteriophora genome. (XLSX 336 kb)

Additional file 3: Table S2.

Transcript abundance of H. Indica transcripts along with KOG analysis of transcripts showing FPKM values ≥ 100 and ≥ 1000. (XLSX 486 kb)

Additional file 4: Table S3.

KEGG Pathways represented by transcripts with FPKM value of ≥100 and ≥1000. (XLSX 20 kb)

Additional file 5: Table S4.

C. elegans kinase Group/family/subfamily not found in H. indica. (XLSX 10 kb)

Additional file 6: Table S5a.

H. indica secretome prediction by SignalP. (XLSX 90 kb)

Additional file 7: Table S5b.

MEROPS analysis of H. indica transcripts to identify secreted peptidases, proteases, and peptides. (XLSX 11 kb)

Additional file 8: Table S6a.

Repeat elements identified in Heterorhabditis indica transcripts. (DOCX 16 kb)

Additional file 9: Table S6b.

SSR type and microsatellites present in H. indica. (XLSX 187 kb)

Additional file 10: Table S7.

RNAi effector pathway genes in H. indica. (XLSX 12 kb)

Additional file 11: Table S8.

Orthologs of members of C. elegans gene classes present in H. indica at ≥25% identity and query coverage. (XLSX 37 kb)

Additional file 12:

H. indica assembled sequences. (TXT 14804 kb)

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Somvanshi, V.S., Gahoi, S., Banakar, P. et al. A transcriptomic insight into the infective juvenile stage of the insect parasitic nematode, Heterorhabditis indica . BMC Genomics 17, 166 (2016).

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  • Parasitic Nematode
  • RNAi Pathway
  • Query Coverage
  • Infective Juvenile
  • Infective Juvenile Stage